This document discusses the challenges of dynamic resource allocation and task scheduling in heterogeneous cloud environments. It outlines that resource allocation involves deciding how to allocate resources to tasks to maximize utilization, while task scheduling assigns tasks to processors to minimize execution time. The major challenges are optimizing allocated resources to minimize costs while meeting customer demands and application requirements. Allocating resources dynamically in heterogeneous cloud environments is difficult due to issues like resource contention, scarcity, and fragmentation. The document also discusses approaches to resource modeling, allocation, offering, discovery and monitoring that algorithms must address to effectively allocate resources on demand.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...IJCNCJournal
The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.
A Baye's Theorem Based Node Selection for Load Balancing in Cloud Environmentneirew J
Cloud computing is a popular computing model as it renders service to large number of users request on
the fly and has lead to the proliferation of large number of cloud users. This has lead to the overloaded
nodes in the cloud environment along with the problem of load imbalance among the cloud servers and
thereby impacts the performance. Hence, in this paper a heuristic Baye's theorem approach is considered
along with clustering to identify the optimal node for load balancing. Experiments using the proposed
approach are carried out on cloudsim simulator and are compared with the existing approach. Results
demonstrates that task deployment performed using this approach has improved performance in terms of
utilization and throughput when compared to the existing approaches.
A BAYE'S THEOREM BASED NODE SELECTION FOR LOAD BALANCING IN CLOUD ENVIRONMENThiij
Cloud computing is a popular computing model as it renders service to large number of users request on
the fly and has lead to the proliferation of large number of cloud users. This has lead to the overloaded
nodes in the cloud environment along with the problem of load imbalance among the cloud servers and
thereby impacts the performance. Hence, in this paper a heuristic Baye's theorem approach is considered
along with clustering to identify the optimal node for load balancing. Experiments using the proposed
approach are carried out on cloudsim simulator and are compared with the existing approach. Results
demonstrates that task deployment performed using this approach has improved performance in terms of
utilization and throughput when compared to the existing approaches
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...IJCNCJournal
The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.
A Baye's Theorem Based Node Selection for Load Balancing in Cloud Environmentneirew J
Cloud computing is a popular computing model as it renders service to large number of users request on
the fly and has lead to the proliferation of large number of cloud users. This has lead to the overloaded
nodes in the cloud environment along with the problem of load imbalance among the cloud servers and
thereby impacts the performance. Hence, in this paper a heuristic Baye's theorem approach is considered
along with clustering to identify the optimal node for load balancing. Experiments using the proposed
approach are carried out on cloudsim simulator and are compared with the existing approach. Results
demonstrates that task deployment performed using this approach has improved performance in terms of
utilization and throughput when compared to the existing approaches.
A BAYE'S THEOREM BASED NODE SELECTION FOR LOAD BALANCING IN CLOUD ENVIRONMENThiij
Cloud computing is a popular computing model as it renders service to large number of users request on
the fly and has lead to the proliferation of large number of cloud users. This has lead to the overloaded
nodes in the cloud environment along with the problem of load imbalance among the cloud servers and
thereby impacts the performance. Hence, in this paper a heuristic Baye's theorem approach is considered
along with clustering to identify the optimal node for load balancing. Experiments using the proposed
approach are carried out on cloudsim simulator and are compared with the existing approach. Results
demonstrates that task deployment performed using this approach has improved performance in terms of
utilization and throughput when compared to the existing approaches
RESOURCE ALLOCATION METHOD FOR CLOUD COMPUTING ENVIRONMENTS WITH DIFFERENT SE...IJCNCJournal
In a cloud computing environment with multiple data centers over a wide area, it is highly likely that each data center would provide the different service quality to users at different locations. It is also required to consider the nodes at the edge of the network (local cloud) which support applications such as IoTs that require low latency and location awareness. The authors proposed the joint multiple resource allocation method in a cloud computing environment that consists of multiple data centers and each data center provides the different network delay. However, the existing method does not take account of cases where requests that require a short network delay occur more than expected. Moreover, the existing method does not take account of service processing time in data centers and therefore cannot provide the optimal resource allocation when it is necessary to take the total processing time (both network delay and service processing time in a data center) into consideration in resource allocation.
LOAD BALANCING ALGORITHM ON CLOUD COMPUTING FOR OPTIMIZE RESPONE TIMEijccsa
To improve the performance of cloud computing, there are many parameters and issues that we should consider, including resource allocation, resource responsiveness, connectivity to resources, unused resources exploration, corresponding resource mapping and planning for resource. The planning for the use of resources can be based on many kinds of parameters, and the service response time is one of them.
The users can easily figure out the response time of their requests, and it becomes one of the important QoSs. When we discover and explore more on this, response time can provide solutions for the distribution, the load balancing of resources with better efficiency. This is one of the most promising
research directions for improving the cloud technology. Therefore, this paper proposes a load balancing algorithm based on response time of requests on cloud with the name APRA (ARIMA Prediction of Response Time Algorithm), the main idea is to use ARIMA algorithms to predict the coming response time, thus giving a better way of effectively resolving resource allocation with threshold value. The experiment
result outcomes are potential and valuable for load balancing with predicted response time, it shows that prediction is a great direction for load balancing.
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.
Cloud computing is a new computing paradigm that, just as electricity was firstly generated at home and
evolved to be supplied from a few utility providers, aims to transform computing into a utility. It is a mapping
strategy that efficiently equilibrates the task load into multiple computational resources in the network based on the
system status to improve performance. The objective of this research paper is to show the results of Hybrid DEGA,
in which GA is implemented after DE
A Prolific Scheme for Load Balancing Relying on Task Completion Time IJECEIAES
In networks with lot of computation, load balancing gains increasing significance. To offer various resources, services and applications, the ultimate aim is to facilitate the sharing of services and resources on the network over the Internet. A key issue to be focused and addressed in networks with large amount of computation is load balancing. Load is the number of tasks„t‟ performed by a computation system. The load can be categorized as network load and CPU load. For an efficient load balancing strategy, the process of assigning the load between the nodes should enhance the resource utilization and minimize the computation time. This can be accomplished by a uniform distribution of load of to all the nodes. A Load balancing method should guarantee that, each node in a network performs almost equal amount of work pertinent to their capacity and availability of resources. Relying on task subtraction, this work has presented a pioneering algorithm termed as E-TS (Efficient-Task Subtraction). This algorithm has selected appropriate nodes for each task. The proposed algorithm has improved the utilization of computing resources and has preserved the neutrality in assigning the load to the nodes in the network.
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
Quality of Service based Task Scheduling Algorithms in Cloud Computing IJECEIAES
In cloud computing resources are considered as services hence utilization of the resources in an efficient way is done by using task scheduling and load balancing. Quality of service is an important factor to measure the trustiness of the cloud. Using quality of service in task scheduling will address the problems of security in cloud computing. This paper studied quality of service based task scheduling algorithms and the parameters used for scheduling. By comparing the results the efficiency of the algorithm is measured and limitations are given. We can improve the efficiency of the quality of service based task scheduling algorithms by considering these factors arriving time of the task, time taken by the task to execute on the resource and the cost in use for the communication.
