Multi-objective Optimization for Cloud Task Scheduling Based on the ANP Model

作     者：LI Kunlun WANG Jun 

作者机构：Electronic Information Engeering CollegeHebei University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2017年第26卷第5期

页      面：889-898页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 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）] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Science and Technology Support Program(No.2013BAK07B00) the Natural Science Foundation of Heibei Province of China(No.F2013201170) the Educational Commission of Hebei Province of China(No.ZD2014008) 

主　　题：Cloud computing Multi-objective task scheduling NSGA-Ⅱ algorithm ANP model GEP algorithm 

摘      要：We propose a multi-objective optimization algorithm for cloud task scheduling based on the Analytic network process(ANP) model to solve the problems in cloud task scheduling,such as the deficiencies of mathematical description,limited optimization abilities of the traditional multi-objective optimization algorithm and the selection of the Pareto optimal ***,we present the mathematical description of cloud task scheduling using matrix ***,the improved Nondominated sorting genetic algorithm II(NSGA-II) multiobjective evolutionary algorithm whose optimization ability is improved by Gene expression programming(GEP)algorithm has been introduced into the cloud task scheduling field to search the Pareto set among ***,ANP model has been combined with the improved NSGA-II to solve the selection problems of Pareto *** with the multi-objective optimization algorithm based on the weighted polynomial,the proposed algorithm can optimize multiple goals at the same time,and can avoid the additional iterations due to the change of users preferences *** simulation results indicate that the proposed algorithm is effective.