Recently, with the rapid spread use of mobile devices, some problems have begun to emerge. The most important of these are that the mobile devices batteries’ life may be short and that these devices may be in some cases. The complex tasks that must be addressed to solve such problems on mobile devices can be transferred to the cloud environment when appropriate conditions are met. The decision to offload to the cloud environment at this stage is very important. In this thesis, a context-aware decision-making system has been developed for offloading to cloud environments. Unlike similar tasks, the processes determined for transfer to the cloud are not run randomly, but rather according to the mobile user's application usage habits. The developed system was implemented in a real environment for one month. According to the results, it was determined that processes transferred to the cloud were completed in less time and consumed less energy.
CONTEXT-AWARE DECISION MAKING SYSTEM FOR MOBILE CLOUD OFFLOADINGIJCNCJournal
In this study, a mobile cloud offloading system has been developed to decide that a process run on the cloud or on the mobile platform. A context-aware decision algorithm has been developed. The low performance and problem of battery consumption of mobile devices have been fundamental challenges on the mobile computing. To overcome this kind of challenges, recent advances towards mobile cloud computing propose a selective mobile-to-cloud offloading service by moving a mobile application from a slow mobile device to a fast server in the cloud during run time. Determine whether a process running on cloud or not is an important issue. Power consumption and time limits are vitally important for decision. In this study we used PowerTutor application which is a dynamic power measurement modelling tool. Another important factor is the process completion time. Calculate the power consumption is very difficult
A survey to harness an efficient energy in cloud computingijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
Empirical studies have revealed that a significant amount of energy is lost unnecessarily in the
network architectures, protocols, routers and various other network devices. Thus there is a need for techniques
to obtain green networking in the computer architecture which can lead to energy saving. Green networking is
an emerging phenomenon in the computer industry because of its economic and environmental benefits. Saving
energy leads to cost-cutting and lower emission of greenhouse gases which are apparently one of the major
threats to the environment. ’Greening’ as the name suggests is the process of constructing network architecture
in such a way so as to avoid unnecessary loss of power and energy due its various components and can be
implemented using various techniques out of which four are mentioned in this review paper, namely Adaptive
link rate (ALR), Dynamic Voltage and Frequency scaling(DVFS), Interface proxying and energy aware
applications and software.
A survey on energy efficient with task consolidation in the virtualized cloud...eSAT Journals
Abstract Cloud computing is a new model of computing that is widely used in today’s industry, organizations and society in information technology service delivery as a utility. It enables organizations to reduce operational expenditure and capital expenditure. However, cloud computing with underutilized resources still consumes an unacceptable amount of energy than fully utilized resource. Many techniques for optimizing energy consumption in virtualized cloud have been proposed. This paper surveys different energy efficient models with task consolidation in the virtualized cloud computing environment. Keywords: Cloud computing, Virtualization, Task consolidation, Energy consumption, Virtual machine
A survey on energy efficient with task consolidation in the virtualized cloud...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
CONTEXT-AWARE DECISION MAKING SYSTEM FOR MOBILE CLOUD OFFLOADINGIJCNCJournal
In this study, a mobile cloud offloading system has been developed to decide that a process run on the cloud or on the mobile platform. A context-aware decision algorithm has been developed. The low performance and problem of battery consumption of mobile devices have been fundamental challenges on the mobile computing. To overcome this kind of challenges, recent advances towards mobile cloud computing propose a selective mobile-to-cloud offloading service by moving a mobile application from a slow mobile device to a fast server in the cloud during run time. Determine whether a process running on cloud or not is an important issue. Power consumption and time limits are vitally important for decision. In this study we used PowerTutor application which is a dynamic power measurement modelling tool. Another important factor is the process completion time. Calculate the power consumption is very difficult
A survey to harness an efficient energy in cloud computingijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
Empirical studies have revealed that a significant amount of energy is lost unnecessarily in the
network architectures, protocols, routers and various other network devices. Thus there is a need for techniques
to obtain green networking in the computer architecture which can lead to energy saving. Green networking is
an emerging phenomenon in the computer industry because of its economic and environmental benefits. Saving
energy leads to cost-cutting and lower emission of greenhouse gases which are apparently one of the major
threats to the environment. ’Greening’ as the name suggests is the process of constructing network architecture
in such a way so as to avoid unnecessary loss of power and energy due its various components and can be
implemented using various techniques out of which four are mentioned in this review paper, namely Adaptive
link rate (ALR), Dynamic Voltage and Frequency scaling(DVFS), Interface proxying and energy aware
applications and software.
A survey on energy efficient with task consolidation in the virtualized cloud...eSAT Journals
Abstract Cloud computing is a new model of computing that is widely used in today’s industry, organizations and society in information technology service delivery as a utility. It enables organizations to reduce operational expenditure and capital expenditure. However, cloud computing with underutilized resources still consumes an unacceptable amount of energy than fully utilized resource. Many techniques for optimizing energy consumption in virtualized cloud have been proposed. This paper surveys different energy efficient models with task consolidation in the virtualized cloud computing environment. Keywords: Cloud computing, Virtualization, Task consolidation, Energy consumption, Virtual machine
A survey on energy efficient with task consolidation in the virtualized cloud...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
In this video from SC17 in Denver, Dan Reed moderates a panel discussion on HPC Software for Energy Efficiency.
"We have already achieved major gains in energy-efficiency for both the datacenter and HPC equipment. For example, the PUE of the Swiss Supercomputer (CSCS) datacenter prior to 2012 was 1.8, but the current PUE is about 1.25; a factor of ~1.5 improvement. HPC system improvements have also been very strong, as evidenced by FLOPS/Watt performance on the Green500 List. While we have seen gains from data center and HPC system efficiency, there are also energy-efficiency gains to be had from software- application performance improvements, for example. This panel will explore what HPC software capabilities were most helpful over the past years in improving HPC system energy efficiency? It will then look forward; asking in what layers of the software stack should a priority be put on introducing energy-awareness; e.g., runtime, scheduling, applications? What is needed moving forward? Who is responsible for that forward momentum?"
Watch the video: https://wp.me/p3RLHQ-hHQ
Learn more: https://sc17.supercomputing.org/presentation/?id=pan103&sess=sess245
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system
IMPROVING REAL TIME TASK AND HARNESSING ENERGY USING CSBTS IN VIRTUALIZED CLOUDijcax
Cloud computing provides the facility for the business customers to scale up and down their resource usage based on needs. This is because of the virtualization technology. The scheduling objectives are to improve the system’s schedule ability for the real-time tasks and to save energy. To achieve the objectives, we employed the virtualization technique and rolling-horizon optimization with vertical scheduling operation.
The project considers Cluster Scoring Based Task Scheduling (CSBTS) algorithm which aims to decrease task’s completion time and the policies for VM’s creation, migration and cancellation are to dynamically adjust the scale of cloud in a while meets the real-time requirements and to save energy.
IMPROVING REAL TIME TASK AND HARNESSING ENERGY USING CSBTS IN VIRTUALIZED CLOUDijcax
Cloud computing provides the facility for the business customers to scale up and down their resource usage
based on needs. This is because of the virtualization technology. The scheduling objectives are to improve
the system’s schedule ability for the real-time tasks and to save energy. To achieve the objectives, we
employed the virtualization technique and rolling-horizon optimization with vertical scheduling operation.
The project considers Cluster Scoring Based Task Scheduling (CSBTS) algorithm which aims to decrease
task’s completion time and the policies for VM’s creation, migration and cancellation are to dynamically
adjust the scale of cloud in a while meets the real-time requirements and to save energy.
Rapid and Energy Efficient Data Transmission Technique using Event Aggregatio...ijsrd.com
In this Paper we analyze WSNs and show that it's possible to achieve better performances in terms of energy consumption and latency. Event aggregation in WSNs is a process of combining several low-level events into a high-level event to eliminate redundant information to be transmitted and thus save energy. Existing works on event aggregation consider either latency constraint or aggregation function, but not both. Moreover, existing works only consider optimal aggregation for single high level event, but many applications are composed of multiple high-level events. This paper studies the problem of aggregating multiple high-level events in WSNs with different latency constraints and aggregation functions. We propose relation matrix to define aggregation function, which describes the similarity among limited number of primitive events rather than the growing number of high-level events. Based on it, we propose an event aggregation algorithm jointly considering the two issues for single high-level event. This algorithm supports partial aggregation which is more general than fully aggregation. Through selection the optimal base events, the work is extended to multiple high level events and consider the practical reliable constraint. The simulation results show that our algorithm outperforms existing approaches and saves significant amount of energy (up to 35% in our system).
Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifeti...IJECEIAES
Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency.
A Novel Weighted Clustering Based Approach for Improving the Wireless Sensor ...IJERA Editor
Great lifetime and reliability is the key aim of Wireless Sensor Network (WSN) design. As for prolonging
lifetime of this type of network, energy is the most important resource; all recent researches are focused on more
and more energy efficient techniques. Proposed work is Weighted Clustering Approach based on Weighted
Cluster Head Selection, which is highly energy efficient and reliable in mobile network scenario. Weight
calculation using different attributes of the nodes like SNR (Signal to Noise Ratio), Remaining Energy, Node
Degree, Mobility, and Buffer Length gives efficient Cluster Head (CH) on regular interval of time. CH rotation
helps in optimum utilization of energy available with all nodes; results in prolonged network lifetime.
Implementation is done using the NS2 network simulator and performance evaluation is carried out in terms of
PDR (Packet Delivery Ratio), End to End Delay, Throughput, and Energy Consumption. Demonstration of the
obtained results shows that proposed work is adaptable for improving the performance. In order to justify the
solution, the performance of proposed technique is compared with the performance of traditional approach. The
performance of proposed technique is found optimum as compared to the traditional techniques.
An energy optimization with improved QOS approach for adaptive cloud resources IJECEIAES
In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (퐴퐶푅푅) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model 퐴퐶푅푅 in terms of average run time, power consumption and average power required than any other state-of-art techniques.
Novel approach for hybrid MAC scheme for balanced energy and transmission in ...IJECEIAES
Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
A SURVEY ON REDUCING ENERGY SPRAWL IN CLOUD COMPUTINGaciijournal
Cloud computing is the cluster of autonomic computing, grid computing and utility computing. Cloud
providers are there to rescue their customers from the problem of dynamism. The providers focus on
resource sharing and in improving the performance. Energy consumption is the major factor to degrade the
performance. Reducing energy sprawl will bloom the performance. This paper delineates the different
techniques involved in scheduling the workload of the servers in order to minimize the energy sprawl.
Intelligent flood disaster warning on the fly: developing IoT-based managemen...journalBEEI
The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network
is used.
Intelligent task processing using mobile edge computing: processing time opti...IAESIJAI
The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delay-sensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities, battery resources, and storage availability, it enhances the durability and performance of mobile devices by offloading local intensive computation tasks to edge servers. However, the optimal solution is not always guaranteed by offloading computation, there-fore, the offloading decision is a crucial step depending on many parameters that should be taken in consideration. In this paper, we use a simulator to compare a two tier edge orchestrator architecture with the results obtained by implementing a system model that aims to minimize a task’s processing time constrained by time delay and the limited device’s computational resource and usage based on a modified version.
The increased usage of mobile devices caused them to face a large amount of resource, memory and processing speed scarcity. Of all other constraints, energy is the major problem for this to carry out a task. The concept of offloading gets into play for mobile devices i.e., the task or the computation which needs to be performed involving more service in the android systems will be shifted to resourceful server (for ex cloud) and getting back the results done from the cloud. The decision of whether to offload a computation or not will depend on the task accounting to the energy spent by the device while working with the application versus the amount of energy spent by the same device for uploading the task to the cloud and getting the result back from the cloud. As this concept depicts energy is the major constraint for whether to offload a task or not, there is a model called CUCKOO framework which acts as an interface between the cloud and the android environment to support for the task offloading to the cloud. Thus this framework bridges the gap between the smartphones as well as the cloud environment so that computation intensive task can be performed with less amount of energy consumed. In this work two applications are used to detect the amount of energy consumed in the cloud as well as the smartphones namely eyedentify and Photoshoot.
Energy-efficient data-aggregation for optimizing quality of service using mo...IJECEIAES
Quality of service (QoS) is essential for carrying out data transmission using resource-constrained sensor nodes in wireless sensor network (WSN). The introduction of mobile agent-based data aggregation is reported to offer energy efficiency; however, it has limitations, especially using a single mobile agent, where QoS optimization is not feasible. A review of existing studies showcases some dedicated attempts to use a mobile agent-based approach and address QoS enhancements. However, they were never combined studied. Therefore, this paper introduces a unique concept of retaining maximum QoS performance during data aggregation using a single mobile agent. The model introduces a unique communication framework, transmission provisioning using exceptional routine management, and simplified energy modeling. The proposed model has aimed for a lower delay and faster data aggregation speed with lower consumption of transmittance energy. The implementation and assessment of the model are carried out considering the challenging environment of WSN with multiple scales of data priority. The proposed model also contributes to evolving out with simplified communication vectors in a highly decentralized method. MATLAB's simulation outcome shows that the proposed system offers better delay performance, optimal energy management, and faster response time than existing schemes.
Energy Optimized Link Selection Algorithm for Mobile Cloud ComputingEswar Publications
Mobile cloud computing is the revolutionary distributed computing research area which consists of three different domains: cloud computing, wireless networks and mobile computing targeting to improve the task computational capabilities of the mobile devices in order to minimize the energy consumption. Heavy computations can be offloaded to the cloud to decrease energy consumption for the mobile device. In some mobile cloud applications, it has been more energy inefficient to use the cloud compared to the conventional computing conducted in the local device. Despite mobile cloud computing being a reliable idea, still faces several
problems for mobile phones such as storage, short battery life and so on. One of the most important concerns for mobile devices is low energy consumption. Different network links has different bandwidths to uplink and downlink task as well as data transmission from mobile to cloud or vice-versa. In this paper, a novel optimal link selection algorithm is proposed to minimize the mobile energy. In the first phase, all available networks are
scanned and then signal strength is calculated. All the calculated signals along with network locations are given
input to the optimal link selection algorithm. After the execution of link selection algorithm, an optimal network link is selected.
ENERGY EFFICIENT COMPUTING FOR SMART PHONES IN CLOUD ASSISTED ENVIRONMENTIJCNCJournal
In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.
Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim IJECEIAES
Cloud computing has been widely accepted by the researchers for the web applications. During the past years, distributed computing replaced the centralized computing and finally turned towards the cloud computing. One can see lots of applications of cloud computing like online sale and purchase, social networking web pages, country wide virtual classes, digital libraries, sharing of pathological research labs, supercomputing and many more. Creating and allocating VMs to applications use virtualization concept. Resource allocates policies and load balancing polices play an important role in managing and allocating resources as per application request in a cloud computing environment. Cloud analyst is a GUI tool that simulates the cloud-computing environment. In the present work, the cloud servers are arranged through step network and a UML model for a minimization of energy consumption by processor, dynamic random access memory, hard disk, electrical components and mother board is developed. A well Unified Modeling Language is used for design of a class diagram. Response time and internet characteristics have been demonstrated and computed results are depicted in the form of tables and graphs using the cloud analyst simulation tool.
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...ijccsa
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
KEYWORDS
Energy Consumption, Virtual Machine Placement, Harmony Search Algorithm, Server Consolidati
In this video from SC17 in Denver, Dan Reed moderates a panel discussion on HPC Software for Energy Efficiency.
"We have already achieved major gains in energy-efficiency for both the datacenter and HPC equipment. For example, the PUE of the Swiss Supercomputer (CSCS) datacenter prior to 2012 was 1.8, but the current PUE is about 1.25; a factor of ~1.5 improvement. HPC system improvements have also been very strong, as evidenced by FLOPS/Watt performance on the Green500 List. While we have seen gains from data center and HPC system efficiency, there are also energy-efficiency gains to be had from software- application performance improvements, for example. This panel will explore what HPC software capabilities were most helpful over the past years in improving HPC system energy efficiency? It will then look forward; asking in what layers of the software stack should a priority be put on introducing energy-awareness; e.g., runtime, scheduling, applications? What is needed moving forward? Who is responsible for that forward momentum?"
Watch the video: https://wp.me/p3RLHQ-hHQ
Learn more: https://sc17.supercomputing.org/presentation/?id=pan103&sess=sess245
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system
IMPROVING REAL TIME TASK AND HARNESSING ENERGY USING CSBTS IN VIRTUALIZED CLOUDijcax
Cloud computing provides the facility for the business customers to scale up and down their resource usage based on needs. This is because of the virtualization technology. The scheduling objectives are to improve the system’s schedule ability for the real-time tasks and to save energy. To achieve the objectives, we employed the virtualization technique and rolling-horizon optimization with vertical scheduling operation.
