Green datacenters has become a major research area among researchers in academy and industry. One of the
recent approaches getting higher attention is supplying datacenters with renewable sources of energy, leading to
cleaner and more sustainable datacenters. However, this path poses new challenges. The main problem with
existing renewable energy technologies is high variability, which means high fluctuation of available energy
during different time periods on a day, month or year. In our paper, we address the issue of better managing
datacenter workload in order to achieve higher utilization of available renewable energy. We implement an
algorithm in CloudSim simulator which decides to postpone or urgently run a specific job asking for datacenter
resources, based on job’s deadline and available solar energy. The aim of this algorithm is to make workload
energy consumption through 24 hours match as much as possible the solar energy availability in 24 hours. Two
typical, clear and cloudy days, are taken in consideration for simulation. The results from our experiments show
that, for the chosen workload model, jobs are better managed by postponing or urgently running them, in terms
of leveraging available solar energy. This yields up to 17% higher utilization of daily solar energy
An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Ne...IJECEIAES
Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads.
A survey to harness an efficient energy in cloud computingijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
GMC: Greening MapReduce Clusters Considering both Computation Energy and Cool...Tarik Reza Toha
Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this paper, we propose a machine learning based approach named as Green MapReduce Cluster (GMC) that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. GMC predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of GMC over a real testbed. Our evaluation reveals that GMC reduces total energy consumption by up to 47% compared to other alternatives while experiencing marginal throughput degradation in a few cases.
Optimization of energy consumption in cloud computing datacenters IJECEIAES
Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
A survey on energy efficient with task consolidation in the virtualized cloud...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Ne...IJECEIAES
Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads.
A survey to harness an efficient energy in cloud computingijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
GMC: Greening MapReduce Clusters Considering both Computation Energy and Cool...Tarik Reza Toha
Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this paper, we propose a machine learning based approach named as Green MapReduce Cluster (GMC) that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. GMC predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of GMC over a real testbed. Our evaluation reveals that GMC reduces total energy consumption by up to 47% compared to other alternatives while experiencing marginal throughput degradation in a few cases.
Optimization of energy consumption in cloud computing datacenters IJECEIAES
Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
A survey on energy efficient with task consolidation in the virtualized cloud...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A survey on energy efficient with task consolidation in the virtualized cloud...eSAT Journals
Abstract Cloud computing is a new model of computing that is widely used in today’s industry, organizations and society in information technology service delivery as a utility. It enables organizations to reduce operational expenditure and capital expenditure. However, cloud computing with underutilized resources still consumes an unacceptable amount of energy than fully utilized resource. Many techniques for optimizing energy consumption in virtualized cloud have been proposed. This paper surveys different energy efficient models with task consolidation in the virtualized cloud computing environment. Keywords: Cloud computing, Virtualization, Task consolidation, Energy consumption, Virtual machine
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
BGPC: Energy-Efficient Parallel Computing Considering Both Computational and ...Tarik Reza Toha
Parallel computing has become popular now-a-days due to its computing efficiency and cost effectiveness. However, in parallel computing systems, the computing demands a set of machines instead of a single machine. Therefore, it consumes a significant amount of power compared to single-machine computing systems. Moreover, a noticeable amount of power is necessary for maintaining the optimum temperature in the working environment of the parallel systems. This power is generally known as the cooling power required for the systems.
Although several power saving parallel computing schemes have already been proposed in the literature to date in order to minimize computational power consumption of a parallel system, designing a scheme considering both computational and cooling power consumption with low-cost resource is yet to be investigated in the literature. Therefore, in this thesis, we propose a low-cost power saving scheme simultaneously considering both computational and cooling power consumption. We design a machine learning framework BPGC, which tries to find the number of machines needed to be activated to be optimal, or at least near-optimal, in terms of minimum total energy consumption, with minimal overhead.
In order to predict total energy, we need to predict response time, computational power, and cooling power. We fit different machine learning algorithms for these predictions by using a year long collected training data. K-nearest neighbors, Support Vector Machine for regression, and Additive Regression using Random Forest show the highest accuracy for these predictions respectively. We implement BPGC framework in our test-bed with two green methods and static method. Our framework outperforms the green methods with a little degradation of QoS compared to the best QoS provider, that is, static method.
Harvesting aware energy management for time-critical wireless sensor networksIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine P...IJECEIAES
With the increasing expansion of cloud data centers and the demand for cloud services, one of the major problems facing these data centers is the “increasing growth in energy consumption ". In this paper, we propose a method to balance the burden of virtual machine resources in order to reduce energy consumption. The proposed technique is based on a four-adaptive threshold model to reduce energy consumption in physical servers and minimize SLA violation in cloud data centers. Based on the proposed technique, hosts will be grouped into five clusters: hosts with low load, hosts with a light load, hosts with a middle load, hosts with high load and finally, hosts with a heavy load. Virtual machines are transferred from the host with high load and heavy load to the hosts with light load. Also, the VMs on low hosts will be migrated to the hosts with middle load, while the host with a light load and hosts with middle load remain unchanged. The values of the thresholds are obtained on the basis of the mathematical modeling approach and the 퐾-Means Clustering Algorithm is used for clustering of hosts. Experimental results show that applying the proposed technique will improve the load balancing and reduce the number of VM migration and reduce energy consumption.
Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
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.
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.
A hybrid approach for scheduling applications in cloud computing environment IJECEIAES
Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list.
ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND R...IAEME Publication
Cloud Computing is an internet based computing which makes and different types of services available to users. For customers based on their required services over the internet virtualized resources are provided. The fast growth of cloud resources with customers demand increases the energy consumption results in carbon dioxide emission. However, energy consumption and carbon dioxide emission in cloud data centre have massive impact on global environment triggering intense research in this area. To minimize the energy consumption in this paper we propose VM Assignment scheduling algorithm, it is based energy consumption and balancing the resource utilization. we consider both the VM and host energy consumption and classify the VMs based the resource usage and schedule them to balance the resources utilization among the hosts in the cloud data centre which leads to better energy efficiency and reduces the heat generation. The effectiveness of the proposed technique has been verified by simulating on CloudSim. Experimental results confirm that the technique proposed here can significantly reduce energy consumption in cloud.
Cloud computing offers to users worldwide a low cost on-demand services, according to their requirements. In the recent years, the rapid growth and service quality of cloud computing has made it an attractive technology for different Tech Companies. However with the growing number of data centers resources, high levels of energy cost are being consumed with more carbon emissions in the air. For instance, the Google data center estimation of electric power consumption is equivalent to the energy requirement of a small sized city. Also, even if the virtualization of resources in cloud computing datacenters may reduce the number of physical machines and hardware equipments cost, it is still restrained by energy consumption issue. Energy efficiency has become a major concern for today’s cloud datacenter researchers, with a simultaneous improvement of the cloud service quality and reducing operation cost. This paper analyses and discusses the literature review of works related to the contribution of energy efficiency enhancement in cloud computing datacenters. The main objective is to have the best management of the involved physical machines which host the virtual ones in the cloud datacenters.
Energy efficient utilization of data center resources can be carried out by optimization of the resources allocated in virtual machine placement through live migration. This paper proposes a method to optimize virtual machine placement in Banker algorithm for energy efficient cloud computing to tackle the issue of load balancing for hotspot mitigation and proposed method is named as Optimized Virtual Machine Placement in Banker algorithm (OVMPBA). By determining the state of host overload through dynamic thresholds technique and minimization migration policy for VM selection from the overloaded host an attempt is made to efficiently utilize the available computing resources and thus minimize the energy consumption in the cloud environment. The above research work is experimentally simulated on CloudSim Simulator and the experimental result shows that proposed OVMPBA method provides better energy efficiency and lesser number of migrations against existing methods of host overload detection-virtual machine selection and therefore maximizes the cloud energy efficiency.
Spark for Behavioral Analytics Research: Spark Summit East talk by John W uSpark Summit
This presentation reports our experience on using the machine learning techniques in Apache Spark ecosystem to understand the user behavior in a number of applications. In this context, Spark makes the vast computing power of a large high-performance computing system available to the behavioral economists without requiring the application scientists to learn about parallel computing. To illustrate the effectiveness of this approach, we focus on a compute-intensive task of establishing baseline for studying the impact of policies on consumer behavior. The gold standard for this type of baseline is a randomized control group, however, this control group can only provide a group-level reference, not for individual consumers. In many cases, the self-selection bias along with other factors can make it extremely difficult to generate a unbiased control group. By harnessing the computing power of Spark, we are able to learn the behavior pattern for each individual user and therefore create a much more precise baseline for behavioral analysis. We will use two use cases to illustrate the approach: a residential electricity usage study and a traffic pattern prediction study.
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud
computing requires many tasks to be executed by the provided resources to achieve good performance,
shortest response time and high utilization of resources. To achieve these challenges there is a need to
develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on
reducing energy consumption
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud
computing requires many tasks to be executed by the provided resources to achieve good performance,
shortest response time and high utilization of resources. To achieve these challenges there is a need to
develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on
reducing energy consumption.
Achieving Energy Proportionality In Server ClustersCSCJournals
a great amount of interests in the past few years. Energy proportionality is a principal to ensure that energy consumption is proportional to the system workload. Energy proportional design can effectively improve energy efficiency of computing systems. In this paper, an energy proportional model is proposed based on queuing theory and service differentiation in server clusters, which can provide controllable and predictable quantitative control over power consumption with theoretically guaranteed service performance. Futher study for the transition overhead is carried out corresponding strategy is proposed to compensate the performance degradation caused by transition overhead. The model is evaluated via extensive simulations and is justified by the real workload data trace. The results show that our model can achieve satisfied service performance while still preserving energy efficiency in the system.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
Demand-driven Gaussian window optimization for executing preferred population...IJECEIAES
Scheduling is one of the essential enabling technique for Cloud computing which facilitates efficient resource utilization among the jobs scheduled for processing. However, it experiences performance overheads due to the inappropriate provisioning of resources to requesting jobs. It is very much essential that the performance of Cloud is accomplished through intelligent scheduling and allocation of resources. In this paper, we propose the application of Gaussian window where jobs of heterogeneous in nature are scheduled in the round-robin fashion on different Cloud clusters. The clusters are heterogeneous in nature having datacenters with varying sever capacity. Performance evaluation results show that the proposed algorithm has enhanced the QoS of the computing model. Allocation of Jobs to specific Clusters has improved the system throughput and has reduced the latency.
