Improving server utilization using fast virtual machine migration

Isci, Canturk; Liu, J.; Abali, Bülent; Kephart, Jeffrey O.; Kouloheris, J.

doi:10.1147/jrd.2011.2167775

Cited by 37 publications

(16 citation statements)

References 9 publications

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“…The work in [2] improved upon the work in [4] by considering the case where multiple cloud providers may concurrently provide the VM services. In [7], the authors developed a novel VM migration scheme to reduce the cost of VM migration by directly accessing the memory of the VMs to be migrated based on their workloads. Instead of executing multiple migration processes in parallel, the authors in [3] studied the migration schedule strategy for VMs to be migrated.…”

Section: Related Workmentioning

confidence: 99%

“…(5), (6) and (7). Then, the server with the smallest correlation coefficient with current being migrated VM would be selected as the destination server.…”

Section: The Destination Server Selection Algorithmmentioning

confidence: 99%

“…We then use the MiCDS algorithm to calculate the correlation coefficients between the VM and its candidate servers also according to Eqs. (5), (6) and (7). Finally, the server with minimum correlation coefficient is selected as the server to which the VM is migrated.…”

Section: Migration To Guarantee Service Qualitymentioning

confidence: 99%

“…First is the implementation of the migration technique, as in [2][3][4]6,7,12,14]. These works focus on how to implement or improve the migration schemes, such as how can faster or more stable VM migration be achieved, while the other concerns the design of the migration scheme to satisfy the SLA requirements of the applications [10,11,13,17].…”

Section: Introductionmentioning

confidence: 99%

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Correlation-based virtual machine migration in dynamic cloud environments

Liu

Shaoping

et al. 2015

Photon Netw Commun

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Virtual machine (VM) migration enables flexible and efficient resource management in modern data centers. Although various VM migration algorithms have been proposed to improve the utilization of physical resources in data centers, they generally focus on how to select VMs to be migrated only according to their resource requirements and ignore the relationship between the VMs and servers with respect to their varying resource usage as well as the time at which the VMs should be migrated. This may dramatically degrade the algorithm performance and increase the operating and the capital cost when the resource requirements of the VMs change dynamically over time. In this paper, we propose an integrated VM migration strategy to jointly consider and address these issues. First, we establish a service level agreement-based soft migration mechanism to significantly reduce the number of VM migrations. Then, we develop two algorithms to solve the VM and server selection issues, in which the correlation between the VMs and the servers is used to identify the appropriate VMs to be migrated and the destination servers for them. The experimental results obtained from extensive simulations show the effectiveness of the proposed algorithms compared to traditional schemes B Liu Liu in terms of the rate of resource usage, the operating cost and the capital cost.

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Section: Related Workmentioning

confidence: 99%

“…(5), (6) and (7). Then, the server with the smallest correlation coefficient with current being migrated VM would be selected as the destination server.…”

Section: The Destination Server Selection Algorithmmentioning

confidence: 99%

Section: Migration To Guarantee Service Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correlation-based virtual machine migration in dynamic cloud environments

Liu

Shaoping

et al. 2015

Photon Netw Commun

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“…Lui et al [114] propose an alternative memory-to-memory VM migration algorithm that uses trace and replay of operations, claiming a much lower migration overhead. The work in [97] proposes another alternative VM migration algorithm that uses remote direct memory access, bypassing Transmission Control Protocol (TCP) in network operations and reducing the data transmission overhead. Besides these works, another technique appeared in the literature to perform live migration of VMs, known as post-copy [89].…”

Section: Virtual Machine Migrationmentioning

confidence: 99%

High Performance Scientific Computing over Hybrid Cloud Platforms

Arroyo¹

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"Croyez ceux qui cherchent la vérité, doutez de ceux qui la trouvent." "Cree a aquellos que buscan la verdad. Duda de los que la han encontrado" André Gide (1952).iii AbstractScientific applications generally require large computational requirements, memory and data management for their execution. Such applications have traditionally used high-performance resources, such as shared memory supercomputers, clusters of PCs with distributed memory, or resources from Grid infrastructures on which the application needs to be adapted to run successfully. In recent years, the advent of virtualization techniques, together with the emergence of Cloud Computing, has caused a major shift in the way these applications are executed. However, the execution management of scientific applications on high performance elastic platforms is not a trivial task.

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Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing

Henry

Anbuananth

Kalarani

2022

Concurrency and Computation

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Data sharing in cloud computing happens with multiple participants to freely distribute the group data, which focuses on advancing the effectiveness of work in cooperative backgrounds and has attained widespread benefits. The main intent of this article is to accomplish a virtual machines (VMs) placement and migration model using a hybrid meta-heuristic concept. A new meta-heuristic algorithm named DJ-HA is developed for optimal VM placement and migration to reduce the count of active servers, and minimization of makespan, and energy consumption with a faster convergence rate in a cloud background. Then, the VM migration is done based on the multi-objective function concerning energy consumption and makespan using the same hybrid DJ-HA.From the result analysis, the energy consumption of the DJ-HA is correspondingly secured at 4.3%, 3.5%, 31%, and 33% more advanced than PSO, GWO, DHOA, and JA, at the 100th iteration for Experiment 1. Accordingly, the cost function of the suggested DJ-HA is secured at 88.8%, 89.4%, 33.3%, and 50% increased than PSO, GWO, DHOA, and JA at the 100th iteration for Experiment 4. Hence, it is proved that the suggested VM migration using DJ-HA is enriched than the other conventional algorithms.

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Improving server utilization using fast virtual machine migration

Cited by 37 publications

References 9 publications

Correlation-based virtual machine migration in dynamic cloud environments

Correlation-based virtual machine migration in dynamic cloud environments

High Performance Scientific Computing over Hybrid Cloud Platforms

Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing

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