The project considers Cluster Scoring Based Task Scheduling (CSBTS) algorithm which aims to decrease task’s completion time and the policies for VM’s creation, migration and cancellation are to dynamically adjust the scale of cloud in a while meets the real-time requirements and to save energy.
IMPROVING REAL TIME TASK AND HARNESSING ENERGY USING CSBTS IN VIRTUALIZED CLOUDijcax
Cloud computing provides the facility for the business customers to scale up and down their resource usage
based on needs. This is because of the virtualization technology. The scheduling objectives are to improve
the system’s schedule ability for the real-time tasks and to save energy. To achieve the objectives, we
employed the virtualization technique and rolling-horizon optimization with vertical scheduling operation.
The project considers Cluster Scoring Based Task Scheduling (CSBTS) algorithm which aims to decrease
task’s completion time and the policies for VM’s creation, migration and cancellation are to dynamically
adjust the scale of cloud in a while meets the real-time requirements and to save energy.
Rapid and Energy Efficient Data Transmission Technique using Event Aggregatio...ijsrd.com
In this Paper we analyze WSNs and show that it's possible to achieve better performances in terms of energy consumption and latency. Event aggregation in WSNs is a process of combining several low-level events into a high-level event to eliminate redundant information to be transmitted and thus save energy. Existing works on event aggregation consider either latency constraint or aggregation function, but not both. Moreover, existing works only consider optimal aggregation for single high level event, but many applications are composed of multiple high-level events. This paper studies the problem of aggregating multiple high-level events in WSNs with different latency constraints and aggregation functions. We propose relation matrix to define aggregation function, which describes the similarity among limited number of primitive events rather than the growing number of high-level events. Based on it, we propose an event aggregation algorithm jointly considering the two issues for single high-level event. This algorithm supports partial aggregation which is more general than fully aggregation. Through selection the optimal base events, the work is extended to multiple high level events and consider the practical reliable constraint. The simulation results show that our algorithm outperforms existing approaches and saves significant amount of energy (up to 35% in our system).
Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifeti...IJECEIAES
Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency.
A Novel Weighted Clustering Based Approach for Improving the Wireless Sensor ...IJERA Editor
Great lifetime and reliability is the key aim of Wireless Sensor Network (WSN) design. As for prolonging
lifetime of this type of network, energy is the most important resource; all recent researches are focused on more
and more energy efficient techniques. Proposed work is Weighted Clustering Approach based on Weighted
Cluster Head Selection, which is highly energy efficient and reliable in mobile network scenario. Weight
calculation using different attributes of the nodes like SNR (Signal to Noise Ratio), Remaining Energy, Node
Degree, Mobility, and Buffer Length gives efficient Cluster Head (CH) on regular interval of time. CH rotation
helps in optimum utilization of energy available with all nodes; results in prolonged network lifetime.
Implementation is done using the NS2 network simulator and performance evaluation is carried out in terms of
PDR (Packet Delivery Ratio), End to End Delay, Throughput, and Energy Consumption. Demonstration of the
obtained results shows that proposed work is adaptable for improving the performance. In order to justify the
solution, the performance of proposed technique is compared with the performance of traditional approach. The
performance of proposed technique is found optimum as compared to the traditional techniques.
An energy optimization with improved QOS approach for adaptive cloud resources IJECEIAES
In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (퐴퐶푅푅) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model 퐴퐶푅푅 in terms of average run time, power consumption and average power required than any other state-of-art techniques.
Novel approach for hybrid MAC scheme for balanced energy and transmission in ...IJECEIAES
Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
A SURVEY ON REDUCING ENERGY SPRAWL IN CLOUD COMPUTINGaciijournal
Cloud computing is the cluster of autonomic computing, grid computing and utility computing. Cloud
providers are there to rescue their customers from the problem of dynamism. The providers focus on
resource sharing and in improving the performance. Energy consumption is the major factor to degrade the
performance. Reducing energy sprawl will bloom the performance. This paper delineates the different
techniques involved in scheduling the workload of the servers in order to minimize the energy sprawl.
Intelligent flood disaster warning on the fly: developing IoT-based managemen...journalBEEI
The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network
is used.
Intelligent task processing using mobile edge computing: processing time opti...IAESIJAI
The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delay-sensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities, battery resources, and storage availability, it enhances the durability and performance of mobile devices by offloading local intensive computation tasks to edge servers. However, the optimal solution is not always guaranteed by offloading computation, there-fore, the offloading decision is a crucial step depending on many parameters that should be taken in consideration. In this paper, we use a simulator to compare a two tier edge orchestrator architecture with the results obtained by implementing a system model that aims to minimize a task’s processing time constrained by time delay and the limited device’s computational resource and usage based on a modified version.
The increased usage of mobile devices caused them to face a large amount of resource, memory and processing speed scarcity. Of all other constraints, energy is the major problem for this to carry out a task. The concept of offloading gets into play for mobile devices i.e., the task or the computation which needs to be performed involving more service in the android systems will be shifted to resourceful server (for ex cloud) and getting back the results done from the cloud. The decision of whether to offload a computation or not will depend on the task accounting to the energy spent by the device while working with the application versus the amount of energy spent by the same device for uploading the task to the cloud and getting the result back from the cloud. As this concept depicts energy is the major constraint for whether to offload a task or not, there is a model called CUCKOO framework which acts as an interface between the cloud and the android environment to support for the task offloading to the cloud. Thus this framework bridges the gap between the smartphones as well as the cloud environment so that computation intensive task can be performed with less amount of energy consumed. In this work two applications are used to detect the amount of energy consumed in the cloud as well as the smartphones namely eyedentify and Photoshoot.
Energy-efficient data-aggregation for optimizing quality of service using mo...IJECEIAES
Quality of service (QoS) is essential for carrying out data transmission using resource-constrained sensor nodes in wireless sensor network (WSN). The introduction of mobile agent-based data aggregation is reported to offer energy efficiency; however, it has limitations, especially using a single mobile agent, where QoS optimization is not feasible. A review of existing studies showcases some dedicated attempts to use a mobile agent-based approach and address QoS enhancements. However, they were never combined studied. Therefore, this paper introduces a unique concept of retaining maximum QoS performance during data aggregation using a single mobile agent. The model introduces a unique communication framework, transmission provisioning using exceptional routine management, and simplified energy modeling. The proposed model has aimed for a lower delay and faster data aggregation speed with lower consumption of transmittance energy. The implementation and assessment of the model are carried out considering the challenging environment of WSN with multiple scales of data priority. The proposed model also contributes to evolving out with simplified communication vectors in a highly decentralized method. MATLAB's simulation outcome shows that the proposed system offers better delay performance, optimal energy management, and faster response time than existing schemes.
Energy Optimized Link Selection Algorithm for Mobile Cloud ComputingEswar Publications
Mobile cloud computing is the revolutionary distributed computing research area which consists of three different domains: cloud computing, wireless networks and mobile computing targeting to improve the task computational capabilities of the mobile devices in order to minimize the energy consumption. Heavy computations can be offloaded to the cloud to decrease energy consumption for the mobile device. In some mobile cloud applications, it has been more energy inefficient to use the cloud compared to the conventional computing conducted in the local device. Despite mobile cloud computing being a reliable idea, still faces several
problems for mobile phones such as storage, short battery life and so on. One of the most important concerns for mobile devices is low energy consumption. Different network links has different bandwidths to uplink and downlink task as well as data transmission from mobile to cloud or vice-versa. In this paper, a novel optimal link selection algorithm is proposed to minimize the mobile energy. In the first phase, all available networks are
scanned and then signal strength is calculated. All the calculated signals along with network locations are given
input to the optimal link selection algorithm. After the execution of link selection algorithm, an optimal network link is selected.
ENERGY EFFICIENT COMPUTING FOR SMART PHONES IN CLOUD ASSISTED ENVIRONMENTIJCNCJournal
In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.
Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim IJECEIAES
Cloud computing has been widely accepted by the researchers for the web applications. During the past years, distributed computing replaced the centralized computing and finally turned towards the cloud computing. One can see lots of applications of cloud computing like online sale and purchase, social networking web pages, country wide virtual classes, digital libraries, sharing of pathological research labs, supercomputing and many more. Creating and allocating VMs to applications use virtualization concept. Resource allocates policies and load balancing polices play an important role in managing and allocating resources as per application request in a cloud computing environment. Cloud analyst is a GUI tool that simulates the cloud-computing environment. In the present work, the cloud servers are arranged through step network and a UML model for a minimization of energy consumption by processor, dynamic random access memory, hard disk, electrical components and mother board is developed. A well Unified Modeling Language is used for design of a class diagram. Response time and internet characteristics have been demonstrated and computed results are depicted in the form of tables and graphs using the cloud analyst simulation tool.