A survey on energy efficient with task consolidation in the virtualized cloud...eSAT Journals
Abstract Cloud computing is a new model of computing that is widely used in today’s industry, organizations and society in information technology service delivery as a utility. It enables organizations to reduce operational expenditure and capital expenditure. However, cloud computing with underutilized resources still consumes an unacceptable amount of energy than fully utilized resource. Many techniques for optimizing energy consumption in virtualized cloud have been proposed. This paper surveys different energy efficient models with task consolidation in the virtualized cloud computing environment. Keywords: Cloud computing, Virtualization, Task consolidation, Energy consumption, Virtual machine
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
BGPC: Energy-Efficient Parallel Computing Considering Both Computational and ...Tarik Reza Toha
Parallel computing has become popular now-a-days due to its computing efficiency and cost effectiveness. However, in parallel computing systems, the computing demands a set of machines instead of a single machine. Therefore, it consumes a significant amount of power compared to single-machine computing systems. Moreover, a noticeable amount of power is necessary for maintaining the optimum temperature in the working environment of the parallel systems. This power is generally known as the cooling power required for the systems.
Although several power saving parallel computing schemes have already been proposed in the literature to date in order to minimize computational power consumption of a parallel system, designing a scheme considering both computational and cooling power consumption with low-cost resource is yet to be investigated in the literature. Therefore, in this thesis, we propose a low-cost power saving scheme simultaneously considering both computational and cooling power consumption. We design a machine learning framework BPGC, which tries to find the number of machines needed to be activated to be optimal, or at least near-optimal, in terms of minimum total energy consumption, with minimal overhead.
In order to predict total energy, we need to predict response time, computational power, and cooling power. We fit different machine learning algorithms for these predictions by using a year long collected training data. K-nearest neighbors, Support Vector Machine for regression, and Additive Regression using Random Forest show the highest accuracy for these predictions respectively. We implement BPGC framework in our test-bed with two green methods and static method. Our framework outperforms the green methods with a little degradation of QoS compared to the best QoS provider, that is, static method.
Harvesting aware energy management for time-critical wireless sensor networksIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine P...IJECEIAES
With the increasing expansion of cloud data centers and the demand for cloud services, one of the major problems facing these data centers is the “increasing growth in energy consumption ". In this paper, we propose a method to balance the burden of virtual machine resources in order to reduce energy consumption. The proposed technique is based on a four-adaptive threshold model to reduce energy consumption in physical servers and minimize SLA violation in cloud data centers. Based on the proposed technique, hosts will be grouped into five clusters: hosts with low load, hosts with a light load, hosts with a middle load, hosts with high load and finally, hosts with a heavy load. Virtual machines are transferred from the host with high load and heavy load to the hosts with light load. Also, the VMs on low hosts will be migrated to the hosts with middle load, while the host with a light load and hosts with middle load remain unchanged. The values of the thresholds are obtained on the basis of the mathematical modeling approach and the 퐾-Means Clustering Algorithm is used for clustering of hosts. Experimental results show that applying the proposed technique will improve the load balancing and reduce the number of VM migration and reduce energy consumption.
Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
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Phone : +91 97518 00789 / +91 72999 51536
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.
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.
A hybrid approach for scheduling applications in cloud computing environment IJECEIAES
Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list.
ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND R...IAEME Publication
Cloud Computing is an internet based computing which makes and different types of services available to users. For customers based on their required services over the internet virtualized resources are provided. The fast growth of cloud resources with customers demand increases the energy consumption results in carbon dioxide emission. However, energy consumption and carbon dioxide emission in cloud data centre have massive impact on global environment triggering intense research in this area. To minimize the energy consumption in this paper we propose VM Assignment scheduling algorithm, it is based energy consumption and balancing the resource utilization. we consider both the VM and host energy consumption and classify the VMs based the resource usage and schedule them to balance the resources utilization among the hosts in the cloud data centre which leads to better energy efficiency and reduces the heat generation. The effectiveness of the proposed technique has been verified by simulating on CloudSim. Experimental results confirm that the technique proposed here can significantly reduce energy consumption in cloud.
Cloud computing offers to users worldwide a low cost on-demand services, according to their requirements. In the recent years, the rapid growth and service quality of cloud computing has made it an attractive technology for different Tech Companies. However with the growing number of data centers resources, high levels of energy cost are being consumed with more carbon emissions in the air. For instance, the Google data center estimation of electric power consumption is equivalent to the energy requirement of a small sized city. Also, even if the virtualization of resources in cloud computing datacenters may reduce the number of physical machines and hardware equipments cost, it is still restrained by energy consumption issue. Energy efficiency has become a major concern for today’s cloud datacenter researchers, with a simultaneous improvement of the cloud service quality and reducing operation cost. This paper analyses and discusses the literature review of works related to the contribution of energy efficiency enhancement in cloud computing datacenters. The main objective is to have the best management of the involved physical machines which host the virtual ones in the cloud datacenters.
