Open AccessMOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres
Author(s) -
Elsedimy Elsayed,
Algarni Fahad
Publication year - 2022
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/ntw2.12033
Subject(s) - computer science , job shop scheduling , ant colony optimization algorithms , scheduling (production processes) , cloud computing , mathematical optimization , distributed computing , pareto principle , algorithm , routing (electronic design automation) , mathematics , computer network , operating system
Task scheduling in cloud data centres is an optimisation problem that aims to minimise power consumption and task makespan as well as ensures the quality of service delivered to cloud consumers. Although there are several existing task scheduling approaches, these methods mainly focus on optimising makespans of tasks while ignoring critical issues. This paper presents a comprehensive multi‐objective task scheduling model based on an improved Ant Colony Optimisation (ACO) algorithm, referred to as MOTS‐ACO. In order to promote the diversity of the Pareto set and accelerate the convergence speed, adaptive distribution probability is incorporated into the proposed algorithm, specifically in the process of updating the global rule. The performance of MOTS‐ACO is compared with several existing multi‐objectives task scheduling algorithms based on the makespan time, turnaround time, power efficiency and load balancing parameters. The results show the superiority of MOTS‐ACO in terms of the makespan time, turnaround time, power efficiency and load balancing. Moreover, the proposed MOTS‐ACO algorithm introduces more diversity in the search and accelerates the convergence speed towards the Pareto optimal solution.