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...ijccsa
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
KEYWORDS
Energy Consumption, Virtual Machine Placement, Harmony Search Algorithm, Server Consolidati
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...neirew J
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
An Efficient Cloud Scheduling Algorithm for the Conservation of Energy throug...IJECEIAES
Method of broadcasting is the well known operation that is used for providing support to different computing protocols in cloud computing. Attaining energy efficiency is one of the prominent challenges, that is quite significant in the scheduling process that is used in cloud computing as, there are fixed limits that have to be met by the system. In this research paper, we are particularly focusing on the cloud server maintenance and scheduling process and to do so, we are using the interactive broadcasting energy efficient computing technique along with the cloud computing server. Additionally, the remote host machines used for cloud services are dissipating more power and with that they are consuming more and more energy. The effect of the power consumption is one of the main factors for determining the cost of the computing resources. With the idea of using the avoidance technology for assigning the data center resources that dynamically depend on the application demands and supports the cloud computing with the optimization of the servers in use.
Cost-Efficient Task Scheduling with Ant Colony Algorithm for Executing Large ...Editor IJCATR
The aim of cloud computing is to share a large number of resources and pieces of equipment to compute and store knowledge and information for great scientific sources. Therefore, the scheduling algorithm is regarded as one of the most important challenges and problems in the cloud. To solve the task scheduling problem in this study, the ant colony optimization (ACO) algorithm was adapted from social theories with a fair and accurate resource allocation approach based on machine performance and capacity. This study was intended to decrease the runtime and executive costs. It was also meant to optimize the use of machines and reduce their idle time. Finally, the proposed method was compared with Berger and greedy algorithms. The simulation results indicate that the proposed algorithm reduced the makespan and executive cost when tasks were added. It also increased fairness and load balancing. Moreover, it made the optimal use of machines possible and increased user satisfaction. According to evaluations, the proposed algorithm improved the makespan by 80%.
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
A Survey of Cyber foraging systems: Open Issues, Research ChallengesEswar Publications
This paper presents a survey on current applications which practice the pervasive mechanism of cyber foraging. The applications include the LOCUSTS framework, Slingshot, Pupetter. This applications advocated the operating principle of task sharing among resource deficient mobile devices. These applications face some design issues for providing efficient performance like task distribution and task migration apart from the security aspect. The general operating mechanism of the cyber foraging technique are also discussed upon and the design options to leverage the throughput of the inherent mechanism is also represented in a suitable way.
IMPROVING REAL TIME TASK AND HARNESSING ENERGY USING CSBTS IN VIRTUALIZED CLOUDijcax
Cloud computing provides the facility for the business customers to scale up and down their resource usage
based on needs. This is because of the virtualization technology. The scheduling objectives are to improve
the system’s schedule ability for the real-time tasks and to save energy. To achieve the objectives, we
employed the virtualization technique and rolling-horizon optimization with vertical scheduling operation.
The project considers Cluster Scoring Based Task Scheduling (CSBTS) algorithm which aims to decrease
task’s completion time and the policies for VM’s creation, migration and cancellation are to dynamically
adjust the scale of cloud in a while meets the real-time requirements and to save energy
IMPROVING REAL TIME TASK AND HARNESSING ENERGY USING CSBTS IN VIRTUALIZED CLOUDijcax
Cloud computing provides the facility for the business customers to scale up and down their resource usage
based on needs. This is because of the virtualization technology. The scheduling objectives are to improve
the system’s schedule ability for the real-time tasks and to save energy. To achieve the objectives, we
employed the virtualization technique and rolling-horizon optimization with vertical scheduling operation.
The project considers Cluster Scoring Based Task Scheduling (CSBTS) algorithm which aims to decrease
task’s completion time and the policies for VM’s creation, migration and cancellation are to dynamically
adjust the scale of cloud in a while meets the real-time requirements and to save energy
IMPROVING REAL TIME TASK AND HARNESSING ENERGY USING CSBTS IN VIRTUALIZED CLOUDijcax
Cloud computing provides the facility for the business customers to scale up and down their resource usage
based on needs. This is because of the virtualization technology. The scheduling objectives are to improve
the system’s schedule ability for the real-time tasks and to save energy. To achieve the objectives, we
employed the virtualization technique and rolling-horizon optimization with vertical scheduling operation.
The project considers Cluster Scoring Based Task Scheduling (CSBTS) algorithm which aims to decrease
task’s completion time and the policies for VM’s creation, migration and cancellation are to dynamically
adjust the scale of cloud in a while meets the real-time requirements and to save energy.
Similar to A NEW CONTEXT-SENSITIVE DECISION MAKING SYSTEM FOR MOBILE CLOUD OFFLOADING (20)
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Cloud Computing, being one of the most recent innovative developments of the IT world, has been
instrumental not just to the success of SMEs but, through their productivity and innovative contribution to
the economy, has even made a remarkable contribution to the economic growth of the United States. To
this end, the study focuses on how cloud computing technology has impacted economic growth through
SMEs in the United States. Relevant literature connected to the variables of interest in this study was
reviewed, and secondary data was generated and utilized in the analysis section of this paper. The findings
of this paper revealed that there have been meaningful contributions that the usage of virtualization has
made in the commercial dealings of small firms in the United States, and this has also been reflected in the
economic growth of the country. This paper further revealed that as important as cloud-based software is,
some SMEs are still skeptical about how it can help improve their business and increase their bottom line
and hence have failed to adopt it. Apart from the SMEs, some notable large firms in different industries,
including information and educational services, have adopted cloud computing technology and hence
contributed to the economic growth of the United States. Lastly, findings from our inferential statistics
revealed that no discernible change has occurred in innovation between small and big businesses in the
adoption of cloud computing. Both categories of businesses adopt cloud computing in the same way, and
their contribution to the American economy has no significant difference in the usage of virtualization.
Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in
recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized
application of MIMO technology within WSNs. This approach operates effectively, especially in
challenging and resource-constrained environments. By facilitating collaboration among sensor nodes,
Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments.
Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring,
industrial automation, and healthcare applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication. IJCSIT publishes original research papers and review papers, as well as auxiliary material such as: research papers, case studies, technical reports etc.
With growing, Car parking increases with the number of car users. With the increased use of smartphones
and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking
Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This
gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are
utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the
server and are recovered by the mobile application which offers many options attractively and with no cost
to users and lets the user check reservation details. With IoT technology, the smart parking system can be
connected wirelessly to easily track available locations.
Welcome to AIRCC's International Journal of Computer Science and Information Technology (IJCSIT), your gateway to the latest advancements in the dynamic fields of Computer Science and Information Systems.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This research aims to further understanding in the field of continuous authentication using behavioural
biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing
Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed
machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and
Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust
model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch
dynamics can effectively distinguish users. However, further studies are needed to make it viable option
for authentication systems. You can access our dataset at the following
link:https://github.com/AuthenTech2023/authentech-repo
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
provide an overview of its impressive performance across a wide range of natural language processing
tasks, such as language translation, question-answering, and text completion. Throughout this research
project, a summarizing tool was also created to help us retrieve content from any types of document,
specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the
summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
The security of Electric Vehicle (EV) charging has gained momentum after the increase in the EV adoption
in the past few years. Mobile applications have been integrated into EV charging systems that mainly use a
cloud-based platform to host their services and data. Like many complex systems, cloud systems are
susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this
paper, we explore the security of key components in the EV charging infrastructure, including the mobile
application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack
between an EV app and its cloud services. Our results showed that it is possible to launch attacks against
the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and
operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions
and future research directions.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This paper describes the outcome of an attempt to implement the same transitive closure (TC) algorithm
for Apache MapReduce running on different Apache Hadoop distributions. Apache MapReduce is a
software framework used with Apache Hadoop, which has become the de facto standard platform for
processing and storing large amounts of data in a distributed computing environment. The research
presented here focuses on the variations observed among the results of an efficient iterative transitive
closure algorithm when run against different distributed environments. The results from these comparisons
were validated against the benchmark results from OYSTER, an open source Entity Resolution system. The
experiment results highlighted the inconsistencies that can occur when using the same codebase with
different implementations of Map Reduce.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
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introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
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using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
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A NEW CONTEXT-SENSITIVE DECISION MAKING SYSTEM FOR MOBILE CLOUD OFFLOADING
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
DOI: 10.5121/ijcsit.2018.10305 71
A NEW CONTEXT-SENSITIVE DECISION
MAKING SYSTEM FOR MOBILE CLOUD
OFFLOADING
Mustafa Tanrıverdi1
and M. Ali Akcayol2
1
Institute of Information, Gazi University, Ankara, Turkey
2
Department of Computer Engineering, Gazi University, Ankara, Turkey
ABSTRACT
Recently, with the rapid spread use of mobile devices, some problems have begun to emerge. The most
important of these are that the mobile devices batteries’ life may be short and that these devices may be in
some cases. The complex tasks that must be addressed to solve such problems on mobile devices can be
transferred to the cloud environment when appropriate conditions are met. The decision to offload to the
cloud environment at this stage is very important. In this thesis, a context-aware decision-making system
has been developed for offloading to cloud environments. Unlike similar tasks, the processes determined
for transfer to the cloud are not run randomly, but rather according to the mobile user's application usage
habits. The developed system was implemented in a real environment for one month. According to the
results, it was determined that processes transferred to the cloud were completed in less time and consumed
less energy.