Energy efficient utilization of data center resources can be carried out by optimization of the resources allocated in virtual machine placement through live migration. This paper proposes a method to optimize virtual machine placement in Banker algorithm for energy efficient cloud computing to tackle the issue of load balancing for hotspot mitigation and proposed method is named as Optimized Virtual Machine Placement in Banker algorithm (OVMPBA). By determining the state of host overload through dynamic thresholds technique and minimization migration policy for VM selection from the overloaded host an attempt is made to efficiently utilize the available computing resources and thus minimize the energy consumption in the cloud environment. The above research work is experimentally simulated on CloudSim Simulator and the experimental result shows that proposed OVMPBA method provides better energy efficiency and lesser number of migrations against existing methods of host overload detection-virtual machine selection and therefore maximizes the cloud energy efficiency.
Spark for Behavioral Analytics Research: Spark Summit East talk by John W uSpark Summit
This presentation reports our experience on using the machine learning techniques in Apache Spark ecosystem to understand the user behavior in a number of applications. In this context, Spark makes the vast computing power of a large high-performance computing system available to the behavioral economists without requiring the application scientists to learn about parallel computing. To illustrate the effectiveness of this approach, we focus on a compute-intensive task of establishing baseline for studying the impact of policies on consumer behavior. The gold standard for this type of baseline is a randomized control group, however, this control group can only provide a group-level reference, not for individual consumers. In many cases, the self-selection bias along with other factors can make it extremely difficult to generate a unbiased control group. By harnessing the computing power of Spark, we are able to learn the behavior pattern for each individual user and therefore create a much more precise baseline for behavioral analysis. We will use two use cases to illustrate the approach: a residential electricity usage study and a traffic pattern prediction study.
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud
computing requires many tasks to be executed by the provided resources to achieve good performance,
shortest response time and high utilization of resources. To achieve these challenges there is a need to
develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on
reducing energy consumption
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud
computing requires many tasks to be executed by the provided resources to achieve good performance,
shortest response time and high utilization of resources. To achieve these challenges there is a need to
develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on
reducing energy consumption.
Achieving Energy Proportionality In Server ClustersCSCJournals
a great amount of interests in the past few years. Energy proportionality is a principal to ensure that energy consumption is proportional to the system workload. Energy proportional design can effectively improve energy efficiency of computing systems. In this paper, an energy proportional model is proposed based on queuing theory and service differentiation in server clusters, which can provide controllable and predictable quantitative control over power consumption with theoretically guaranteed service performance. Futher study for the transition overhead is carried out corresponding strategy is proposed to compensate the performance degradation caused by transition overhead. The model is evaluated via extensive simulations and is justified by the real workload data trace. The results show that our model can achieve satisfied service performance while still preserving energy efficiency in the system.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
Demand-driven Gaussian window optimization for executing preferred population...IJECEIAES
Scheduling is one of the essential enabling technique for Cloud computing which facilitates efficient resource utilization among the jobs scheduled for processing. However, it experiences performance overheads due to the inappropriate provisioning of resources to requesting jobs. It is very much essential that the performance of Cloud is accomplished through intelligent scheduling and allocation of resources. In this paper, we propose the application of Gaussian window where jobs of heterogeneous in nature are scheduled in the round-robin fashion on different Cloud clusters. The clusters are heterogeneous in nature having datacenters with varying sever capacity. Performance evaluation results show that the proposed algorithm has enhanced the QoS of the computing model. Allocation of Jobs to specific Clusters has improved the system throughput and has reduced the latency.
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.
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.
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.
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...ijccsa
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
KEYWORDS
Energy Consumption, Virtual Machine Placement, Harmony Search Algorithm, Server Consolidati
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...neirew J
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
Enhancing hybrid renewable energy performance through deep Q-learning networ...IJECEIAES
In a stand-alone system, the use of renewable energies, load changes, and interruptions to transmission lines can cause voltage drops, impacting its reliability. A way to offset a change in the nature of hybrid renewable energy immediately is to utilize energy storage without needing to turn on other plants. Photovoltaic panels, a wind turbine, and a wallbox unit (responsible for providing the vehicle’s electrical need) are the components of the proposed system; in addition to being a power source, batteries also serve as a storage unit. Taking advantage of deep learning, particularly convolutional neural networks, and this new system will take advantage of recent advances in machine learning. By employing algorithms for deep Q-learning, the agent learns from the data of the various elements of the system to create the optimal policy for enhancing performance. To increase the learning efficiency, the reward function is implemented using a fuzzy Mamdani system. Our proposed experimental results shows that the new system with fuzzy reward using deep Q-learning networks (DQN) keeps the battery and the wallbox unit optimally charged and less discharged. Moreover confirms the economic advantages of the proposed approach performs better approximate to +25% Moreover, it has dynamic response capabilities and is more efficient over the existing optimization approach using deep learning without fuzzy logic.