Cloud computing Review over various scheduling algorithmsIJEEE
Cloud computing has taken an importantposition in the field of research as well as in thegovernment organisations. Cloud computing uses virtualnetwork technology to provide computer resources tothe end users as well as to the customer’s. Due tocomplex computing environment the use of high logicsand task scheduler algorithms are increase which resultsin costly operation of cloud network. Researchers areattempting to build such kind of job scheduling algorithms that are compatible and applicable in cloud computing environment.In this paper, we review research work which is recently proposed by researchers on the base of energy saving scheduling techniques. We also studying various scheduling algorithms and issues related to them in cloud computing.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
PERFORMANCE FACTORS OF CLOUD COMPUTING DATA CENTERS USING [(M/G/1) : (∞/GDM O...ijgca
The ever-increasing status of the cloud computing h
ypothesis and the budding concept of federated clou
d
computing have enthused research efforts towards in
tellectual cloud service selection aimed at develop
ing
techniques for enabling the cloud users to gain max
imum benefit from cloud computing by selecting
services which provide optimal performance at lowes
t possible cost. Cloud computing is a novel paradig
m
for the provision of computing infrastructure, whic
h aims to shift the location of the computing
infrastructure to the network in order to reduce th
e maintenance costs of hardware and software resour
ces.
Cloud computing systems vitally provide access to l
arge pools of resources. Resources provided by clou
d
computing systems hide a great deal of services fro
m the user through virtualization. In this paper, t
he
cloud data center is modelled as
queuing system with a single task arrivals
and a task request buffer of infinite capacity.
A survey on various resource allocation policies in cloud computing environmenteSAT 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.
A survey on various resource allocation policies in cloud computing environmenteSAT Journals
Abstract Cloud computing is bringing a revolution in computing environment replacing traditional software installations, licensing issues into complete on-demand services through internet. In Cloud computing multiple cloud users can request number of cloud services simultaneously. So there must be a provision that all resources are made available to requesting user in efficient manner to satisfy their need. Resource allocation is based on quality of service and service level agreement. In cloud computing environment, to allocate resources to the user there are several methods but provider should consider the efficient way to guarantee that the applications’ requirements are attended to correctly and satisfy the user’s need This paper survey different resource allocation policies used in cloud computing environment. Keywords: Cloud computing, Resource allocation
A SURVEY ON RESOURCE ALLOCATION IN CLOUD COMPUTINGijccsa
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
A SURVEY ON RESOURCE ALLOCATION IN CLOUD COMPUTINGijccsa
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
RESOURCE ALLOCATION METHOD FOR CLOUD COMPUTING ENVIRONMENTS WITH DIFFERENT SE...IJCNCJournal
In a cloud computing environment with multiple data centers over a wide area, it is highly likely that each data center would provide the different service quality to users at different locations. It is also required to consider the nodes at the edge of the network (local cloud) which support applications such as IoTs that require low latency and location awareness. The authors proposed the joint multiple resource allocation method in a cloud computing environment that consists of multiple data centers and each data center provides the different network delay. However, the existing method does not take account of cases where requests that require a short network delay occur more than expected. Moreover, the existing method does not take account of service processing time in data centers and therefore cannot provide the optimal resource allocation when it is necessary to take the total processing time (both network delay and service processing time in a data center) into consideration in resource allocation.
LOAD BALANCING ALGORITHM ON CLOUD COMPUTING FOR OPTIMIZE RESPONE TIMEijccsa
To improve the performance of cloud computing, there are many parameters and issues that we should consider, including resource allocation, resource responsiveness, connectivity to resources, unused resources exploration, corresponding resource mapping and planning for resource. The planning for the use of resources can be based on many kinds of parameters, and the service response time is one of them.
The users can easily figure out the response time of their requests, and it becomes one of the important QoSs. When we discover and explore more on this, response time can provide solutions for the distribution, the load balancing of resources with better efficiency. This is one of the most promising
research directions for improving the cloud technology. Therefore, this paper proposes a load balancing algorithm based on response time of requests on cloud with the name APRA (ARIMA Prediction of Response Time Algorithm), the main idea is to use ARIMA algorithms to predict the coming response time, thus giving a better way of effectively resolving resource allocation with threshold value. The experiment
result outcomes are potential and valuable for load balancing with predicted response time, it shows that prediction is a great direction for load balancing.
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.
Cloud computing is a new computing paradigm that, just as electricity was firstly generated at home and
evolved to be supplied from a few utility providers, aims to transform computing into a utility. It is a mapping
strategy that efficiently equilibrates the task load into multiple computational resources in the network based on the
system status to improve performance. The objective of this research paper is to show the results of Hybrid DEGA,
in which GA is implemented after DE
A Prolific Scheme for Load Balancing Relying on Task Completion Time IJECEIAES
In networks with lot of computation, load balancing gains increasing significance. To offer various resources, services and applications, the ultimate aim is to facilitate the sharing of services and resources on the network over the Internet. A key issue to be focused and addressed in networks with large amount of computation is load balancing. Load is the number of tasks„t‟ performed by a computation system. The load can be categorized as network load and CPU load. For an efficient load balancing strategy, the process of assigning the load between the nodes should enhance the resource utilization and minimize the computation time. This can be accomplished by a uniform distribution of load of to all the nodes. A Load balancing method should guarantee that, each node in a network performs almost equal amount of work pertinent to their capacity and availability of resources. Relying on task subtraction, this work has presented a pioneering algorithm termed as E-TS (Efficient-Task Subtraction). This algorithm has selected appropriate nodes for each task. The proposed algorithm has improved the utilization of computing resources and has preserved the neutrality in assigning the load to the nodes in the network.
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
Quality of Service based Task Scheduling Algorithms in Cloud Computing IJECEIAES
In cloud computing resources are considered as services hence utilization of the resources in an efficient way is done by using task scheduling and load balancing. Quality of service is an important factor to measure the trustiness of the cloud. Using quality of service in task scheduling will address the problems of security in cloud computing. This paper studied quality of service based task scheduling algorithms and the parameters used for scheduling. By comparing the results the efficiency of the algorithm is measured and limitations are given. We can improve the efficiency of the quality of service based task scheduling algorithms by considering these factors arriving time of the task, time taken by the task to execute on the resource and the cost in use for the communication.