KEYWORDS
Mobile Cloud Offloading, Mobile Cloud Computing, Context-Aware System, Forecasting, Dynamic
Estimation, Energy-Efficiency
1. INTRODUCTION
Mobile device sales have been increasing rapidly in recent years [1]. These devices can run
diverse applications to meet billions of users’ requests. Examples of these applications include
advanced media players, GPS applications, real-time gaming applications, and communications
applications. These mobile applications typically require user interaction, intensive data
processing, fast response times, and long battery life. When compared to computers, however,
mobile phones have more limited features in terms of processor capacity and battery life. Running
complex applications on a mobile device can lead to poor performance and shorten its battery life
[2]. Cloud computing services have become increasingly popular in recent years at low cost,
along with scalable and flexible architecture. The most appropriate way to overcome the
problems caused by the limitations of mobile devices can be thought of as mobile cloud
offloading [3].
Transferring a process to the cloud and retrieving the results naturally requires time and energy.
At the same time, the capacity and cost of the cloud environment must be taken into
consideration. A decision must be made to transfer the process to the cloud, taking into account
the cost of the transfer [2].
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
72
Many studies have been done on transferring a process from mobile devices to the cloud
environment [2, 4–8]. Unlike other studies, this study focuses on two main issues. The first is the
integration of the PowerTutor (PT) application into the developed application in order to be able
to measure power on the mobile device instantly without the need for any additional hardware.
The second is the execution of complex operations and web services with the AsyncTask class.
Many of the applications in similar studies were developed based on the Android operating
system, in which a complex process must be run with the AsyncTask class. This allows a
complex process to take longer to complete by allocating fewer resources rather than using all the
resources of the mobile device to complete it more quickly. For example, a process that can be
completed in 5 seconds using all available resources on the mobile device can be completed in 50
seconds by using fewer resources on the backplane [9]. This makes it more complex to measure
the performance of the processes running on the developed mobile cloud application.
When developing a mobile cloud application to run in a real environment, one must consider the
requirement to use the AsyncTask class and instant power measurement. In this study, all
processes are executed with AsyncTask class. For dynamic power measurement, the PT
application is customized and integrated into the developed application.
The complex processes selected for transfer to the cloud in the studies involving the application
were chosen to run randomly at certain time intervals. In this study, the selected processes were
run according to the usage habits of the mobile user. For this, the applications that mobile users
use in their daily life are paired with determined processes to be transferred to the cloud and run
in the background simultaneously.
In this research study, the following subjects have been developed and improved:
• Instantaneous power measurement is very important for a dynamic system that decides to
offloading to the cloud without needing any additional hardware. In this study, the open-source
PT application is integrated into the developed system for instantaneous power measurement
and the source code is customized for mobile devices of different brands.
• Due to the working principle of mobile operating systems, a complex process that can
negatively affect the operation of the device's main processes is carried out in the background.
The measured power value during the execution of a process is very important in the decision-
making stages. It may not be very accurate to calculate this measurement using the difference
in power between the start and end of the process. Instead, calculating with power values
obtained before and during the operation of the relevant process will provide more accurate
values. This study is unique in this respect; there is no study on this topic in the literature.
• It has been determined that most studies in this area are conducted in the simulation or
experimental environments. The mobile cloud system developed in this study was
implemented in a real environment and the results are compared.
• Processes determined for the application are not run at random time intervals as in similar
studies, but simultaneously in the background according to the usage habits of the mobile
users.
• One major disadvantage of mobile devices is their short battery life. Due to this, the time when
the mobile device will be plugged in is estimated, and until this time, information about
whether the current battery-power level is adequate is used in the decision stages.
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
73
2. RELATED WORK
A survey was conducted by Kumar and others in the field of mobile cloud offloading. According
to the data obtained, processes are transferred to the cloud environment in order to achieve high
performance or power savings [10]. In a study by Zhou and others, the PowerTutor application
has been used for dynamic access to power-consumption data [11]. At the same time, criteria like
power consumption, internet bandwidth, cloud resource status, signal level, and GSM internet
usage fees are used in making the decision to transfer a process to the cloud. In the study
conducted by Magurawalage and others, the power-consumption data and instantaneous internet
connection level values were used in the decision-making stages [12]. In addition, the cost values
of transferring to, working in, and receiving results from the cloud are also taken into
consideration. A special algorithm has been developed using this data and simulated with the
CloudSim application. Lin and others have developed a decision engine called CADA for
offloading to the cloud [2]. In addition to the performance data of past processes, CADA also
uses information like internet connection and location in its decision stages. The decision-making
engine developed in the study was implemented in the real environment. Hao and others
conducted a study to determine the energy efficient tasks. Then these tasks are provided for
caching and offloading. They formulate offloading problem as a mixed integer nonlinear
programming and propose efficient algorithm to solve this problem. Simulation result have shown
that proposed scheme has low energy cost compared to other schemes [5]. Chun and others have
developed an application called CloneCloud in their study [7]. The CloneCloud application
allows a process to be transferred to the cloud in parts rather than as a whole. Lim and Lee have
conducted a study on the pre-calculation of the energy consumed by processes transferred from
mobile to cloud [13]. It is noted that the pre-accurate estimate of the energy consumption is very
important in the stage of transferring processes to the cloud. In this study, an algorithm for
estimating dynamic energy consumption has been developed. The developed algorithm is
compared with decision table, linear regression, local weighted learning, sample based learning
and KStar algorithms. These algorithms were compared according to their ability to predict
energy consumption consumed as a result of facial recognition, chess, and video processing being
transferred to the cloud. In the facial recognition process, the proposed algorithm and the KStar
algorithm are close to each other and reach the correct estimates. In chess application, the
proposed algorithm made more accurate estimations than the other algorithms. In the video
processing application, the proposed algorithm and the linear regression algorithm have obtained
the most accurate estimates. Aldmour and others proposed an offloading model contains a
decision engine to examine the feasibility of the offloading [14]. This model investigates whether
or not a mobile phone preserves energy by offloading to cloud computing. It has been determined
that file size is important in this phase. This model takes into account the completion time and the
required energy values. According to experimental results, the performance of the cloud is
increased as the file size increases. Li and others proposed an energy-aware task offloading
mechanism in multiuser mobile cloud computing. Many criteria are used in the decision stages
and the solution of the optimization problem is solved by 0-1 nonlinear integer programming.
Proposed algorithm compared with competition-based algorithm and user-satisfaction algorithm.
The simulation results validate that the proposed algorithm can achieve better performances [15].
Islam and others have done a study on the processing of mass health data for smart cities [16].
The most important issue here is the transfer of patient data to the cloud system in a short time
and with less energy spent. For this, cloudlet structures have been established between mobile
devices and the public cloud system. Later, the appropriate Cloudlet selection was made with Ant
colony optimization. In the experimental study, patient records consisting of texts, documents and
pictures assigned to poisson distribution of 10 patients were transferred to the cloud environment.
The results are compared with the options of "not to cloud transfer","selection of appropriate first
cloudlet" and "model with genetic algorithm developed by researchers in previous studies". As a
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
74
result, developed system has provided the best values in terms of working time and the number of
tasks successfully completed.