An optimized cost-based data allocation model for heterogeneous distributed ...IJECEIAES
Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation.
ENERGY-AWARE DISK STORAGE MANAGEMENT: ONLINE APPROACH WITH APPLICATION IN DBMSijdms
Energy consumption has become a first-class optimization goal in design and implementation of dataintensive
computing systems. This is particularly true in the design of database management systems
(DBMS), which was found to be the major consumer of energy in the software stack of modern data
centers. Among all database components, the storage system is one of the most power-hungry elements. In
previous work, dynamic power management (DPM) techniques that make real-time decisions to transition
the disks to low-power modes are normally used to save energy in storage systems. In this paper, we tackle
the limitations of DPM proposals in previous contributions. We introduced a DPM optimization model
integrated with model predictive control (MPC) strategy to minimize power consumption of the disk-based
storage system while satisfying given performance requirements. It dynamically determines the state of
disks and plans for inter-disk data fragment migration to achieve desirable balance between power
consumption and query response time. Via analyzing our optimization model to identify structural
properties of optimal solutions, we propose a fast-solution heuristic DPM algorithm that can be integrated
in large-scale disk storage systems for efficient state configuration and data migration. We evaluate our
proposed ideas by running simulations using extensive set of synthetic workloads based on popular TPC
benchmarks. Our results show that our solution significantly outperforms the best existing algorithm in
both energy savings and response time.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
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
AN INTEGER-LINEAR ALGORITHM FOR OPTIMIZING ENERGY EFFICIENCY IN DATA CENTERSijfcstjournal
Nowadays, to meet the enormous computational requests, energy consumption, the largest part which is
related to idle resources, is strictly increased as a great part of a data center's budget. So, minimizing
energy consumption is one of the most important issues in the field of green computing. In this paper, we
present a mathematical model formed as integer-linear programming which minimizes energy consumption
and maximizes user’s satisfaction, simultaneously. However, migration variables, as principal decision
variables of the model, can be relaxed to continuous activities in some practical problems. This constraint
relaxation helps a decision maker to find faster solutions that are usually good approximations for
optimum. Near feasible solutions (infeasible solutions that are desirably close to the feasible region) have
been investigated as another relaxation considering the kind of solutions. For this purpose, we initially
present a measure to evaluate the amount of infeasibility of solutions and then let the model consider an
extended region including solutions with remissible infeasibility, if necessary.
An energy optimization with improved QOS approach for adaptive cloud resources IJECEIAES
In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (퐴퐶푅푅) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model 퐴퐶푅푅 in terms of average run time, power consumption and average power required than any other state-of-art techniques.
Energy harvesting earliest deadline first scheduling algorithm for increasing...IJECEIAES
In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life. Energy is a parameter by which the lifetime of system can be enhanced. To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.
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Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Utilization
1. Enida Sheme, Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 8, (Part - 3) August 2016, pp.23-28
www.ijera.com 23 | P a g e
Implementing Workload Postponing In Cloudsim to Maximize
Renewable Energy Utilization
Enida Sheme*, Neki Frashëri*
Information Technology Faculty, Polytechnic University of Tirana, Sheshi “Nënë Tereza”, Nr.4, Tirana,
Albania
ABSTRACT
Green datacenters has become a major research area among researchers in academy and industry. One of the
recent approaches getting higher attention is supplying datacenters with renewable sources of energy, leading to
cleaner and more sustainable datacenters. However, this path poses new challenges. The main problem with
existing renewable energy technologies is high variability, which means high fluctuation of available energy
during different time periods on a day, month or year. In our paper, we address the issue of better managing
datacenter workload in order to achieve higher utilization of available renewable energy. We implement an
algorithm in CloudSim simulator which decides to postpone or urgently run a specific job asking for datacenter
resources, based on job’s deadline and available solar energy. The aim of this algorithm is to make workload
energy consumption through 24 hours match as much as possible the solar energy availability in 24 hours. Two
typical, clear and cloudy days, are taken in consideration for simulation. The results from our experiments show
that, for the chosen workload model, jobs are better managed by postponing or urgently running them, in terms
of leveraging available solar energy. This yields up to 17% higher utilization of daily solar energy.
Keywords: green datacenters, energy consumption, renewable energy, solar energy, simulator
I. INTRODUCTION
Today’s green datacenters mean energy
efficient, sustainable and eco-friendly system. With
the advances of renewable energy technology, wind
and solar energy sources are rapidly becoming clean
substitutes of fuel based sources of energy. Many
giants of datacenters such as Google, Amazon,
Facebook, HP, etc have begun to supply their
datacenters with renewable energy and they aim to
achieve 100% coverage in the near future [1], [2].
However, because of their intermittency and
variations through periods of time, the integration of
renewable energy resources into datacenter is very
challenging. Usage of energy storage is usually
costly and not eco-friendly.
In this paper, we prove that workload
flexibility in Cloud Computing data centers can offer
a unique opportunity to tackle the challenges in
integrating renewable energy resources. This would
better make use of the available renewable energy
and also lower the datacenter’s carbon footprint. To
achieve these objectives, we propose and implement
an algorithm which decides to postpone or urgently
run a specific job asking for datacenter resources,
based on job’s deadline and available solar energy.
