research-article

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Authors:

Zu-Guo YuAuthors Info & Claims

Neural Computing and Applications, Volume 35, Issue 7

Pages 5421 - 5436

https://doi.org/10.1007/s00521-022-07941-8

Published: 04 November 2022 Publication History

Abstract

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

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Cited By

Mahmoodabadi ZNouri-Baygi M(2024)An approximation algorithm for virtual machine placement in cloud data centersThe Journal of Supercomputing10.1007/s11227-023-05505-880:1(915-941)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11227-023-05505-8
Zhang MLi WJin HZhang LMu YWang L(2023)An adaptive variance vector-based evolutionary algorithm for large scale multi-objective optimizationNeural Computing and Applications10.1007/s00521-023-08505-035:22(16357-16379)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s00521-023-08505-0

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Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 35, Issue 7

Mar 2023

765 pages

ISSN:0941-0643

EISSN:1433-3058

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© The Author(s) 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 04 November 2022

Accepted: 11 October 2022

Received: 26 October 2021

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Cited By

Mahmoodabadi ZNouri-Baygi M(2024)An approximation algorithm for virtual machine placement in cloud data centersThe Journal of Supercomputing10.1007/s11227-023-05505-880:1(915-941)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11227-023-05505-8
Zhang MLi WJin HZhang LMu YWang L(2023)An adaptive variance vector-based evolutionary algorithm for large scale multi-objective optimizationNeural Computing and Applications10.1007/s00521-023-08505-035:22(16357-16379)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s00521-023-08505-0

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