Cloud computing Review over various scheduling algorithmsIJEEE
Cloud computing has taken an importantposition in the field of research as well as in thegovernment organisations. Cloud computing uses virtualnetwork technology to provide computer resources tothe end users as well as to the customer’s. Due tocomplex computing environment the use of high logicsand task scheduler algorithms are increase which resultsin costly operation of cloud network. Researchers areattempting to build such kind of job scheduling algorithms that are compatible and applicable in cloud computing environment.In this paper, we review research work which is recently proposed by researchers on the base of energy saving scheduling techniques. We also studying various scheduling algorithms and issues related to them in cloud computing.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
PERFORMANCE FACTORS OF CLOUD COMPUTING DATA CENTERS USING [(M/G/1) : (∞/GDM O...ijgca
The ever-increasing status of the cloud computing h
ypothesis and the budding concept of federated clou
d
computing have enthused research efforts towards in
tellectual cloud service selection aimed at develop
ing
techniques for enabling the cloud users to gain max
imum benefit from cloud computing by selecting
services which provide optimal performance at lowes
t possible cost. Cloud computing is a novel paradig
m
for the provision of computing infrastructure, whic
h aims to shift the location of the computing
infrastructure to the network in order to reduce th
e maintenance costs of hardware and software resour
ces.
Cloud computing systems vitally provide access to l
arge pools of resources. Resources provided by clou
d
computing systems hide a great deal of services fro
m the user through virtualization. In this paper, t
he
cloud data center is modelled as
queuing system with a single task arrivals
and a task request buffer of infinite capacity.
A survey on various resource allocation policies in cloud computing environmenteSAT 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.
A survey on various resource allocation policies in cloud computing environmenteSAT Journals
Abstract Cloud computing is bringing a revolution in computing environment replacing traditional software installations, licensing issues into complete on-demand services through internet. In Cloud computing multiple cloud users can request number of cloud services simultaneously. So there must be a provision that all resources are made available to requesting user in efficient manner to satisfy their need. Resource allocation is based on quality of service and service level agreement. In cloud computing environment, to allocate resources to the user there are several methods but provider should consider the efficient way to guarantee that the applications’ requirements are attended to correctly and satisfy the user’s need This paper survey different resource allocation policies used in cloud computing environment. Keywords: Cloud computing, Resource allocation
A SURVEY ON RESOURCE ALLOCATION IN CLOUD COMPUTINGijccsa
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
A SURVEY ON RESOURCE ALLOCATION IN CLOUD COMPUTINGijccsa
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
A Survey on Resource Allocation in Cloud Computingneirew J
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
Allocation Strategies of Virtual Resources in Cloud-Computing NetworksIJERA Editor
In distributed computing, Cloud computing facilitates pay per model as per user demand and requirement.
Collection of virtual machines including both computational and storage resources will form the Cloud. In
Cloud computing, the main objective is to provide efficient access to remote and geographically distributed
resources. Cloud faces many challenges, one of them is scheduling/allocation problem. Scheduling refers to a
set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its
allocation strategy according to the changing environment and the type of task. In this paper we will see FCFS,
Round Robin scheduling in addition to Linear Integer Programming an approach of resource allocation.
Linked List Implementation of Discount Pricing in Cloudpaperpublications3
Abstract: In the cloud computing environment computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. In order to enjoy these discounts, the customers must be ready to adjust the time limits. By strategically scheduling multiple customers’ resource request, a cloud broker takes the responsibility of distributing the discounts offered by cloud service providers. Here the focus is on how a broker can help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. A randomized online stack-centric scheduling algorithm (ROSA) is implemented with linked list in order to maintain the status of the resource and to allocate resources without time constrains.
PROPOSED ONTOLOGY FRAMEWORK FOR DYNAMIC RESOURCE PROVISIONING ON PUBLIC CLOUDIAEME Publication
Cloud computing is an essential ingredient of today’s modern information technology. Cloud computing is totally based on internet. With the use of cloud computing resources can be shared from anywhere and anytime. In cloud computing there are multiple users simultaneously requests for the number of services and its important to provision all resources to user in efficient manner to satisfy their requirements. To come out this problem in this paper we had reviwed the different types of resource allocation strategies and proposed an ontology based resource management framwork for dynamic resource allocation. Ontology Framework contain four sections, each section equipped with functionality to collect information regarding all resources available in actual cloud deployment based on signed SLA agreement, and then replies to the user with appropriate allocation.
Improving resource utilization in infrastructural cloudeSAT Journals
Abstract
In this paper, an introspective overview is made on the design aspects of (Software as Service) Cloud system by improving
infrastructure utilization integrating different lease and scheduling techniques for user and resources to implement On-demand
allocation of resources to the user. The primary aim of Cloud Computing is to provide mobility development of web-based
application by means of early accessible tools and interfaces by using and manipulating infrastructure. Cloud-based services
integrate global scattered resource, which offer its users different types of services without the difficulties and complications. In
this paper main focus is on resource allocation to user, both Premium and Non-Premium with more priority to Premium user
managing their effective utilization and providing security from data alteration and modification. In this paper a small protocol of
cloud application is implemented adding security to it making the stored user data and information secure via the fraud detection
system. Its main purpose is to improve utilization of infrastructure Cloud by providing On-demand availability of the resources to
the users by reducing the expenses. Application are network based so that the business user free to use the services from anywhere
that they choose using virtually any type of electronic device. Each application is pay-per-usage basis, allowing the business
owner to predict their budget for the usage of number of application according to business need. This system offers a less
expensive platform and infrastructure solution to improve the efficiency and elasticity of IT operations.
Keywords: Cloud Computing, Encryption and Decryption, Reverse Circle Cipher and Upper Bound.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
ABSTRACT
Cloud computing utilizes large scale computing infrastructure that has been radically changing the IT landscape enabling remote access to computing resources with low service cost, high scalability , availability and accessibility. Serving tasks from multiple users where the tasks are of different characteristics with variation in the requirement of computing power may cause under or over utilization of resources.Therefore maintaining such mega-scale datacenter requires efficient resource management procedure to increase resource utilization. However, while maintaining efficiency in service provisioning it is necessary to ensure the maximization of profit for the cloud providers. Most of the current research works aims at how providers can offer efficient service provisioning to the user and improving system performance. There are comparatively fewer specific works regarding resource management which also deals with the economic section that considers profit maximization for the provider. In this paper we represent a model that deals with both efficient resource utilization and pricing of the resources. The joint resource management model combines the work of user assignment, task scheduling and load balancing on the fact of CPU power endorsement. We propose four algorithms respectively for user assignment, task scheduling, load balancing and pricing that works on group based resources offering reduction in task execution time(56.3%),activated physical machines(41.44%),provisioning cost(23%) . The cost is calculated over a time interval involving the number of served customer at this time and the amount of resources used within this time
GROUP BASED RESOURCE MANAGEMENT AND PRICING MODEL IN CLOUD COMPUTINGijcsit
Cloud computing utilizes large scale computing infrastructure that has been radically changing the IT landscape enabling remote access to computing resources with low service cost, high scalability , availability and accessibility. Serving tasks from multiple users where the tasks are of different characteristics with variation in the requirement of computing power may cause under or over utilization of resources.Therefore maintaining such mega-scale datacenter requires efficient resource management procedure to increase resource utilization. However, while maintaining efficiency in service provisioning it is necessary to ensure the maximization of profit for the cloud providers. Most of the current research works aims at how providers can offer efficient service provisioning to the user and improving system performance. There are comparatively fewer specific works regarding resource management which also deals with the economic section that considers profit maximization for the provider. In this paper we represent a model that deals with both efficient resource utilization and pricing of the resources. The joint resource management model combines the work of user assignment, task scheduling and load balancing on the fact of CPU power endorsement. We propose four algorithms respectively for user assignment, task scheduling, load balancing and pricing that works on group based resources offering reduction in task execution time(56.3%),activated physical machines(41.44%),provisioning cost(23%) . The cost is calculated over a time interval involving the number of served customer at this time and the amount of resources used within this time.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Efficient Resource Sharing In Cloud Using Neural NetworkIJERA Editor
In cloud computing, collaborative cloud computing(CCC) is the emerging technology where globally-dispersed cloud resource belonging to different organization are collectively used in a cooperative manner to provide services. In previous research, Harmony enables a node to locate its desired resources and also find the reputation of the located resources, so that a client can choose resource providers not only by resource availability but also by the provider’s reputation of providing the resource. In proposed system to reform resource utilization based on optimal time period to allocate resources to the neural network training and to load factor calculation the dynamic priority scheduling technique is used to assign the priority to the cloud users according to their load. The dynamic priority scheduling algorithm strikes the right balance between performance and power efficiency.
