Enhanced multi-verse optimizer for task scheduling in cloud computing environments

Sarah E. Shukri, Rizik Al-Sayyed, Amjad Hudaib, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Cloud computing is a trending technology that allows users to use computing resources remotely in a pay-per-use model. One of the main challenges in cloud computing environments is task scheduling, in which tasks should be scheduled efficiently to minimize execution time and cost while maximizing resources’ utilization. Many meta-heuristic algorithms are used for task scheduling in cloud environments in the literature such as Multi-Verse Optimizer (MVO) and Particle Swarm Optimization (PSO). In this paper, an Enhanced version of the Multi-Verse Optimizer (EMVO) is proposed as a superior task scheduler in this area. The proposed EMVO is compared with both original MVO and the PSO algorithms in cloud environments. The results show that EMVO substantially outperforms both MVO and PSO algorithms in terms of achieving minimized makespan time and increasing resources’ utilization.

Original languageEnglish
Article number114230
JournalExpert Systems with Applications
DOIs
Publication statusAccepted/In press - 2020
Externally publishedYes

Keywords

  • Algorithm
  • Cloud Computing
  • Makespan
  • Multi-verse Optimizer
  • MVO
  • Optimization
  • Task Scheduling
  • Virtual Machines

Fingerprint

Dive into the research topics of 'Enhanced multi-verse optimizer for task scheduling in cloud computing environments'. Together they form a unique fingerprint.

Cite this