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Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers
Ding, Zhe Tian, Yu-Chu Wang, You-Gan Zhang, Wei-Zhe Yu, Zu-Guo
Ding, Zhe
Tian, Yu-Chu
Wang, You-Gan
Zhang, Wei-Zhe
Yu, Zu-Guo
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.
Keywords
genetic algorithm, fitness function, data center, virtual machine placement, energy efficiency
Date
2023
Type
Journal article
Journal
Neural Computing and Applications
Book
Volume
35
Issue
7
Page Range
5421-5436
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open