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
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Hybrid Based Resource Provisioning in CloudEditor IJCATR
The data centres and energy consumption characteristics of the various machines are often noted with different capacities.
The public cloud workloads of different priorities and performance requirements of various applications when analysed we had noted
some invariant reports about cloud. The Cloud data centres become capable of sensing an opportunity to present a different program.
In out proposed work, we are using a hybrid method for resource provisioning in data centres. This method is used to allocate the
resources at the working conditions and also for the energy stored in the power consumptions. Proposed method is used to allocate the
process behind the cloud storage.
Similar to Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneous Clouds (20)
Data Mining is a significant field in today’s data-driven world. Understanding and implementing its concepts can lead to discovery of useful insights. This paper discusses the main concepts of data mining, focusing on two main concepts namely Association Rule Mining and Time Series Analysis
A Review on Real Time Integrated CCTV System Using Face Detection for Vehicle...rahulmonikasharma
We are describes the technique for real time human face detection and counting the number of passengers in vehicle and also gender of the passengers.The Image processing technology is very popular,now at present all are going to use it for various purpose. It can be applied to various applications for detecting and processing the digital images. Face detection is a part of image processing. It is used for finding the face of human in a given area. Face detection is used in many applications such as face recognition, people tracking, or photography. In this paper,The webcam is installed in public vehicle and connected with Raspberry Pi model. We use face detection technique for detecting and counting the number of passengers in public vehicle via webcam with the help of image processing and Raspberry Pi.
Considering Two Sides of One Review Using Stanford NLP Frameworkrahulmonikasharma
Sentiment analysis is a type of natural language processing for tracking the mood of the public about a particular product or a topic and is useful in several ways. Polarity shift is the most classical task which aims at classifying the reviews either positive or negative. But in many cases, in addition to the positive and negative reviews, there still many neutral reviews exist. However, the performance sometimes limited due to the fundamental deficiencies in handling the polarity shift problem. We propose an Improvised Dual Sentiment Analysis (IDSA) model to address this problem for sentiment classification. We first propose a novel data expansion technique by creating sentiment-reversed review for each training and test review. We develop a corpus based method to construct a pseudo-antonym dictionary. It removes DSA’s dependency on an external antonym dictionary for review reversion. We conduct a range of experiments and the results demonstrates the effectiveness of DSA in addressing the polarity shift in sentiment classification. .
A New Detection and Decoding Technique for (2×N_r ) MIMO Communication Systemsrahulmonikasharma
The requirements of fifth generation new radio (5G- NR) access networks are very high capacity and ultra-reliability. In this paper, we proposed a V-BLAST2 × N_r MIMO system that is analyzed, improved, and expected to achieve both very high throughput and ultra- reliability simultaneously.A new detection technique called parallel detection algorithm is proposed. The performance of the proposed algorithm compared with existing linear detection algorithms. It was seen that the proposed technique increases the speed of signal transmission and prevents error propagation which may be present in serial decoding techniques. The new algorithm reduces the bit error probability and increases the capacity simultaneouslywithout using a standard STC technique. However, it was seen that the BER of systems using the proposed algorithm is slightly higher than a similar system using only STC technique. Simulation results show the advantages of using the proposed technique.
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A wireless network enables people to communicate and access applications and information without wires. This provides freedom of movement and the ability to extend applications to different parts of a building, city, or nearly anywhere in the world. Wireless networks allow people to interact with e-mail or browse the Internet from a location that they prefer. Adhoc Networks are self-organizing wireless networks, absent any fixed infrastructure. broadcasting of data through proper channel is essential. Various protocols are designed to avoid the loss of data. In this paper an overview of different broadcast protocols are discussed.
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Security is important for many sensor network applications. A particularly harmful attack against sensor and ad hoc networks is known as the Sybil attack [6], where a node Illegitimately claims multiple identities.Mobility cause a main problem when we talk about security in Mobile Ad-hoc networks. It doesn’t depend on fixed architecture, the nodes are continuously moving in a random fashion. In this article we will focus on identifying the Sybil attack in MANET. It uses air medium for communication so it is more prone to the attack. Sybil attack is one in which single node present multiple fake identities to other nodes, which cause destruction.
A Landmark Based Shortest Path Detection by Using A* and Haversine Formularahulmonikasharma
In 1900, less than 20 percent of the world populace lived in cities, in 2007, fair more than 50 percent of the world populace lived in cities. In 2050, it has been anticipated that more than 70 percent of the worldwide population (about 6.4 billion individuals) will be city tenants. There's more weight being set on cities through this increment in population [1]. With approach of keen cities, data and communication technology is progressively transforming the way city regions and city inhabitants organize and work in reaction to urban development. In this paper, we create a nonspecific plot for navigating a route throughout city A asked route is given by utilizing combination of A* Algorithm and Haversine equation. Haversine Equation gives least distance between any two focuses on spherical body by utilizing latitude and longitude. This least distance is at that point given to A* calculation to calculate minimum distance. The method for identifying the shortest path is specify in this paper.
Processing Over Encrypted Query Data In Internet of Things (IoTs) : CryptDBs,...rahulmonikasharma
Internet of Things (IoT) is the developing technologies that would be the biggest agents to modify the current world. Machine-to-machine communications perform with virtual, mobile and instantaneous connections. In IoT system, it consists of data-gathering sensors various other household devices. Intended for protecting IoT system, the end-to-end secure communication is a necessary measure to protect against unauthorized entities (e.g., modification attacks and eavesdropping,) and the data unprotected on the Cloud. The most important concern hereby is how to preserve the insightful information and to provide the privacy of user data. In IoT, the encrypted data computing is based on techniques appear to be promising approaches. In this paper, we discuss about the recent secure database systems, which are capable to execute SQL queries over encrypted data.