After implementing it in a well known simulator
called CloudSim, we run experiments to show that,
by better managing the coming workload of a
datacenter, there is a great opportunity to integrate
renewable energy as supplier for datacenters.
Implementing the algorithm in CloudSim is the main
contribution of this paper, as no other study reports
to have done this before.
The paper is organized as follows. Section
2 presents related works of the field. At Section 3,
we describe datacenter, workload and renewable
energy characteristics used for our study. An
introduction to the simulator and the proposed
algorithm are also included. Section 4 presents the
experiments and results to finalize with conclusions
at Section 5.
II. RELATED WORK
There have been studies, mostly in the
latest decade, regarding renewable energy
integration as datacenter’s energy source. There are
basically two main categories of studies and research
in this field, in terms of:
Real implementation or simulator: the algorithm
is implemented in real environment of a
physical datacenter or it’s implemented and
tested in a simulator, where more scenarios are
possible.
1 single datacenter or geographically distributed
datacenters. If the input is one datacenter, the
goal is to manage the workload energy
consumption to better fit into the available
renewable energy. If a geographically
distributed datacenter is the target of
implementation, the aim is to migrate jobs
towards datacenters locations with higher level
of renewable energy availability.
RESEARCH ARTICLE OPEN ACCESS
2. Enida Sheme, Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 8, (Part - 3) August 2016, pp.23-28
www.ijera.com 24 | P a g e
[3] presents a novel cooperation scheme
between smart city and datacenters. This study
results from DC4Cities approach project. Its aim is
to reorganize workload in order to match the shape
of renewable energy supply curve and minimize
brown energy consumption, by predicting future
renewable energy availability. As a result, the
percentage of renewable energy consumed by the
data centre is increased up to 23.25% compared to
non-smart scheduler. T. Cioara et al. present at [4] a
flexible mechanism for shifting the DC’s energy
demand profile from time intervals with limited
renewable energy production to time intervals when
spikes of production are predicted. A daily action
plan is built one day ahead, to be corrected every
four hours, and at last a check is made every 15
minutes. The workload is considered as “real-time”
and “delay-tolerant”. For 24 hours of simulation, the
results show 12% renewable energy usage increase,
which is translated into 2845 kg of CO2 reduction.
While the mentioned works [3] and [4] are
implemented in simulators the authors have built in
their own, we have used a well-known simulator
called CloudSim, which still lacks this algorithm.
Also, the workload we are based on resembles
Google’s trace file, as described in Section 3.2.
In contrast to the above case, [5], [6] and
[7] represent studies where the scheduling algorithm
is implemented in a real physical infrastructure. A
real Datacenter of 16 servers is built from
researchers at Rutgers University. The authors
present at [5] GreenHadoop, a MapReduce
framework for a datacenter powered by a
photovoltaic solar array and the electrical grid as a
backup. It aims an increase of green energy
consumption and decrease of electricity cost in
comparison with Hadoop. The results show an
increase of green energy consumption by up to 31%
and decrease of electricity cost by up to 39%,
compared to Hadoop. Over the same Hardware, the
authors present at [6] and [7] GreenSwitch and
GreenSlot. GreenSlot is a parallel batch job
scheduler, which predicts the amount of solar energy
that will be available in the near future, and
schedules the workload to maximize the green
energy consumption while meeting the jobs’
deadlines. If grid energy must be used to avoid
deadline violations, the scheduler selects times when
it is cheap. Two types of workload, each of them
categorized into deferrable and non-deferrable, are
run for 24 hours of simulation time. GreenSwitch [7]
is a model-based approach for dynamically
scheduling the workload and selecting the source of
energy to supply the selected hardware platform
(called Parasol).
Studies like [8] and [9] address scheduling
jobs over geographically distributed Datacenters in
order to exploit maximum renewable energy
capacities. Zhang et al. propose GreenWare [8], a
novel middleware system based on an efficient
request dispatching algorithm. It aims to maximize
the percentage of renewable energy used to power a
network of distributed dc’s, within cost budget
constraints of the Internet service operator. Using a 2
months trace file representing Wikipedia web
requests workload, simulation is run over 4
distributed homogeneous Datacenters. As a result of
GreenWare, percentage of renewable usage
increased significantly, under cost limits. Likely,
authors at [9] achieve an increase of renewable
energy usage, considering best cooling conditions of
datacenters’ locations.
III. SYSTEM SETUP
In this paragraph, we will describe the main
factors that affect the datacenter system. First,
datacenter parameters and workload characteristics,
as configured for simulation, are outlined. Then, we
present renewable energy data belonging to Albania
weather conditions. Section 3.4 gives basic
knowledge about the simulator we used to
implement the algorithm and run the experiments
followed by implementation details of the workload
postpone algorithm. The algorithm is explained in
details at the fifth subsection.
3.1 Datacenter
Datacenter represents the processing entity
in our system. It runs the workload and consumes
energy, which we track during 24 hours of
simulations. Datacenter parameters are chosen based
on similar experimental studies in the field of energy
consumption in datacenters and typical datacenter
size in Albania.
