Monarch Butterfly Optimization for Reliable Scheduling in Cloud

作     者：B.Gomathi S.T.Suganthi Karthikeyan Krishnasamy J.Bhuvana 

作者机构：Department of Information TechnologyHindusthan College of Engineering and TechnologyCoimbatore641032India Department of Computer NetworkingLebanese French UniversityErbil44001Iraq Department of Information TechnologyCoimbatore Institute of TechnologyCoimbatore641014India Department of MCASchool of Computer Science and ITJain(Deemed to be)UniversityBangalore560069India 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第12期

页      面：3693-3710页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

主　　题：Improved monarch butterfly optimization cloud computing makespan reliability fuzzy dominance task scheduling 

摘      要：Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application *** scheduling of the cloud tasks is a well-recognized NP-hard *** Task scheduling problem is convoluted while convincing different objectives,which are dispute in *** this paper,Multi-Objective Improved Monarch Butterfly Optimization(MOIMBO)algorithm is applied to solve multi-objective task scheduling problems in the cloud in preparation for Pareto optimal *** different dispute objectives,such as makespan,reliability,and resource utilization,are deliberated for task scheduling *** Epsilonfuzzy dominance sort method is utilized in the multi-objective domain to elect the foremost solutions from the Pareto optimal solution ***,together with the Self Adaptive and Greedy Strategies,have been incorporated to enrich the performance of the proposed *** capability and effectiveness of the proposed algorithm are measured with NSGA-II and MOPSO *** simulation results prompt that the proposed MOIMBO algorithm extensively diminishes the makespan,maximize the reliability,and guarantees the appropriate resource utilization when associating it with identified existing algorithms.