The Experts below are selected from a list of 1767 Experts worldwide ranked by ideXlab platform
Maolin Tang - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (2) - A Penalty-Based Genetic Algorithm for the Migration Cost-Aware Virtual Machine Placement Problem in Cloud Data Centers
Neural Information Processing, 2020Co-Authors: T.k. Sarker, Maolin TangAbstract:In the past few years, the virtual machine VM placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used ini?źtoday's cloud data centers as they do not consider the Migration Cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the Migration Cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm GA for the VM placement problem that considers the Migration Cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.
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a penalty based genetic algorithm for the Migration Cost aware virtual machine placement problem in cloud data centers
International Conference on Neural Information Processing, 2015Co-Authors: T.k. Sarker, Maolin TangAbstract:In the past few years, the virtual machine VM placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used ini?źtoday's cloud data centers as they do not consider the Migration Cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the Migration Cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm GA for the VM placement problem that considers the Migration Cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.
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A penalty-based genetic algorithm for the Migration Cost-aware virtual machine placement problem in cloud data centers
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015Co-Authors: T.k. Sarker, Maolin TangAbstract:© Springer International Publishing Switzerland 2015. In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today’s cloud data centers as they do not consider the Migration Cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the Migration Cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the Migration Cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.
Fuyuki Ishikawa - One of the best experts on this subject based on the ideXlab platform.
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Energy and Migration Cost-Aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacenters
IEEE Transactions on Services Computing, 2019Co-Authors: Quanwang Wu, Fuyuki IshikawaAbstract:Energy efficiency has become one of the major concerns for today's cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live Migration technology that VM consolidation relies on is Costly in itself, and this Migration Cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited Migration Costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a Migration Cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.
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HPCC/CSS/ICESS - Heterogeneous Virtual Machine Consolidation Using an Improved Grouping Genetic Algorithm
2015 IEEE 17th International Conference on High Performance Computing and Communications 2015 IEEE 7th International Symposium on Cyberspace Safety an, 2015Co-Authors: Quanwang Wu, Fuyuki IshikawaAbstract:Virtual machine (VM) consolidation is a promising approach for improving energy efficiency of the datacenter by increasing the resource utilization of physical machines. However, the live Migration technology that VM consolidation relies on is Costly in itself, and this Migration Cost is usually heterogeneous as well as the datacenter. This paper focuses on how to pay limited Migration Costs to save as much energy as possible via VM consolidation in a heterogeneous cloud environment. That is, how to minimize the energy consumption while keeping most of the VMs in the datacenter unmoved. To capture these two conflicting objectives, a Migration Cost estimation method is first proposed and then a consolidation score function is defined for overall evaluation. To maximize the consolidation score, an improved grouping genetic algorithm (IGGA) based on a greedy heuristic and a swap operation is proposed for VM consolidation. Experiments show that IGGA performs better than existing consolidation methods.
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Heterogeneous Virtual Machine Consolidation Using an Improved Grouping Genetic Algorithm
2015 IEEE 17th International Conference on High Performance Computing and Communications 2015 IEEE 7th International Symposium on Cyberspace Safety an, 2015Co-Authors: Quanwang Wu, Fuyuki IshikawaAbstract:Virtual machine (VM) consolidation is a promising approach for improving energy efficiency of the datacenter by increasing the resource utilization of physical machines. However, the live Migration technology that VM consolidation relies on is Costly in itself, and this Migration Cost is usually heterogeneous as well as the datacenter. This paper focuses on how to pay limited Migration Costs to save as much energy as possible via VM consolidation in a heterogeneous cloud environment. That is, how to minimize the energy consumption while keeping most of the VMs in the datacenter unmoved. To capture these two conflicting objectives, a Migration Cost estimation method is first proposed and then a consolidation score function is defined for overall evaluation. To maximize the consolidation score, an improved grouping genetic algorithm (IGGA) based on a greedy heuristic and a swap operation is proposed for VM consolidation. Experiments show that IGGA performs better than existing consolidation methods.
Christoph Meinel - One of the best experts on this subject based on the ideXlab platform.
