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

作     者：Durairaj, Selvam Sridhar, Rajeswari 

作者机构：Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India 

出 版 物：《CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE》 (并行学和计算：实践与经验)

年 卷 期：2022年第34卷第24期

页      面：e7236-e7236页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：CDC mayfly algorithm multi-objective optimization task scheduling virtual machine 

摘      要：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.