Task scheduling to a virtual machine using a multi‐objective mayfly approach for a cloud environment

Summary Cloud computing has been progressively popular in the arenas of research and business in the recent years. Virtualization is a resource management approach used in today's cloud computing environment. Virtual Machine (VM) migration algorithms allow for more dynamic resource allocation,...

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Bibliographic Details
Published in:Concurrency and computation Vol. 34; no. 24
Main Authors: Durairaj, Selvam, Sridhar, Rajeswari
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc 01.11.2022
Subjects:
Algorithms
CDC
Cloud computing
Data centers
Design optimization
mayfly algorithm
multi‐objective optimization
Resource allocation
Resource management
Resource utilization
Task scheduling
Virtual environments
virtual machine
Workflow
ISSN:1532-0626, 1532-0634
Online Access:Get full text
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Summary:Summary Cloud computing has been progressively popular in the arenas of research and business in the recent years. Virtualization is a resource management approach used in today's cloud computing environment. Virtual Machine (VM) migration algorithms allow for more dynamic resource allocation, as well as improvement in computing power and communication capability in cloud data centers. This necessitates an intelligent optimization approach to VM allocation design for an improved performance of application. In this article, a multi‐objective optimal design approach is proposed to tackle the tasks of VM allocation. Multi‐Objective Optimization (MOO) is a strategy adopted by several methods to handle tasks and workflow scheduling issues that deal with numerous opposing goals. In the cloud computing context, effective task scheduling is critical for achieving cost effective implementation as well as resource utilization. To address the optimal solution, this article proposes an entropy‐based multi objective mayfly algorithm is assessed using a convergence pattern in MOO. The model is tested by implementing in a cloud simulator and results prove that the recommended model has an improved performance with regard to factors such as time and utilization rate.
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.7236