To run the simulation we configured the
number of hosts equal to 100 and the number of
virtual machines running over hosts equal to 200.
This means, 2 virtual machines run for every host.
The host type is HP ProLiant ML110 G5, Xeon
3075, processing capacity 2660 MHz, 2 cores and
RAM of 4GB. TABLE 1 shows the detailed
parameters for the datacenter configuration in
CloudSim.
Table 1: Datacenter detailed parameters
Nr. CPU RAM MIPS Model
Host 100 2 cores 4 GB 2660 Xen
2660Mhz
VM 200 1 core 2 GB 2660
3.2 Workload
The workload chosen for the experiments
represent a synthetic reproduction of a Google
workload, scaled over our own simulating datacenter
parameters. A Google trace file was published in
2011, giving detailed information of 12.000 Google
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servers traffic over 29 days, processing various types
of applications. This workload data are studied in
order to know its characteristics. The main findings
of studies [10], [11] and [12] are used in our
workload in order to produce patterns that resemble
to the Google workload.
As such, we configure the following
workload parameters for our study: total number of
jobs, their length, deadline, resource requirements
and inter-arrival time. The chosen number of jobs is
400, where 200 of them are short, 150 are medium
and 50 are long. The length of short jobs varies from
5 to 7 minutes, medium jobs from 25 to 50 minutes
and long jobs from 100 to 300 minutes. The jobs
length is generated through Poisson distribution.
Deadline is another parameter we set, which is the
limit of time it can pass till the job is fully
completed. Based on bibliography, we categorize
jobs into three types of deadline: loose, medium and
urgent. 130 of short jobs have loose deadline, which
means they are too tolerant over postpone in a later
moment to be run, 50 of short jobs have medium
deadline and 20 are urgent. Out of 150 medium
length jobs, 100 have loose deadline and 50 have
medium deadline. Meanwhile, all long jobs have
loose deadline. Loose, medium and urgent deadline
is set in proportion to jobs length. Regarding
resource requirements, half of short jobs require an
average of 25% of CPU usage and other half
requires 50% of CPU. 50 out of 150 medium jobs
require 25% CPU and 100 of them need an average
of 50% CPU. While long jobs needs to use an
average of 80% CPU.
The inter-arrival time is set to every 5
minutes for short jobs, every 30 minutes for medium
length jobs and every hour.
3.3 Renewable Energy
In our study, we used solar energy to
represent renewable energy. A. Maraj present a
study regarding solar energy in Tirana [13]. We
acquire the solar energy data from the results of this
study. The parameters are provided from the
database built through the utilization of a data
collecting system, which is installed on behalf of the
Department of Energy, Faculty of Mechanical
Engineering, Polytechnic University of Tirana. Solar
power irradiance on a 45° tilted 1 m2 solar panel,
installed over the terrace of the central building of
this University, has been collected, providing data
for every hour of its daily operation. We consider
two days as a model: typical clear summer day and
typical cloudy summer day. The specific dates are
July 16, 2010 and July 26, 2010 respectively.
Further details on solar panel specifications and
results of the study are explained from A. Maraj’s
article [13]. Results of the available solar energy are
shown in Fig. 1 and Fig. 2. Horizontal axis shows
288 5-minutes intervals of one day and vertical axis
represents Energy produced in Wh.
Figure 1: Available solar energy during a typical
clear summer day in Albania, generated from 1m2
solar panel
Figure 2: Available solar energy during a cloudy
summer day in Albania, generated from 1m2
solar
panel
3.4 Cloudsim
CloudSim is an extensible simulation
toolkit that enables modeling and simulation of
Cloud computing systems and application
provisioning environments. The CloudSim toolkit
supports both system and behavior modeling of
Cloud system components such as data centers,
virtual machines (VMs) and resource provisioning
policies.
Its main functional entities include:
Hosts: physical machines where the jobs are to
be executed
Virtual machines: virtual entities running over
real physical entities
Cloudlets: representing the workload or the jobs
to be executed in the datacenter
Broker: a scheduler which allocates virtual
machines to hosts and cloudlets to virtual
machines.
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CloudSim is chosen as a simulator because
of its high rate in reviews of the energy efficiency in
datacenter field, 7 years among researchers and still
being widely used, open source code and a rich
forum of programmers and researchers.
The proposed algorithm is implemented in
CloudSim as a workload scheduling mechanism,
evaluating and changing accordingly the time when
the cloudlet is allocated to virtual machines. Further
details on the algorithm are given in the following
subsection.
3.5 Proposed Algorithm
The algorithm we propose for better
managing the workload in terms of higher
leveraging of available renewable energy is based in
the following steps.
1. New job arrives
2. Is it urgent?
3. If yes, send it to the broker to allocate virtual
machine to it, ready for running.
4. If not, go to step 5
5. Does the job arrive in a time when renewable is
increasing?
