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Mapping and Consolidation of VMs Using Locust-Inspired Algorithms for Green Cloud Computing

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Abstract

High energy consumption and serious reduction in the number of virtual machine (VM) migrations in cloud data centres have become increasingly urgent challenges. Finding an efficient VM mapping method is vital in dealing with these challenges. Server consolidation is a well-known NP-hard problem. Moreover, efficient resource mapping and VM migration should consider multiple factors synthetically, including quality of service, energy consumption, resource utilisation, and migration overheads, which are multi-objective optimisation problems. This letter aims to address these issues using a novel bio-inspired mapping algorithm. Also, this letter revisits the existing locust-inspired resource scheduling algorithm employed in cloud data centres with a real workload as well as an analogy and model and presents a novel algorithm. Critical analysis of the locust approach has shown that it opens new opportunities for future research, suggestions for which have been offered. Such analysis ensures the hardware reliability of an algorithm and the algorithm’s quality of performance. The results show that the proposed algorithm outperforms state-of-the-art bio-inspired algorithms. We compared our algorithm with heuristic and meta-heuristic algorithms. The experimental results show that compared with these algorithms, our algorithm efficiently reduces performance degradation due to migration (PDM), energy consumption, and the number of migrations along with improving server utilisation.

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Notes

  1. http://www.spec.org/power_ssj2008/results/res2011q1/power_ssj2008-20110127-00342.html.

  2. http://www.spec.org/power_ssj2008/results/res2011q1/power_ssj2008-20110124-00339.html.

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Authors and Affiliations

  1. Department of Communication Technology and Networks, Universiti Putra Malaysia, UPM Serdang, 43400, Selangor D.E., Malaysia

    Mohammed Alaa Ala’anzy & Mohamed Othman

  2. Laboratory of Computational Science and Mathematical Physics, Institute for Mathematical Research (INSPEM), Universiti Putra Malaysia, UPM Serdang, 43400, Selangor D.E., Malaysia

    Mohamed Othman

Authors
  1. Mohammed Alaa Ala’anzy
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  2. Mohamed Othman
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Correspondence to Mohammed Alaa Ala’anzy.

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This work is supported in part by the Malaysian Ministry of Education under Research Management Centre, Universiti Putra Malaysia, Putra Grant scheme with High Impact Factor under Grant Number UPM/700-2/1/GPB/2017/9557900.

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Ala’anzy, M.A., Othman, M. Mapping and Consolidation of VMs Using Locust-Inspired Algorithms for Green Cloud Computing. Neural Process Lett 54, 405–421 (2022). https://doi.org/10.1007/s11063-021-10637-0

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