article

A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

Authors:

Liang LiuAuthors Info & Claims

Journal of Computer and System Sciences, Volume 79, Issue 8

Pages 1230 - 1242

https://doi.org/10.1016/j.jcss.2013.02.004

Published: 01 December 2013 Publication History

Abstract

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.

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Velázquez Cruz RHernández Castellanos CLi XHandl J(2024)"A Cooperative Multi Indicator-Based Ant Colony Optimization Algorithm for the MOGenConVRP"Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654416(215-218)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654416
Mehta SKaur PAgarwal P(2024)Improved whale optimization variants for SLA-compliant placement of virtual machines in cloud data centersMultimedia Tools and Applications10.1007/s11042-023-15528-183:1(149-171)Online publication date: 1-Jan-2024
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Kosari SHosseini Shirvani MKhaledian NJavaheri D(2024)A Hybrid Discrete Grey Wolf Optimization Algorithm Imbalance-ness Aware for Solving Two-dimensional Bin-packing ProblemsJournal of Grid Computing10.1007/s10723-024-09761-722:2Online publication date: 10-May-2024
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Index Terms

A multi-objective ant colony system algorithm for virtual machine placement in cloud computing
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic

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Reviews

Reviewer: Zoltan Adam Mann

Virtualization is a powerful method for achieving good utilization of physical servers, but finding the optimal mapping of virtual machines (VMs) on the servers of a data center is a challenging problem, and in particular, a multi-objective problem. Hence, the authors attack it with a multi-objective ant colony algorithm, searching for Pareto-optimal solutions based on two dimensions: power consumption and resource wastage. This is a nice, although not revolutionary, contribution to the ever-growing body of research on the VM placement problem. The authors demonstrate with the results of a thorough experimental evaluation that the proposed method, according to the metrics used, outperforms several previous approaches. The idea of using multi-objective optimization techniques to address the VM placement problem seems to be promising, so researchers working in this field may find this work very inspiring. For example, considering other optimization dimensions (like application performance or the number of VM migrations) could be a natural next step to build upon the approach of this paper. Online Computing Reviews Service

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Published In

Journal of Computer and System Sciences Volume 79, Issue 8

December, 2013

152 pages

ISSN:0022-0000

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Academic Press, Inc.

United States

Publication History

Published: 01 December 2013

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https://dl.acm.org/doi/10.1145/3638530.3654416
Mehta SKaur PAgarwal P(2024)Improved whale optimization variants for SLA-compliant placement of virtual machines in cloud data centersMultimedia Tools and Applications10.1007/s11042-023-15528-183:1(149-171)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15528-1
Kosari SHosseini Shirvani MKhaledian NJavaheri D(2024)A Hybrid Discrete Grey Wolf Optimization Algorithm Imbalance-ness Aware for Solving Two-dimensional Bin-packing ProblemsJournal of Grid Computing10.1007/s10723-024-09761-722:2Online publication date: 10-May-2024
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Yousefi MBabamir S(2024)A hybrid energy-aware algorithm for virtual machine placement in cloud computingComputing10.1007/s00607-024-01280-3106:5(1297-1320)Online publication date: 1-May-2024
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