Quality Determination and Grading of Tomatoes using Raspberry Pirahulmonikasharma
In India cultivation of tomatoes is carried out by traditional methods and techniques. Today tremendous improvement in field of agriculture technologies and products can be seen. The tomatoes affect the overall production drastically. Image processing technique can be key technique for finding good qualities of tomatoes and grading. This work aimed to study different types of algorithms used for quality grading and sorting of fruit from the acquire image. In previous years several types of techniques are applied to analyses the good quality fruits. A simple system can be implemented using Raspberry pi with computer vision technology and image processing algorithms.
Comparative of Delay Tolerant Network Routings and Scheduling using Max-Weigh...rahulmonikasharma
Network management and Routing is supportively done by performing with the nodes, due to infrastructure-less nature of the network in Ad hoc networks or MANET. The nodes are maintained itself from the functioning of the network, for that reason the MANET security challenges several defects. Routing process and Scheduling is a significant idea to enhance the security in MANET. Other than, scheduling has been recognized to be a key issue for implementing throughput/capacity optimization in Ad hoc networks. Designed underneath conventional (LT) light tailed assumptions, traffic fundamentally faces Heavy-tailed (HT) assumption of the validity of scheduling algorithms. Scheduling policies are utilized for communication networks such as Max-Weight, backpressure and ACO, which are provably throughput optimality and the Pareto frontier of the feasible throughput region under maximal throughput vector. In wireless ad-hoc network, the issue of routing and optimal scheduling performs with time varying channel reliability and multiple traffic streams. Depending upon the security issues within MANETs in this paper presents a comparative analysis of existing scheduling policies based on their performance to progress the delay performance in most scenarios. The security issues of MANETs considered from this paper presents a relative analysis of existing scheduling policies depend on their performance to progress the delay performance in most developments.
DC Conductivity Study of Cadmium Sulfide Nanoparticlesrahulmonikasharma
The dc conductivity of consolidated nanoparticle of CdS has been studied over the temperature range from 303 K to 523 K and the conductivity has been found to be much larger than that of single crystals.
A Survey on Peak to Average Power Ratio Reduction Methods for LTE-OFDMrahulmonikasharma
OFDM (Orthogonal Frequency Division Multiplexing) is generally preferred for high data rate transmission in digital communication. The Long-Term Evolution (LTE) standards for the fourth generation (4G) wireless communication systems. Orthogonal Frequency Division Multiple Access (OFDMA) and Single Carrier Frequency Division Multiple Access (SC-FDMA) are the two multiple access techniques which are generally used in LTE.OFDM system has a major shortcoming of high peak to average power ratio (PAPR) value. This paper explains different PAPR reduction techniques and presents a comparison of the various techniques based on theoretical results. It also presents a survey of the various PAPR reduction techniques and the state of the art in this area.
IOT Based Home Appliance Control System, Location Tracking and Energy Monitoringrahulmonikasharma
Home automation has been a dream of sciences for so many years. It could wind up conceivable in twentieth century simply after power all family units and web administrations were begun being utilized on across the board level. The point of home robotization is to give enhanced accommodation, comfort, vitality effectiveness and security. Vitality checking and protection holds prime significance in this day and age in view of the irregularity between control age and request observing frameworks accessible in the market. Ordinarily, customers are disappointed with the power charge as it doesn't demonstrate the power devoured at the gadget level. This paper shows the outline and execution of a vitality meter utilizing Arduino microcontroller which can be utilized to gauge the power devoured by any individual electrical apparatus. The primary expectation of the proposed vitality meter is to screen the power utilization at the gadget level, transfer it to the server and build up remote control of any apparatus. So we can screen the power utilization remotely and close down gadgets if vital. The car segment is additionally one of the application spaces where vehicle can be made keen by utilizing "IOT". So a vehicle following framework is additionally executed to screen development of vehicles remotely.
Thermal Radiation and Viscous Dissipation Effects on an Oscillatory Heat and ...rahulmonikasharma
An anticipated outcome that is intended chapter is to investigate effects of magnetic field on an oscillatory flow of a viscoelastic fluid with thermal radiation, viscous dissipation with Ohmic heating which bounded by a vertical plane surface, have been studied. Analytical solutions for the quasi – linear hyperbolic partial differential equations are obtained by perturbation technique. Solutions for velocity and temperature distributions are discussed for various values of physical parameters involving in the problem. The effects of cooling and heating of a viscoelastic fluid compared to the Newtonian fluid have been discussed.
Advance Approach towards Key Feature Extraction Using Designed Filters on Dif...rahulmonikasharma
In fast growing database repository system, image as data is one of the important concern despite text or numeric. Still we can’t replace test on any cost but for advancement, information may be managed with images. Therefore image processing is a wide area for the researcher. Many stages of processing of image provide researchers with new ideas to keep information safe with better way. Feature extraction, segmentation, recognition are the key areas of the image processing which helps to enhance the quality of working with images. Paper presents the comparison between image formats like .jpg, .png, .bmp, .gif. This paper is focused on the feature extraction and segmentation stages with background removal process. There are two filters, one is integer filter and second one is floating point Filter, which is used for the key feature extraction from image. These filters applied on the different images of different formats and visually compare the results.
Alamouti-STBC based Channel Estimation Technique over MIMO OFDM Systemrahulmonikasharma
The examination on various looks into on MIMO STBC framework in order to accomplish the higher framework execution is standard that the execution of the remote correspondence frameworks can be improved by usage numerous transmit and get radio wires, that is normally gathered on the grounds that the MIMO procedure, and has been incorporated. The Alamouti STBC might be a promising because of notice the pick up inside the remote interchanges framework misuse MIMO. To broaden the code rate and furthermore the yield of the symmetrical zone time square code for more than 4 transmit reception apparatuses is examined. The outlined framework is beated once forced with M-PSK (i.e upto 32-PSK) regulation. The channel estimation examine in these conditions.
Empirical Mode Decomposition Based Signal Analysis of Gear Fault Diagnosisrahulmonikasharma
A vibration investigation is about the specialty of searching for changes in the vibration example, and after that relating those progressions back to the machines mechanical outline. The level of vibration and the example of the vibration reveal to us something about the interior state of the turning segment. The vibration example can let us know whether the machine is out of adjust or twisted. Al-so blames with the moving components and coupling issues can be distinguished. This paper shows an approach for equip blame investigation utilizing signal handling plans. The information has been taken from college of ohio, joined states. The investigation has done utilizing MATLAB software.
This paper discusses a new algorithm of a univariate method, which is vitally important to develop a short-term load forecasting module for planning and operation of distribution system. It has many applications including purchasing of energy, generation and infrastructure development etc. We have discussed different time series forecasting approaches in this paper. But ARIMA has proved itself as the most appropriate method in forecasting of the load profile for West Bengal using the historical data of the year of 2017. Auto Regressive Integrated Moving Average model gives more accuracy level of load forecast than any other techniques. Mean Absolute Percentage Error (MAPE) has been calculated for the mentioned forecasted model.