When the literature is examined, it is observed that the studies done in this area are limited,
having gradually increased in recent years. Generally, studies can be categorized as either
collecting data on the mobile environment, making decisions about offloading to the cloud, or
offloading a process piece-by-piece. Generally, special algorithms have been developed for
solving the specified problems [2, 3, 6, 13, 14, 15, 16] . In a few studies, machine-learning
algorithms have been used [17-20]. Large parts of developed applications are context-sensitive.
Some of the studies were done on power consumption and completion time, some in the context
of internet-connection status, and some in the context of location and time. It has been determined
that most applications are run in the simulation environment or experimental application
environments. In a few publications, the results are evaluated based on the actual application
environment [2, 16, 20]. According to the results of the current studies, the developed solutions
can be efficient in terms of energy consumption and performance, the Wi-Fi connection
environment is generally more efficient than the GSM connection environment, and the cloud
environment provides better performance than does the mobile environment [2, 6, 11, 16, 22-26].
3. CONTEXT-SENSITIVE DECISION MAKING SYSTEM FOR MOBILE
CLOUD OFFLOADING
The system comprises two applications: a mobile application that works on mobile devices and a
Java web application that works in the cloud environment.
3.1. Mobile Application
The mobile application was developed with Android SDK (Software Development Kit) in the
Eclipse application development environment. SQLite was used as the database [27]. The
PowerTutor application was configured and integrated into the application. All processes were
executed with AsyncTask class [9].
The mobile application has two important modules: the profile-information collection module and
the past-process records module.
Process
Mobile / cloud
Power
Completion time
Internet connection
SQLite database
Figure 1. General structure of the past process records module.
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
75
Fig. 1 shows the general structure of the past-process records module. The result of running a
process in the mobile or cloud environment is recorded in the database. The recorded information
is as follows:
• For the process field, the "ID" value of the executed process is recorded.
• For the environment field, the value is set to "1" if the process is run in the mobile
environment and "2" if the process is run in the cloud environment.
• The time elapsed during the completion of the process is recorded in the time field.
• For the connection type field, “0” is recorded if the mobile device does not have internet
access, “1” if the mobile device's internet connection is provided via Wi-Fi, “2” if it is
provided via EDGE, “3” if provided via 3G, and “4” if provided via 4.5G (LTE).
• For the bandwidth field, the internet bandwidth value obtained during the transfer of the
relevant process to the cloud is recorded.
• For the power field, the power value calculated during the operation is recorded.
PT application can perform instantaneous power measurement. Rather than calculating the power
spent during the execution of a process using the difference between the start and end power
values, it is more accurate to obtain the power values at certain intervals during the execution to
calculate the average power. At this point, the average power value before the start time should be
taken into consideration, along with the average power value measured during the process. In this
study, to measure the average power value before the start of the process, the average working
time in the past of the relevant process is obtained as t and the time interval between t and the
start time of the process is taken into consideration
In Fig. 2, the power values measured for each second are observed before and during a process
that takes n seconds to complete. For example, the instantaneous power value at the start of the
process is shown as P0 and the instantaneous power value 1 second after starting is shown as P1.
The time interval for obtaining instantaneous power value from the data obtained in the PT
application is set to 1 second. In other words, the instantaneous power value measured in the PT
application can change over at least 1 second.
Pt Pt+1 P.. P0 P.. Pn-1 Pn
(t+1). s .. .. (n-1). s
t time ago start time of process n. s
Figure. 2. Calculation of average power value
In equation (1), for a process with an average completion time of t and a working time of n, the
difference is calculated between the power mean values before starting and during the work. The
resulting Port value is recorded in the process record table as the average power consumption
value.
t
P
n
P
P
t
i
it
n
i
n
ort
∑∑ =
+
=
−= 0
)(
0 (1)
These performance data of the process are used to estimate the completion time and energy cost
in decision stages. In similar studies, the past process information is recorded and used in the
decision stages [6, 11, 29].
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
76
3.2. Cloud Application
Cloud application is a web project developed with the Java programming language. This
application has two basic tasks: answering web service requests from mobile devices and
collecting data from mobile application databases in a central database. As part of the first task,
the processes running in the mobile application are also provided for the cloud with the same
library and the same code. For this task, the Rest service was used as web service structure
between the mobile and cloud applications. In the second task, a Mysql database was created in
the cloud environment and mobile application data was transferred to this database via web
services.
3.3. Application Matching
When similar studies are examined, it seems that complex processes have been used to transfer to
the cloud. The Eight Queens Problem, Character Counting in Log File, Face Detection, and
Optical Character Recognition (OCR) were selected for use in this study. These were chosen
because these complex processes consume more power and have longer completion times than
average. These processes are usually chosen from process types such as image processing and
mathematical cycle operations. It is not possible for a mobile device user to use an application
that involves these processes on a daily basis. Therefore, in the studies involving the application,
the specified processes are run randomly by selecting specific time intervals in the background.
Unlike similar studies, in this study the processes are run according to the mobile user's
application usage habits. For this, profile information, including the application information that
the user ran instantly, was recorded at 5-minute intervals for a week. Applications obtained from
all users’ profile information are rated as H (high), M (medium), or L (low) according to their
resource requirements and listed in Table 1.
Table 1. Applications and resource requirements from user profile information
Application CPU Storage Data Transfer GPU Battery Usage
Temple Run H M M H H
Bike Racing H M M H H
Moto Racing H M M H H
Real Racing 3 H M M H H
Candy Crush M M M M M
Bubble Shooter M M M M M
Word Game M M M M M
AutoCAD 360 H M L H H
WhatsApp M H M M M
Navigation Apps M L L M H
Snapchat H H H H H
Facebook M M H M M
Instagram M H H H M
Twitter M L M L M
Google Drive M H H L M
Forum Apps L L M L L
Advertisement Applications M L M L M
Shopping Apps M L M L M
Finance Apps L L M L L
Mobile Banking Applications L L M L L
News Apps M L M M L
Mail Apps L L L L L
Browser Apps M L M M L
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
77
A study by Pandi and Charaf categorized mobile applications according to their resource
requirements. Table 2 shows these categories and resource requirements [30].
Table 2. Application categories and resource requirements
Application Type CPU Storage Internet GPU
Browser Apps +++ ++ +++ ++
Browser + Flash Apps + + ++
Games +++ ++ ++ +++
Office Apps ++ + ++
Image View Apps ++ ++ ++
Chat(text) Apps + +
Chat (voice) Apps ++ ++
Offline Media Apps +++ ++ +++ +++
Storage Apps ++ +++ +++
NS/SSH(remote access) Applications + ++
Nugroho has provided information on the resource usage of mobile applications in his study. This
researcher has benefited from the application named “Terminal” when determining the resource
usage of mobile applications. This application can access the log files of the connected mobile
device and monitor the resources used by an application [31]. Within the scope of this study, the
resource requirements of the processes used in the cloud transfer stages are rated, taking into
account the related research in this area [30, .31]. The processes and resource requirements are
shown in Table 3.
Table 3. Processes and resource requirements used in the application
No Process CPU Storage Data Transfer GPU Battery Usage
1 Character counting M L M L M
2 Eight puzzle M L L L L
3 Face detect H M H H H
4 OCR H M M M H
Table 4. The mapping table of the types of processes to be used in study with the applications used by the users
User Application Matched Application
Temple Run Face detect
Bike Racing Face detect
Moto Racing Face detect
Real Racing 3 Face detect
Candy Crush OCR
Bubble Shooter OCR
Word Game OCR
AutoCAD 360 Face detect
WhatsApp OCR
GPS Apps Character counting
Snapchat Face detect
Facebook OCR
Instagram Face detect
Twitter Character counting
Google Drive OCR
Forum Apps Character counting
Advertisement Apps Character counting
Shopping Apps Character counting
Finance Apps Eight queens puzzle
Mobile Banking Apps Character counting
News Apps Eight queens puzzle
Mail Apps Eight queens puzzle
Browser Apps Eight queens puzzle
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
78
Taking all the information in this section into account, the applications that mobile users use in
their daily life are matched to the process types determined under study. The matches shown in
Table 4 are transferred to mobile applications via a web service. This allows the process to run
throughout the application in the background according to the specified matches.
4. DECISION MODEL FOR MOBILE CLOUD OFFLOADING
The decision model includes energy cost estimation, determination of the critical battery level and
the decision algorithm.
