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

Research Areas
Currently no objects available

Publication Details
Author list: Dabiah Alboaneen, Hugo Tianfield, Yan Zhang, and Bernardi Pranggono
Publisher: Elsevier
Publication year: 2021
Journal: Future Generation Computer Systems
Volume number: 115
Issue number: February 2021
Start page: 201
End page: 212
Number of 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.

Currently no objects available

Currently no objects available

Currently no objects available

Last updated on 2021-22-03 at 09:51

Share link