Impact of Coupling Coefficient on Coupled Line Couplerrahulmonikasharma
The coupled line coupler is a type of directional coupler which finds practical utility. It is mainly used for sampling the microwave power. In this paper, 3 couplers A,B & C are designed with different values of coupling coefficient 6dB,10dB & 18dB respectively at a frequency of 2.5GHz using ADS tool. The return loss, isolation loss & transmission loss are determined. The design & simulation is done using microstrip line technology.
Design Evaluation and Temperature Rise Test of Flameproof Induction Motorrahulmonikasharma
The ignition of flammable gases, vapours or dust in presence of oxygen contained in the surrounding atmosphere may lead to explosion. Flameproof three phase induction motors are the most common and frequently used in the process industries such as oil refineries, oil rigs, petrochemicals, fertilizers, etc. The design of flameproof motor is such that it allows and sustain explosion within the enclosure caused by ignition of hazardous gases without transmitting it to the external flammable atmosphere. The enclosure is mechanically strong enough to withstand the explosion pressure developed inside it. To prevent an explosion due to hot spot on the surface of the motor, flameproof induction motors are subjected to heat run test to determine the maximum surface temperature and temperature class with respect to the ignition temperature of the surrounding flammable gas atmosphere. This paper highlights the design features of flameproof motors and their surface temperature classification for different sizes.
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Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/
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HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
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
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
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
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneous Clouds
1. International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 7 93 – 97
_______________________________________________________________________________________________
93
IJRITCC | July 2017, Available @ http://www.ijritcc.org
_______________________________________________________________________________________
Challenges in Dynamic Resource Allocation and Task Scheduling in
Heterogeneous Clouds
Ms. Ragini Karwayun
Researcher
Mewar University
Rajasthan
raginik22 @gmail.com
Dr. H. S. Sharma
Pro Vice Chancellor
Mewar University
Rajasthan
profhssharma@yahoo.com
Dr. K. P. Yadav
Director
IIMT
Greater Noida
karunesh732@gmail.com
Abstract— Resource Allocation and Task scheduling are the most important key words in today’s dynamic cloud based
applications. Task scheduling involves assigning tasks to available processors with the aim of producing minimum execution time,
whereas resource allocation involves deciding on an allocation policy to allocate resources to various tasks so as to have
maximum resource utilization. Algorithms used for scheduling resources for virtual machines are designed for both homogeneous
and heterogeneous environments. Majority of the algorithms focus on processing ability often neglecting other features such as
network bandwidth and actual resource requirements. One of the major pitfalls in cloud computing is related to optimizing the
resources being allocated. Because of the uniqueness of the model, resource allocation is performed with the objective of
minimizing the costs associated with it. The other challenges of resource allocation are meeting customer demands and application
requirements. In this paper we will focus on the challenges faced in task scheduling and resource allocation in dynamic
heterogeneous clouds.
Keywords-Resource Allocation, Task Scheduling, distributed Heterogeneous Clouds
__________________________________________________*****_________________________________________________
I. INTRODUCTION
Internet based environment of cloud computing framework
provides opportunities for provisioning, consumption and
delivery of services such as information, software and
computing resources. In today’s world there are numerous
vendors that rent computing resources on-demand, bill on a
pay-as-you-go basis, and multiplex many users on the same
physical infrastructure. These cloud computing environments
provide an illusion of infinite computing resources to cloud
users so that they can increase or decrease their resource
consumption rate according to the demands.
In cloud computing environments, the two important
stakeholders: cloud providers and cloud users have different set
of goals and objectives often conflicting with each other. There
are providers who hold massive computing resources in their
large datacenters and rent resources out to users on a per-usage
basis. Then there are users who have applications with
fluctuating loads and lease resources from providers to run their
applications. Providers on one hand wish to maximize revenues
by achieving high resource utilization, while users wish to have
minimum expenses while meeting their performance
requirements[6].
The two aspects of cloud computing applications are based
on tradeoff between various characteristics depending on the
domain and for providing the service by the cloud service
provider and using the services by the cloud user, an initial
agreement called the Service Level Agreement (SLA) has to be
made between the cloud users and the cloud service provider.
[2] While resource allocation is made to the cloud user, SLA
violation should be avoided as much as possible or SLA
violation should be minimal without compromising Quality of
Service (QoS) parameters like performance, availability,
response time, security, reliability, throughput etc.
Performance: For some application demands, performance is
an important criteria. The system should perform well to
provide service to the cloud user.
Response Time: For interactive applications, response time is
an important factor. The system must respond well for these
kinds of applications.
Reliability: The system used should be reliable so that the
cloud user has no head ache of changing the system.
Availability: Whenever cloud resources are requested the
cloud service provider must be able to allocate within short
span.
Security: For critical applications like online transaction
applications, system us ed has to be secure. Otherwise it is not
safe to use such a kind of system.
Throughput: No. of applications run per unit time should be
more.
II. CHALLENGES IN RESOURCE ALLOCATION IN CLOUDS
In cloud computing, Resource Allocation is the process of
assigning available resources to the needed cloud applications
over the internet. Cloud services are starved if the resource
allocation is not managed properly. Resource provisioning
solves that problem by allowing the service providers to
properly manage the resources for each individual module.
Resource Allocation Strategy is all about integrating cloud
provider activities for utilizing and allocating available
resources within the limit of cloud environment so as to meet
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the needs of the cloud application. It requires the type and
amount of resources needed by each application in order to
complete a user job. The order and time of allocation of
resources are also an input for an optimal strategy. An optimal
Resource allocation scheme should avoid the following criteria
as follows:
Resource contention situation arises when two applications try
to access the same resource at the same time.
Scarcity of resources arises when there are limited resources.
Resource fragmentation situation arises when the resources
are isolated. [There will be enough resources but not able to
allocate to the needed application.]
Over-provisioning of resources arises when the application
gets surplus resources than the demanded one.
Under-provisioning of resources occurs when the application
is assigned with fewer numbers of resources than the demand.
Cloud Users
Interfaces
Applications
Interfaces
Virtualized Resources
Cloud Providers
Cloud users estimation of resources demands to complete a job
before the estimated time may lead to an over-provisioning of
resources. Cloud providers’ allocation of resources may lead to
an under-provisioning of resources. From the cloud user’s
angle, the application requirement and Service Level
Agreement (SLA) are major inputs to allocation algorithms.
The offerings, resource status and available resources are the
inputs required from the provider’s side to manage and allocate
resources to host applications [1]. The outcome of any optimal
Resource allocation scheme must satisfy the QoS parameters
specified in SLA. Cloud providers while providing reliable
resources, faces a crucial problem in allocating and managing
resources dynamically across the applications.
Resource Allocation algorithms are available in two
categories – static and dynamic.
Static Allocation: The cloud user declares the required
resources beforehand. By doing so user is aware of the details
of required resources – types of resources and how many
instances of each type of resources are needed ahead of using
the system. But the drawback in this case is it leads to
underutilization or overutilization of resources depending on
the time the application is run. The striking feature of these
types of algorithms is that they use mostly fair allocation
strategies.