6. If yes go to step 7, if not go to step 12
7. The job is short?
8. If yes, postpone by its length t_new = (t_arrival
+ short_job_ length)
9. If not, is the job medium length?
10. If yes, postpone to the average time between
t_arrival and job’s deadline t_new = [t_arrival
+ (deadline – t_arrival)/2]
11. If not, postpone at its allowed maximum t_new
= (t_arrival + deadline – 1.2*long_job_length )
12. Job has arrived when renewable energy is
decreasing. Is the job short?
13. If yes, postpone it to maximum time allowed
t_new = [t_arrival + deadline – 1.1* short_job_
length]
14. If not, is it medium length job?
15. If yes, postpone to the average time between
t_arrival and job’s deadline
t_new = [t_arrival + (deadline – t_arrival)/2]
16. If not, then it’s long job, run immediately, send
to the broker so that it allocates virtual machine
as the job requires.
Basically, the code is divided in two
sections, testing if the available renewable energy is
increasing or decreasing. In each case, the behaviour
will be different. After testing the urgency of the
arrived job, the algorithm decides to run it if it’s
urgent or postpone if it’s not urgent. The amount of
time it will be postponed dpends on renewable
energy forecast for next period of time, equal to
length of the job. If it’s an increasing period, than
the job is postponed to the next time period, as long
as it doesn’t violate its desired quality of service.
Job’s deadline is the limiting factor for the postpone
process. Otherwise, if it’s a decreasing period, the
behaviour will be contrary to the previous one
described above. Short jobs will be postponed at
their maximum, as they require less processing
resources, while the long jobs are immediately run in
order to use the available solar energy, as step 16
shows.
IV. EXPERIMENTS AND RESULTS
In this section, we describe the experiments
which are run in the simulator, aiming to compare
datacenter’s energy consumption through 24 hours
between three scenarios. First, energy consumption
is measured as it’s offered by default from the
CloudSim simulator, without implementing the
proposed algorithm. In the second scenario,
datacenter energy consumption is estimated after
implementing the proposed algorithm for postponing
the workload in order to match the available solar
energy. A clear summer day weather data is chosen
for input solar energy in this scenario. The third
scenario, in contrast to scenario 2, uses a cloudy
summer day weather data as input for solar energy.
For the first scenario, the energy consumed
by the the datacenter through 24 hours is estimated
from CloudSim as a total of 125 kWh. Fig. 3
illustrates datacenter’s energy consumption
fluctuating as workload intensity and resource
requirements vary. Horizontal axis illustrates 24
hours, while the vertical axis illustrates the energy
consumption in Wh.
Figure 3: Scenario 1, energy consumption through
24 hours simulation, new algorithm not implemented
In order to achieve a case study of 75% of
datacenter energy need supplied by solar energy, it
means a total of 94kWh of solar energy is required
to be produced daily. For this reason, based on the
fact that 1 m2
solar panel produces 3.5kWh per day,
collected from weather data, we calculate that 27 m2
of solar panels are needed to be used to achieve 75%
of the configured datacenter’s energy consumption
with renewable energy. This is the input for
renewable energy during the simulations we run in
our experiments.
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Figure 4: Scenario 2, energy consumption through
24 hours simulation, new algorithm implemented,
clear day renewable energy as input
Figure 5: Scenario 3, energy consumption through
24 hours simulation, new algorithm implemented,
cloudy day renewable energy as input
As Fig. 4 and Fig. 5 illustrate, in both
scenarios, workload postponing provides energy
consumption which resembles to the renewable
energy availability curve. Comparing the results
between available and used solar energy, it is
calculated that our algorithm achieves up to 17%
higher utilization of produced solar energy.
V. CONCLUSIONS
In our paper we address the issue of
supplying datacenters with renewable energy
sources, which poses a new challenge because of its
high variability. Managing the workload, by urgently
running or postponing arrived jobs, is the main
direction we focus on. We propose an algorithm
aiming to match the energy consumption to the
available renewable energy, in order to maximize
renewable energy usage. We implemented the
algorithm in CloudSim simulator and run
experiments to estimate it. To do so, energy
consumption over time is compared in three
different scenarios: no postpone algorithm is
implemented and proposed algorithm is
implemented with two different solar energy inputs,
clear and cloudy summer days. The results of the
experiments show that workload management and
scheduling is a promising direction towards greener
datacenters. Comparing the results for the estimated
energy consumption, with and without the proposed
algorithm, it is clearly illustrated that the
mechanisms helps in higher utilization of renewable
energy, up to 17%.
The study is limited to specific type of
workload which is composed by set of jobs having
loose deadline. This allows jobs to be postponed in
order to maximally exploit available renewable
energy.
Another limitation of this study is time
period taken in consideration for study. We consider
only solar energy values during typical summer clear
and cloudy days. However, the proposed algorithm
must be further developed for adapting to dynamic
weather changes over a day, including more variable
solar energy during one day and considering winter
days also.
Another direction to be explored further
would be integrating other sources of energy as
suppliers for the datacenter i.e wind energy. That
would compensate the dropping of solar energy,
resulting in smoother total renewable energy.
ACKNOWLEDGEMENTS
This research was partially supported by
Cost Project, NESUS, 2016. Thankful to IRIT staff,
University Paul Sabatier, for their support and
valuable comments. Grateful to Ardit Meti for his
assistance.
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