4.1. Energy Cost Estimation for Mobile and Cloud Environments
Cuervo and others have calculated the energy cost estimates for running a process in mobile and
cloud environments using the least-squares method and simple linear regression [32]. Lim and
Lee conducted a study on energy cost estimation using past-process performance data. At the end
of the study, the researchers stated that these estimation calculations could be done with the
"linear regression", "decision table" and "k nearest neighbors" methods [13]. In the thesis study,
Chang made predictions about completion time and power consumption in the cloud and mobile
environment using past performance information for a process. This researcher has benefited
from multiple linear-regression methods when making these estimates [33]. Khoda and others
have also benefited from past performance data while making prediction about the completion
time of a process. Researchers have benefited from statistical regression method when making
these predictions [28]. In this study, the past performance data has been accessed and the simple
linear regression has been used while estimating the energy cost and completion times of a
process.
Table 5 shows the symbols used in the calculation of energy cost and completion time. To
calculate the cost of energy and completion time if a p process is run with the bt connection type
in the cloud environment, all p process records that have been run with the bt connection type in
cloud environment are used. Within the specified n records, the completion time values are
obtained as {T1, T2,... Tn} ∈ T, the average power consumption values are obtained as {P1, P2,...
Pn} ∈ P and internet bandwidth values are obtained as {B1, B2,... Bn} ∈ B.
Table 5. Symbols and explanations used in calculation of energy cost and completion time
Symbol Definition
TECm,p Energy cost required for p process to run in mobile environment (J)
TECc,p Energy cost required for p process to run in cloud environment (J)
Tm,p Estimated completion time of p process in mobile environment (J)
Tc,p Estimated completion time of p process in cloud environment (s)
Pm,p Estimated power value of p process in mobile environment (mW)
Pc,p Estimated power value of p process in cloud environment (mW)
bt Instant internet connection type of mobile device
B Instant internet bandwidth of mobile device (bps)
The estimated completion time Tc,p for a p process in the cloud environment is obtained by linear
regression analysis using B and T values. Related calculations are shown in equations (2), (3) and
(4).
( )( ) ( )( )
( ) ( )22
2
0
∑∑
∑∑∑∑
−
−
=
BBn
BTBBT
b (2)
( ) ( )( )
( ) ( )221
∑∑
∑∑∑
−
−
=
BBn
TBBTn
b
(3)
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
79
BbbT pc 10, += (4)
Using linear regression analysis using B and P values obtains the estimated energy cost Pc, p in the
cloud environment for a p process. Related calculations are shown in equations (5), (6) and (7).
( )( ) ( )( )
( ) ( )22
2
0
∑∑
∑∑∑∑
−
−
=
BBn
BPBBP
b
(5)
( ) ( )( )
( ) ( )221
∑∑
∑∑∑
−
−
=
BBn
PBBPn
b (6)
BbbP pc 10, += (7)
After Tc,p and Pc,p values are obtained, the estimated energy cost for cloud environment TECc,p is
calculated. In similar studies, the estimated energy cost was calculated by multiplying the power
and time values [3, 4, 34].
pcpcpc PTTEC ,,, *= (8)
In order to calculate the cost of the estimated energy and completion time if a p process is run in
the mobile environment, all p work records that have been run in mobile environment are used.
Within the specified n records, the completion time values are obtained as {T1, T2,... Tn} ∈ T and
the average power consumption values are obtained as {P1, P2,... Pn} ∈ P. The Tm,p value is
determined as the average of T values. Pm,p value is also determined as the average of P values.
Finally, the estimated energy cost for mobile TEMm,p is calculated by multiplying the power and
time values as equation (9).
pmpmpm PTTEC ,,, *= (9)
4.2. Critical Battery Level Estimation
In this section it is estimated whether the mobile phone's battery level will be sufficient for
normal usage until the next charging time. Studies on this area have been examined [35, 36].This
information is used in the decision-making stages of running a process in the mobile or cloud
environment. Before running a process, the information about whether the battery level is
sufficient to last until the next charging time is obtained and the weighting factors in the decision
algorithm are changed according to this information.
To obtain information about whether the battery level will be sufficient, it is first necessary to
estimate when the mobile device will charge. In the next step, it is estimated that the applications
will be executed by considering the past application usage habits of the user until the estimated
charging time. Next, the amount of energy required to operate these applications is calculated.
Then, taking into account the user’s past energy consumption data, the amount of energy that the
mobile device needs for the process is calculated. Finally, information on the battery status is
obtained by comparing the amount of energy required and the amount of energy the mobile
device has.
A prediction of the next charging time is estimated will be made based on the user’s past charging
times. In the first step, the next charging time will be estimated by looking at the user’s past
charging times. Later, the previous predictions are compared with the actual charging times and
more accurate predictions are made by taking into account these prediction errors. It is
determined that an accurate prediction cannot be made if the past charging times obtained are
very different from each other or if there are too many past errors. In order to predict when the
mobile phone will be charged, the past charging times are obtained. The averages and standard
deviations are obtained by the minute conversion of the differences between these times and the
10. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
80
instantaneous time. If the calculated standard deviation value is too high, it is determined that the
past charging times of this phone are irregular and cannot make an accurate prediction. In cases
where the standard deviation value is small, prediction errors made in the past are also used for
prediction operations. For a better understanding of these prediction operations, the charge-
estimation scenarios are included in the related thesis study.
After estimating the next charging time, the processes to be run before the next charge will be
determined by taking advantage of the user’s past application-running habits. The Poisson
distribution was used at this stage. The four process types to be used for transfer to the cloud are
the Eight Queens puzzle, character counting, OCR, and facial detection. These process types are
shown in Fig. 3 as i1, i2, i3 and i4 respectively. In the following operations, for each process, the
number of runs between t1- t2 is determined according to the Poisson distribution.
i1 i3 i4 i2 i3 i1 i2 i3 i4
t0 t1 Total energy cost t2
Figure 3. Sample figure for estimation with Poisson distribution.
If an event appropriate to the Poisson distribution occurs λ times in the period t1-t0, the
probability of its occurrence x times in a different t2-t1 period is calculated using equation (10)
and equation (11).
t1t2
t0t1
t
−
−
= (10)
0,1,2...x,
x!
t)*(λe
P(x)
xt*λ
==
−
(11)
The above operations will result in x1, which provides the result of maximum P(x1). The value of
x1 will be determined as the number of runs for i1 between t1 and t2. The x2, x3 and x4 values are
also obtained in similar way.
After the values of x1, x2, x3 and x4 are determined, the required energy cost is calculated when
the specified processes are run in the mobile environment. The estimated energy cost of running a
p process in a mobile environment is expressed as TECm,p. In equation (12), the run number of
these processes is multiplied by the TECm,p values of each process. Then the total energy cost is
calculated by gathering the values obtained for the four processes.
i4m,i3m,i2m,i1m, TEC*x4TEC*x3TEC*x2TEC*x1TEC +++= (12)
Finally, the total amount of energy that the mobile phone has available to run the process until the
device is charged will be obtained. Significant amounts of energy are spent on operations like
operating systems, screen display, and phone calls. It is not possible to measure these energy
values separately, and they do not change much over time. It will be more accurate to consider the
energy that a mobile phone has just to run the process. By taking advantage of past process
records, the average completion times and average power values of the processes run on the two
specified time intervals will be obtained. As a result of multiplying these two values, the amount
of energy the mobile device has to run the process until it is charged will be estimated.
To summarize this section, in the first step, the mobile phone's next charging time is estimated.
Then, between the instantaneous time and the charging time, the processes that the user might run
were determined and the total energy cost required for these processes was calculated. Finally, the
11. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
81
amount of energy that the phone has available to run the process at the two specified time
intervals is estimated. As a result, information about the battery level is obtained by comparing
the amount of energy needed for the processes to be run with the amount of energy the phone has.
4.3. Decision Algorithm
In the previous chapters, if the mobile phone's next charging time can be estimated, the energy
cost required for its possible processes between the decision time and the charging time is
expressed as EM. In these two time periods, the amount of energy that the mobile phone has to run
the processes is determined as ES. In addition, the state of the mobile phone battery plugged in is
expressed as Bstate.