Dynamic Allocation: Cloud resources are not declared prior
to starting the application, but are requested by the cloud user
on the run or as and when the application needs. These
algorithms provide best utilization of resources avoiding
underutilization and overutilization of resources, as much as
possible. But nearly all dynamic resource allocation policies are
unfair. The requested resources might not be available when
requested on the fly. The service provider has to make
allocation from other participating cloud data center.
In most cases, the interaction between providers and users is
as follows.
1. A user sends a request for resources to a provider.
2. When the provider receives the request, it looks for
resources to satisfy the request and assigns the
resources to the requesting user, typically as a form of
virtual machines(VMs).
3. Then the user uses the assigned resources to run
applications and pays for the resources that are used.
4. When the user is done with the resources, they are
returned to the provider.
The most interesting aspect of the cloud computing
environment is that the major players in the field are often
different parties with their own interests. Typically, the goal of
providers is to generate as much revenue as possible with
minimum investment[6]. To achieve this they might want to
squash their computing resources; for example, by hosting as
many VMs as possible on each machine, thus wishing to
maximize resource utilization. However, placing too many
VMs on a single machine will cause VMs to interfere with
each other, resulting in degraded and/or unpredictable
performance which, in turn, affects the users. Thus, the
providers may evict existing VMs or reject resource requests
to maintain service quality, but it could make the environment
even more unpredictable. On the other hand, users want their
jobs done at minimal expense or, in other words, they wish to
have maximum performance at minimal cost. This involves
having proper resources that suit the workload requirements of
users' applications and utilize resources effectively. They also
have to take unpredictable resources into account when they
request resources and schedule applications.
Resource allocation in distributed heterogeneous clouds in
dynamic computing environment focuses on four fundamental
issues :
1. Resource modelling.
2. Resource allocation and treatment.
3. Resource offering and treatment.
4. Resource discovery and monitoring selection.
Resource allocation algorithms must have the complete
knowledge of the status of each resource in the computing
environment and use this knowledge to apply it intelligently to
allocate physical or virtual resources to applications as per
their requirements. [1] So the information that is required by
the resource allocation algorithms can be summed up as: cloud
resources, resource modelling, application requirements and
specifications and providers requirements.
Resource Modelling : Infinite computing resources are
available to be used on demand by the cloud users. The cloud
environment thru the use of resource allocation algorithms
tries to meet the demands of the users in a dynamic way,
allowing the multiplexing of physical resources, avoiding both
under provisioning and over provisioning of resources.
Moreover as it is a heterogeneous environment it is essential to
develop a proper resource model. All optimized allocation
algorithms are very strongly dependent on the resource
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modelling scheme used. Computing and network resources
can be represented by using RDF (Resource Description
Framework) and NDL ( Network Description Language). It
must be kept in mind that these tools must be capable of
representing resources, networks and applications virtually in
terms of properties and functionalities[7].
Another important point of consideration is the granularity of
resource description. More the details used in describing the
resources better will be the allocation being more flexible in
usage of resources , on the other hand more details will make
the resource selection and allocation module complex and
harder to handle.
One additional point of challenge in resource allocation in
heterogeneous environment is the interoperability in clouds
inorder to provide seamless flow of data between clouds,
across clouds and their local applications. Layered
architectural design and use of open APIs are practical options
for interoperability.
There are two types of heterogeneities faced by
interoperability in clouds [8]. Vertical is intra-cloud whereas
Horizontal is inter-cloud( different vendors) heterogeneities.
Vertical heterogeneity can be addressed with the help of
middleware and standardization. OVF (Open Virtualization
Format) can be used for managing virtual machines across the
system. Horizontal heterogeneity, as clouds from different
vendors are involved, is very difficult to obtain. As each
vendor may use different level of abstraction to represent the
resources, it is challenging to define a method of interaction
between clouds. This issue can be addressed with the help of
high level of granularity in the modelling but has a serious
drawback of losing information. But it is advantageous to have
horizontal interoperability as there are numerous instances
when a user’s request cannot be addressed by a cloud provider
due to unavailability of resources locally. In such cases, the
provider can borrow resources from another vendor by
dynamically negotiating.
Resource Offering and Treatment: After modelling of
resources, the providers offer interfaces (middleware) that on
one hand handles resources at a lower end and on the other
hand deal with the requirements of the applications at a higher
level. It is important to emphasize on the fact that there should
be a clear level of abstraction between the resource modelling
and the way they are offered to the end user. For example, a
provider can model the resources independently such as speed
of processor or size of memory but offer them as a collection
of items , such as a VM.
Since it is important to have a generic solution for clouds so
that they may support as many applications as possible,
resource offering becomes a very complex process. Providers
must handle major issues like – how to have a good trade off
between the granularity of resource modelling and an easy
generic solution, and how can one define the generic level of
the cloud?
The Resource Allocation Scheme must ensure that all
requirements must be met with the set of available resources
as much as possible. Moreover the requirements of the cloud
users must be specified properly in the SLA and it is essential
that both parties must adhere to it. The list of the requirements
specified in the SLA may include but not limited to the
following:
i. Network bandwidth requirement.
ii. Computational capability (processor speed and
memory) requirement.
iii. Topology of the nodes which help the cloud users to
define inter-node relationships and restrictions on
uplink and downlink communications.
iv. Jurisdiction of physical location and methods of
handling data by applications. Location of data may
be restricted within a country or a continent by
copyright laws.
v. The node proximity is another requirement which
may be seen as a constraint imposed on the physical
distance or delay value (maximum or minimum)
between nodes.
vi. The application interaction defines the types of
configuration applications will use to exchange
information with each other.
vii. The cloud user should also be able to define
scalability rules which gives the specifications for the
growth of applications and how will and how many
resources will they consume.
Authors in [9] describes the methods how the VMs will be
deployed by the cloud users based on the thresholds of the
above specified metrics.
Resource Discovery and Monitoring: Providers need to find
appropriate resources that comply with the user’s request. The
most daunting task is to fine suitable resources within the
boundaries (physical/geographical) in a heterogeneous cloud.
In addition to this the costing factor of inter domain traffic also
need to be take care of.
In [8] a very simple implementation of the resource discovery
service is described for the network virtualization scenario that
uses a discovery framework with an advertisement process.
Cloud users (brokers) use it to discover and match available
resources from different providers. It consists of distributed
repositories responsible for storing resource descriptions and
states. Considering that one of the key features of cloud
computing is its capability of acquiring and releasing
resources on demand [10], resource monitoring should be
continuous, and should help with allocation and reallocation
decisions as part of overall resource usage optimization. It
should be carefully analyzed to find an acceptable trade-off
between the amount of control overhead and the frequency of
resource information refreshing.
The resource monitoring may be passive or active. It is passive
when one or more entities collect information by continuously
sending polling messages to nodes asking for information or
do this on demand when necessary. On the other hand, the
monitoring is active when nodes are autonomous and may
decide when to send asynchronously state information to some
central entity. Both the alternatives can be simultaneously be
used to improve the monitoring solution. To achieve this it is
necessary to synchronize updates in repositories to maintain
consistency and validity of state information.
