.png){kind=icon}
.png){kind=icon}
Return
Enhanced multi-verse optimizer for task scheduling in cloud computing environments
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.114230.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Multi-verse Optimizer
Cloud Computing
Virtual Machines
Task Scheduling
Optimization
Algorithm
Makespan
MVO
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W