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DOCEA/D: Dual-Operator-based Constrained many-objective Evolutionary Algorithm based on Decomposition

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Abstract

System virtualization is a key technology nowadays for reducing energy consumption and investment in server computers. However this environment frequently faces the problem of virtual machine placement (VMP). In order to solve this problem effectively, we have proposed a Dual-Operator-based Constrained many-objective Evolutionary Algorithm, DOCEA/D, which employs a Decomposition-based strategy. The algorithm uses differential evolution (DE) as an evolutionary mechanism and also employs a novel diversity maintenance (DM) technique to avoid trapping in local optima. For the purpose of validating the performance of DOCEA/D, it has been compared here with other contemporary many-objective evolutionary algorithms (MaOEAs) namely MOEA/D, NSGA-III and IBEA on three instances of VMP problem. The results on the different quality indicators clearly demonstrate the superiority of DOCEA/D over the counterpart approaches. As a further proof, we graphically show the superiority of our proposed method over the competing approaches and also demonstrate the statistical significance of our results.

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Data availability

All data generated or analyzed during this study are included in this published article as different tables.

Notes

  1. ECU stands for EC2 Compute Units which is a unit of computing power in Amazon EC2 cloud. ECU equals the computing ability of one Xeon 2007 or Opteron 2007 CPU having a clock of 1.0 to 1.2 GHz [26].

  2. https://aws.amazon.com/ec2/instance-types/.

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Funding

This work was supported by the MoST (Ministry of Science and Technology) endowment and NED University Research Grants.

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  1. NED University of Engineering & Technology, Karachi, Pakistan

    Syed Zaffar Qasim & Muhammad Ali Ismail

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  1. Syed Zaffar Qasim
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  2. Muhammad Ali Ismail
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Correspondence to Syed Zaffar Qasim.

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Qasim, S.Z., Ismail, M.A. DOCEA/D: Dual-Operator-based Constrained many-objective Evolutionary Algorithm based on Decomposition. Cluster Comput 25, 4151–4169 (2022). https://doi.org/10.1007/s10586-022-03647-7

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  • DOI: https://doi.org/10.1007/s10586-022-03647-7

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