Resource Selection and Optimization: Once the information
regarding cloud resource availability is collected, a set of
appropriate candidates may be highlighted. In the next phase
the resource selection process needs to find a configuration
that fulfils all requirements and optimizes the usage of the
infrastructure. In virtual networks, for example, the resource
selection mechanisms main objective is to find the best
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mapping of the virtual networks on the substrate network with
respect to the constraints [11]. Selection of a suitable solution
from a set is a very complex task due to the dynamically
changing environment , very complex algorithm, and all the
other different requirements relevant to the provider.
Optimization algorithms may be used for resource selection.
Optimization strategies used may vary , from simple and well-
known techniques such as simple heuristics with thresholds or
linear programming to newer, more complex ones, such as
Lyapunov optimization [12]. In addition, artificial intelligence
algorithms, biologically inspired ones (e.g., ant colony
behaviour), and game theory may also be applied in this
scenario. Authors in [13] define a system called Volley to
automatically migrate data across geo-distributed datacentres.
This solution uses an iterative optimization algorithm based on
weighted spherical means [13]. Resource selection strategies
fall into a priori and a posterior classes. In the a priori case,
the first allocation solution is an optimal one. To achieve this
goal, the strategy should consider all variables influencing the
allocation. For example, considering VM instances being
allocated, the optimization strategy should figure out the
problem, presenting a solution (or a set of possibilities) that
satisfies all constraints and meets the goals (e.g., minimization
of reallocations) in an optimal manner. In an a posteriori case,
once an initial allocation that can be a suboptimal solution is
made, the provider should manage its resources in a
continuous way in order to improve this solution. If necessary,
decisions such as to add or reallocate resources should be
made in order to optimize the system utilization or comply
with cloud users’ requirements. Since resource utilization and
provisioning are dynamic and changing all the time, it is
important that any a posteriori optimization strategy quickly
reach an optimal allocation level, as a result of a few
configuration trials. Furthermore, it should also be able to
optimize the old ones, readjusting them according to new
demand. In this case, the optimization strategy may also fit
with the definition of a priori and dynamic classification
Thus we have described the four challenges that should be
faced for defining any resource allocation scheme. The first
two challenges need to be taken care of in conception phase
and the next two in operational phase. When designing a
distributed heterogeneous cloud the provider should choose
the nature of its offering such as service, infrastructure and
platform as a service (SaaS, IaaS and PaaS).
III. CHALLENGES IN TASK SCHEDULING IN CLOUDS
The main objective of task scheduling is to schedule the
arriving tasks onto available processors with the aim of
producing minimum schedule length and without violating the
precedence constraints. It is often seen that the different
sections of the application task need different types of
computations. So for single machine architecture it is
impossible to satisfy all the computational requirements of
such applications. So it is essential to schedule different tasks
of such applications to different suitable processors across the
distributed heterogeneous computing resources. This system
helps in executing computationally intensive applications with
different computational requirements. But the performance of
such a system is highly dependent on the scheduling algorithm
used.
We assume that the physical locations of VMs are determined
by the provider. It is assumed that, the responsibility to
determine when to request how many resources of which type,
and how to schedule the user's application on the allocated
resources, lies with the user[6].
In a heterogeneous environment, it is important to schedule a
job on its preferred resources to achieve high performance. In
addition, it is not straightforward to provide fairness among
jobs when there are multiple jobs. Moreover, the capacity of
different machine types needs to be considered when a user
makes a resource request.
Task Scheduling algorithms are static as well as dynamic in
nature. As static algorithms are more fair, stable and easy to
use, they are more commonly used. Hadoop also uses static
algorithm for task scheduling [3].
Performance metrics for task scheduling algorithms are
schedule length, speedup, efficiency and time complexity :
Schedule length : is the maximum finish time of the exit task
in scheduled DAG ( Directed Acyclic Graph).
Speedup: of a schedule is defined as the ratio of the schedule
length obtained by assigning all tasks to the fastest processor,
to the scheduled length of application.
Efficiency: is the speedup divided by the number of
processors used. It is the measure of the effective percentage
of processor time is doing useful computation.
Time complexity: is the amount of time taken to assign every
task to specific processor according to specific priority.
Once a set of resources such as virtual machines are allocated
by the cloud driver ,the analytics engine uses the resources that
are heterogeneous and shared among multiple jobs. In this
section, we consider challenges in job scheduling on a shared,
heterogeneous cluster, to provide good performance while
guaranteeing fairness.
Share and Fairness
In a shared cluster, providing fairness is one of the most
important features that the analytics engine should support.
There are different ways to define fairness, but one method
might be having each job receive equal (or weighted) share of
computing resources at any given moment. In that sense,
Hadoop Fair Scheduler [4] takes the number of slots assigned
to a job as a metric of share, and fairness is provided by
assigning same number of slots to each job.
In a heterogeneous cluster, as all the slots are different, the
number of slots might not be an appropriate metric of the
share. Moreover, even on the same slot, the computation speed
varies depending on jobs. The performance variance on
different resources is also important to consider to improve
overall performance of the data analytics cluster; if jobs are
assigned to slots irrespective of their preferences will not only
make the job run slow, but also may prevent other jobs that
prefer the slot from utilizing it.
Progress Share
Progress share refers to how much progress each job is making
with assigned resources (or slots in Hadoop) compared to the
case of running the job on the entire cluster without sharing;
therefore, it is between 0 (no progress at all) and 1 (all
available resources are occupied).
Scheduler
To calculate the Progress share of each job, the analytics
engine scheduler should be aware of the per-slot computing
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rate(CR). To that end, each job should go through two phases:
calibration and normal. Calibration phase starts when a task is
submitted. In this phase, by measuring the completion time of
these tasks, the scheduler can determine the CR. Once the
scheduler knows the CR, the task enters the normal phase.
During this phase, when a slot becomes available, a job of
which the share is less than its minimum or fair share is
selected. However, if there is another job with a significantly
higher computing rate on the slot, the scheduler chooses that
job to improve overall performance. This is similar to the
―delay scheduling‖ [14] mechanism in Hadoop fair scheduler.
By using progress share, the scheduler can make an
appropriate decision. As a result, the cluster is better utilized.
In addition, each job receives a fair amount of computing
resources.
IV. CONCLUSION
The cloud environment provides heterogeneous hardware and
resource demands; therefore, it is important to exploit these
features to make a data analytics cluster in the cloud efficient.
In this paper, we presented the issues concerning the cloud
users and providers to allocate resources in a cost-effective
manner, and discussed a scheduling issues that should be
taken care of to provide good performance and fairness
simultaneously in heterogeneous cluster.
Beyond data analytics systems, many systems running in the
cloud involve multilevel scheduling. Resource allocation is
done at infrastructure level and job scheduling is performed at
application level. Mesos [15] takes this approach to provide
resource sharing and isolation across distributed applications.
It adopts Dominant Resource Fairness(DRF) [6] as an example
of resource allocation policy and leaves application level
scheduling to each application.
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