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A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers


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Liste des auteurs: Dabiah Alboaneen, Hugo Tianfield, Yan Zhang, and Bernardi Pranggono
Editeur: Elsevier
Année de publication: 2021
Journal: Future Generation Computer Systems
Numéro du volume: 115
Numéro de publication: February 2021
Page d'accueil: 201
Dernière page: 212
Nombre de pages: 12
ISSN: 0167-739X
Web of Science ID: 000591438600008
PubMed-ID:
Scopus ID: 85090867700
eISSN: 1872-7115


The virtual machine (VM) allocation problem is one of the main issues in cloud data centers. This article proposes a new metaheuristic method to optimize joint task scheduling and VM placement (JTSVMP) in cloud data center. The JTSVMP problem, though composed of two parts, namely task scheduling and VM placement, is treated as a joint problem to be resolved by using metaheuristic optimization algorithms (MOAs). The proposed co-optimization process aims to schedule task into the VM which has the least execution cost within deadline constraint and then to place the selected VM on most utilized physical host (PH) within capacity constraint. To evaluate the performance of our proposed co-optimization process, we compare the performances of two different scenarios, i.e., task scheduling algorithms and integrateion co-optimization of task scheduling and VM placement using MOAs, namely the basic glowworm swarm optimization (GSO), moth-flame glowworm swarm optimization (MFGSO) and genetic algorithm (GA). Simulation results show that optimizing joint task scheduling and VM placement leads to better overall results in terms of minimizing execution cost, makespan and degree of imbalance and maximizing PHs resource utilization.


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Dernière mise à jour le 2021-22-03 à 09:51