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CLOSER - Machine Learning Approach for Live Migration Cost Prediction in VMware Environments
Proceedings of the 9th International Conference on Cloud Computing and Services Science, 2019Co-Authors: Mohamed Esam Elsaid, Hazem M Abbas, Christoph MeinelAbstract:Virtualization became a commonly used technology in datacenters during the last decade. Live Migration is an essential feature in most of the clusters hypervisors. Live Migration process has a Cost that includes the Migration time, downtime, IP network overhead, CPU overhead and power consumption. This Migration Cost cannot be ignored, however datacenter admins do live Migration without expectations about the resultant Cost. Several research papers have discussed this problem, however they could not provide a practical model that can be easily implemented for Cost prediction in VMware environments. In this paper, we propose a machine learning approach for live Migration Cost prediction in VMware environments. The proposed approach is implemented as a VMware PowerCLI script that can be easily implemented and run in any vCenter Server Cluster to do data collection of previous Migrations statistics, train the machine learning models and then predict live Migration Cost. Testing results show how the proposed framework can predict live Migration time, network throughput and power consumption Cost with accurate results and for different kinds of workloads. This helps datacenters admins to have better planning for their VMware environments live Migrations.
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machine learning approach for live Migration Cost prediction in vmware environments
International Conference on Cloud Computing and Services Science, 2019Co-Authors: Mohamed Esam Elsaid, Hazem M Abbas, Christoph MeinelAbstract:Virtualization became a commonly used technology in datacenters during the last decade. Live Migration is an essential feature in most of the clusters hypervisors. Live Migration process has a Cost that includes the Migration time, downtime, IP network overhead, CPU overhead and power consumption. This Migration Cost cannot be ignored, however datacenter admins do live Migration without expectations about the resultant Cost. Several research papers have discussed this problem, however they could not provide a practical model that can be easily implemented for Cost prediction in VMware environments. In this paper, we propose a machine learning approach for live Migration Cost prediction in VMware environments. The proposed approach is implemented as a VMware PowerCLI script that can be easily implemented and run in any vCenter Server Cluster to do data collection of previous Migrations statistics, train the machine learning models and then predict live Migration Cost. Testing results show how the proposed framework can predict live Migration time, network throughput and power consumption Cost with accurate results and for different kinds of workloads. This helps datacenters admins to have better planning for their VMware environments live Migrations.
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SC² - Enhanced Cost Analysis of Multiple Virtual Machines Live Migration in VMware Environments
2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2), 2018Co-Authors: Mohamed Esam Elsaid, Ahmed Shawish, Christoph MeinelAbstract:Live Migration is an important feature in modern software-defined datacenters and cloud computing environments. Dynamic resource management, load balance, power saving and fault tolerance are all dependent on the live Migration feature. Despite the importance of live Migration, the Cost of live Migration cannot be ignored and may result in service availability degradation. Live Migration Cost includes the Migration time, downtime, CPU overhead, network and power consumption. There are many research articles that discuss the problem of live Migration Cost with different scopes like analyzing the Cost and relate it to the parameters that control it, proposing new Migration algorithms that minimize the Cost and also predicting the Migration Cost. For the best of our knowledge, most of the papers that discuss the Migration Cost problem focus on open source hypervisors. For the research articles focus on VMware environments, none of the published articles proposed Migration time, network overhead and power consumption modeling for single and multiple VMs live Migration. In this paper, we propose empirical models for the live Migration time, network overhead and power consumption for single and multiple VMs Migration. The proposed models are obtained using a VMware based testbed.
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Enhanced Cost Analysis of Multiple Virtual Machines Live Migration in VMware Environments
2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2), 2018Co-Authors: Mohamed Esam Elsaid, Ahmed Shawish, Christoph MeinelAbstract:Live Migration is an important feature in modern software-defined datacenters and cloud computing environments. Dynamic resource management, load balance, power saving and fault tolerance are all dependent on the live Migration feature. Despite the importance of live Migration, the Cost of live Migration cannot be ignored and may result in service availability degradation. Live Migration Cost includes the Migration time, downtime, CPU overhead, network and power consumption. There are many research articles that discuss the problem of live Migration Cost with different scopes like analyzing the Cost and relate it to the parameters that control it, proposing new Migration algorithms that minimize the Cost and also predicting the Migration Cost. For the best of our knowledge, most of the papers that discuss the Migration Cost problem focus on open source hypervisors. For the research articles focus on VMware environments, none of the published articles proposed Migration time, network overhead and power consumption modeling for single and multiple VMs live Migration. In this paper, we propose empirical models for the live Migration time, network overhead and power consumption for single and multiple VMs Migration. The proposed models are obtained using a VMware based testbed.
