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Weighted-adaptive Inertia Strategy for Multi-objective Scheduling in Multi-clouds

Mazen Farid1,3,*, Rohaya Latip1,2, Masnida Hussin1, Nor Asilah Wati Abdul Hamid1

1 Department of Communication Technology and Networks, Universiti Putra Malaysia (UPM), Serdang, 43400, Malaysia
2 Institute for Mathematical Research (INSPEM), Universiti Putra Malaysia (UPM), Serdang, 43400, Malaysia
3 Faculty of Education-Saber, University of Aden, Aden, 2408, Yemen

* Corresponding Author: Mazen Farid. Email: email

Computers, Materials & Continua 2022, 72(1), 1529-1560. https://doi.org/10.32604/cmc.2022.021410

Abstract

One of the fundamental problems associated with scheduling workflows on virtual machines in a multi-cloud environment is how to find a near-optimum permutation. The workflow scheduling involves assigning independent computational jobs with conflicting objectives to a set of virtual machines. Most optimization methods for solving non-deterministic polynomial-time hardness (NP-hard) problems deploy multi-objective algorithms. As such, Pareto dominance is one of the most efficient criteria for determining the best solutions within the Pareto front. However, the main drawback of this method is that it requires a reasonably long time to provide an optimum solution. In this paper, a new multi-objective minimum weight algorithm is used to derive the Pareto front. The conflicting objectives considered are reliability, cost, resource utilization, risk probability and makespan. Because multi-objective algorithms select a number of permutations with an optimal trade-off between conflicting objectives, we propose a new decision-making approach named the minimum weight optimization (MWO). MWO produces alternative weight to determine the inertia weight by using an adaptive strategy to provide an appropriate alternative for all optimal solutions. This way, consumers’ needs and service providers’ interests are taken into account. Using standard scientific workflows with conflicting objectives, we compare our proposed multi-objective scheduling algorithm using minimum weigh optimization (MOS-MWO) with multi-objective scheduling algorithm (MOS). Results show that MOS-MWO outperforms MOS in term of QoS satisfaction rate.

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Cite This Article

APA Style
Farid, M., Latip, R., Hussin, M., Hamid, N.A.W.A. (2022). Weighted-adaptive inertia strategy for multi-objective scheduling in multi-clouds. Computers, Materials & Continua, 72(1), 1529-1560. https://doi.org/10.32604/cmc.2022.021410
Vancouver Style
Farid M, Latip R, Hussin M, Hamid NAWA. Weighted-adaptive inertia strategy for multi-objective scheduling in multi-clouds. Comput Mater Contin. 2022;72(1):1529-1560 https://doi.org/10.32604/cmc.2022.021410
IEEE Style
M. Farid, R. Latip, M. Hussin, and N.A.W.A. Hamid "Weighted-adaptive Inertia Strategy for Multi-objective Scheduling in Multi-clouds," Comput. Mater. Contin., vol. 72, no. 1, pp. 1529-1560. 2022. https://doi.org/10.32604/cmc.2022.021410



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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