The following information is obtained for each p process for use in the algorithm:
• Tm,p = Estimated completion time of p process in the mobile environment
• Tc,p = Estimated completion time of p process in the cloud environment
• TECm,p = Energy cost estimation if the p process is run in the mobile environment
• TECc,p = Energy cost estimation if the p process is run in the cloud environment
The weight ratios determined for critical battery level are shown in Table 6. The weight ratios of
normal battery level are shown in Table 7. Weight ratios were used in similar studies [11, 37, 38]
Table 6. Weight ratios for critical battery level
Criterion Symbol Weight ratio
Energy EY 0,8
Performance PY 0,2
Table 7. Weight ratios for normal battery level
Criterion Symbol Weight ratio
Energy EN 0,8
Performance PN 0,2
The pseudo code of the decision algorithm is shown in Fig. 4. In the first phase of the algorithm,
the costs of running a process in the mobile and cloud environments are calculated. At this stage,
EM, ES and Bstate values are taken into consideration. In other words, if the mobile phone has
sufficient energy to run the required processes until the next charging time, or if the mobile phone
battery is plugged in, the weight values for the normal battery level will be used during the
calculation of mobile and cloud cost values. Otherwise, the weight values for the critical battery
level will be used. Thus, an energy priority decision can be made. The cost values for the mobile
and cloud environments are obtained as a result of multiplying the value of proportions of the
energy and time values with the determined weight ratios. Later, the costs of the mobile and cloud
environments are compared. If the cloud environment cost is lower than the cost of the mobile
environment, the relevant process is transferred to the cloud; otherwise, the process is run in the
mobile environment.
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Decision Algorithm
→
→
=
inplugged1
inpuluggednot0
Bstate
1. if(Em < ES or Bstate =1)
2. mobile_cost=
+
pc
pm
N
pc
pm
N
T
T
P
TEC
TEC
E
,
,
,
,
**
3. cloud_cost=
+
pm
pc
N
pm
pc
N
T
T
P
TEC
TEC
E
,
,
,
,
**
4. else
5. mobile_cost=
+
pc
pm
Y
pb
pm
Y
T
T
P
TEC
TEC
E
,
,
,
,
**
6. cloud_cost=
+
m,p
c,p
Y
m,p
c,p
Y
T
T
*P
TEC
TEC
*E
7. end if
9. if (cloud_cost < mobile_cost) then
10. transfer p process to cloud
11. else
12. run p process in mobile
13. end if
Figure 4. Pseudo code of decision algorithm.
5. EXPERIMENTAL RESULTS
The developed application has been used in the real environment by 7 mobile users. The
experimental study comprises two phases. In the first stage, a mobile application that could not
transfer its processes to the cloud was used for 15 days. In the second phase, a mobile cloud
application that could transfer its processes to the cloud was used for 15 days. At the end of the
experimental study, the mobile application data was transferred through web services to the
central database in the cloud environment.
While the mobile device users were identified for the experimental study, users were considered
to have different context information and had mobile devices with different specifications. The
brands and models of users’ mobile devices are listed in Table 8.
Table 8. Mobile phone models of users
User Mobile phone model
User 1 Samsung Galaxy A8
User 2 General Mobile Discovery 2
User 3 Samsung Note 4
User 4 Samsung Galaxy S6
User 5 LG G4
User 6 General Mobile 5 plus
User 7 LG G4
The mobile application's ".apk" extension installation file is available on the internet to mobile
phone users. The mobile application installed on the specified mobile phones ran on the
backplane without user interaction.
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5.1. Comparison of Mobile Application and Mobile Cloud Applications
The application used in the first phase of the experimental study is called "mobile application"
and the application used in the second phase and which was capable of transferring processes to
the cloud is called "mobile cloud application".
Figure 5. Comparison of mobile application and mobile cloud applications in terms of energy consumption
In Fig. 5, the mobile and mobile cloud applications are compared for each user in terms of
average energy consumption. In Fig. 6, the mobile and mobile cloud applications are compared in
terms of the completion times of the processes. If a user-based assessment is performed by
looking at Fig. 5 and Fig. 6, it can be said that the mobile cloud application provides better
performance for all users in terms of energy consumption and process completion times. It can
also be said that these performance values are higher for user 4 and user 5 and lower for user 1,
user 2 and user 3.
Figure 6. Comparison of mobile application and mobile cloud applications in terms of completion time.
5.2. Comparison of Mobile Environment and Mobile Cloud Environments
In this section, the data from the mobile cloud application used in the second 15 days of the
experimental study was compared in terms of the mobile and cloud environments.
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Fig. 7. Comparison of mobile and cloud environments in terms of energy consumption.
Fig. 7 shows the average energy consumption values spent in the mobile and cloud environments
for each user. This figure shows that the average energy spent in the cloud environment is less
than in the mobile environment for each user. For users 4 and 5, it can be said that about 50% of
energy saving is achieved by transferring processes to the cloud.
Figure 8. Comparison of mobile and cloud environments in terms of completion time.
In Fig. 8, average completion times within the mobile and cloud environments are compared for
each user. This figure shows that the process completion times for users 1, 2 and 3 are longer than
for other users. This may be because the technical capacities of these users' mobile devices are
more limited than the others’.
5.3. Performance Evaluation Based On Internet Connection Type
Wi-Fi, EDGE, 3G and 4.5G (LTE) internet connection types were used when transferring
processes to the cloud. Fig. 9 shows the number of processes transferred to the cloud by these
connection types for each user. When the figure is examined, it is seen that Wi-Fi is the most used
connection type in the cloud transfer stages for all users. After Wi-Fi, the most used connection
type was 4.5G.
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Figure 9. Number of processes running by connection types.
Fig. 10 shows average energy consumption values by each connection type for each user and Fig.
11 shows their average completion times. When the figures are examined, it can be said that the
processes that are generally operated with a Wi-Fi connection are completed in less time using
less energy. After Wi-Fi, the 4.5G connection type provides the best performance in terms of
energy consumption and completion time. It can even be said that the 4.5G connection type
performs better than Wi-Fi for user 5 and user 7. The reason for this is that these users have the
appropriate 4.5G connection in their location.
Figure 10. Comparison of connection types in terms of energy consumption.
Figure 11. Comparison of connection types in terms of completion types.
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5.4. Comparing Mobile and Cloud Environments Based On Process Type
When the graphs of energy and time values are examined, it is seen that, when compared to the
other processes, the eight queens puzzle process is completed in less time using less energy, and
the face-detection process is completed in more time using more energy. The reason that the eight
queens puzzle process can be completed in a shorter period using less energy in the mobile
environment is that this process requires fewer mobile device resources than do other processes.
This process can be run with lower values in the cloud environment because the size of the file
needing to be transferred to the cloud for this process is smaller than in other processes. The fact
that the face-detection process is completed in the mobile environment over a longer period using
more energy than other processes may indicate that this process uses more mobile resources than
do other processes. The reason this process is completed in the cloud environment using more
values than other processes is that the size of the file needing to be transferred to the cloud is
larger than in other processes.
Figure 12. Comparison of process types in terms of energy consumption.
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Figure 13. Comparison of process types in terms of completion time
6. CONCLUSION
In order to be able to make energy priority calculations at the decision stages, the next charging
time of the mobile device is estimated and it is determined whether the current battery level is
sufficient for normal use until the next charge. Based on this information, the weight ratios in the
decision algorithm are determined. When the estimates on mobile device charging times were
examined, it was determined that accurate estimates are made for users who regularly charge their
phones. For users without a regular charging habit, many prediction errors are made early on, but
these errors gradually decrease over time.
Comparing the results obtained by the mobile applications used in the first phase and mobile
cloud applications used in the second phase, the mobile cloud application showed better
performance at varying rates for each user in terms of completion time and energy consumption.
When the mobile and cloud environment performances are evaluated for each user, it was
determined that in the cloud environment, less energy usage and shorter run times are achieved
for each user. Due to the cloud environment, some users have achieved an approximately 50%
better performance in terms of energy and performance.
According to the results, it can be interpreted that the processes needing more mobile device
resources consume more energy in the mobile environment and take longer to complete, whereas
in the cloud environment, the size of the file transferred to the cloud is the decisive factor in
energy usage and completion time.
The most important result of this study is the conclusion that it may be possible to take advantage
of mobile cloud computing opportunities under appropriate conditions. If mobile application
developers and companies offer the opportunity to transfer specific processes to the cloud for
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their applications, it will be possible for their applications to perform well without using much
energy.
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AUTHORS
Dr. Mustafa Tanrıverdi received the Ph.D. degree in Management Information
System from Gazi University, Ankara, Turkey, in 2017. He was working in
Department of Computer in Gazi University, Turkey until 2007. He has research
interest are mobile applications, cloud computing, software development and
blockchain.
20. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
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Dr. M. Ali Akcayol is professor in Engineering Faculty at Gazi University, Turkey.
His research interests are mobile wireless networks, artificial intelligence, cloud
computing, web technologies, web mining, smart buildings. He has published a lot of
research paper in national, international journals and conferences in her areas of
expertise.