T.k. Sarker - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (2) - A Penalty-Based Genetic Algorithm for the Migration Cost-Aware Virtual Machine Placement Problem in Cloud Data Centers
Neural Information Processing, 2020Co-Authors: T.k. Sarker, Maolin TangAbstract:In the past few years, the virtual machine VM placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used ini?źtoday's cloud data centers as they do not consider the Migration Cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the Migration Cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm GA for the VM placement problem that considers the Migration Cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.
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a penalty based genetic algorithm for the Migration Cost aware virtual machine placement problem in cloud data centers
International Conference on Neural Information Processing, 2015Co-Authors: T.k. Sarker, Maolin TangAbstract:In the past few years, the virtual machine VM placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used ini?źtoday's cloud data centers as they do not consider the Migration Cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the Migration Cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm GA for the VM placement problem that considers the Migration Cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.
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A penalty-based genetic algorithm for the Migration Cost-aware virtual machine placement problem in cloud data centers
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015Co-Authors: T.k. Sarker, Maolin TangAbstract:© Springer International Publishing Switzerland 2015. In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today’s cloud data centers as they do not consider the Migration Cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the Migration Cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the Migration Cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.
Quanwang Wu - One of the best experts on this subject based on the ideXlab platform.
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Energy and Migration Cost-Aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacenters
IEEE Transactions on Services Computing, 2019Co-Authors: Quanwang Wu, Fuyuki IshikawaAbstract:Energy efficiency has become one of the major concerns for today's cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live Migration technology that VM consolidation relies on is Costly in itself, and this Migration Cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited Migration Costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a Migration Cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.
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HPCC/CSS/ICESS - Heterogeneous Virtual Machine Consolidation Using an Improved Grouping Genetic Algorithm
2015 IEEE 17th International Conference on High Performance Computing and Communications 2015 IEEE 7th International Symposium on Cyberspace Safety an, 2015Co-Authors: Quanwang Wu, Fuyuki IshikawaAbstract:Virtual machine (VM) consolidation is a promising approach for improving energy efficiency of the datacenter by increasing the resource utilization of physical machines. However, the live Migration technology that VM consolidation relies on is Costly in itself, and this Migration Cost is usually heterogeneous as well as the datacenter. This paper focuses on how to pay limited Migration Costs to save as much energy as possible via VM consolidation in a heterogeneous cloud environment. That is, how to minimize the energy consumption while keeping most of the VMs in the datacenter unmoved. To capture these two conflicting objectives, a Migration Cost estimation method is first proposed and then a consolidation score function is defined for overall evaluation. To maximize the consolidation score, an improved grouping genetic algorithm (IGGA) based on a greedy heuristic and a swap operation is proposed for VM consolidation. Experiments show that IGGA performs better than existing consolidation methods.
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Heterogeneous Virtual Machine Consolidation Using an Improved Grouping Genetic Algorithm
2015 IEEE 17th International Conference on High Performance Computing and Communications 2015 IEEE 7th International Symposium on Cyberspace Safety an, 2015Co-Authors: Quanwang Wu, Fuyuki IshikawaAbstract:Virtual machine (VM) consolidation is a promising approach for improving energy efficiency of the datacenter by increasing the resource utilization of physical machines. However, the live Migration technology that VM consolidation relies on is Costly in itself, and this Migration Cost is usually heterogeneous as well as the datacenter. This paper focuses on how to pay limited Migration Costs to save as much energy as possible via VM consolidation in a heterogeneous cloud environment. That is, how to minimize the energy consumption while keeping most of the VMs in the datacenter unmoved. To capture these two conflicting objectives, a Migration Cost estimation method is first proposed and then a consolidation score function is defined for overall evaluation. To maximize the consolidation score, an improved grouping genetic algorithm (IGGA) based on a greedy heuristic and a swap operation is proposed for VM consolidation. Experiments show that IGGA performs better than existing consolidation methods.