Metaheuristics Algorithms for Virtual Machine Placement in Cloud Computing Environments—A Review

Gabhane, Jyotsna P.; Pathak, Sunil; Thakare, Nita

doi:10.1007/978-981-16-0965-7_28

Cited by 11 publications

(2 citation statements)

References 63 publications

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“…A detailed literature survey was the need of the hour. Gabhane et al [ 10 ] describe the meta-heuristic model that is extensively studied for VMP in cloud computing. Based on a lot of research and surveys, we found some of the most common related works, like this one.…”

Section: Literature Surveysmentioning

confidence: 99%

“…(1) for each host in active state acth i ∈ ACTH then do (2) for each VM actv ij ∈ acth i then do (3) if actv ij in idle state then (4) Deallocate actv ij resources to acth i (5) end if loop (6) end for loop (7) end if loop (5) end for loop (6) StatusofVM � 0 (7) for each host in active state acth i ∈ ACTH from acth k to acth 1 then do (8) for each VM actv ij ∈ acth i then do (9) if migration of actv ij to ACTH-acth i then (10) StatusofVM � StatusofVM + 1 (11) migration ij ← Host ID which is being targeted (12) end if loop (13) end for loop (14) if StatusofVM � j − 1 then (15) migrate all VMs using migration ij (16)…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

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An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

Srivastava

Tiwari

Srivastava

et al. 2022

Computational Intelligence and Neuroscience

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One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

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Section: Literature Surveysmentioning

confidence: 99%