Multi-objective constraint task scheduling algorithm for multi-core processors

作     者：Xie, Ying Wu, Jinzhao 

作者机构：Southwest Minzu Univ Key Lab Comp Syst State Ethn Affairs Commiss Chengdu 610041 Sichuan Peoples R China Southwest Minzu Univ Sch Comp Sci & Technol Chengdu 610041 Sichuan Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Sichuan Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Guangxi Univ Nationalities Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 53006 Guangxi Peoples R China 

出 版 物：《CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS》 (簇计算)

年 卷 期：2019年第22卷第3期

页      面：953-964页

核心收录：

学科分类：07[理学] 0703[理学-化学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Fundamental Research Funds for the Central Universities, Southwest University for Nationalities [2015NZYQN28] National Natural Science Foundation of China [11461006, 11371003] Special Fund for Scientific and Technological Bases and Talents of Guangxi [2016AD05050] 

主　　题：Multi-core processor Multi-objective constraint Artificial immune Task scheduling 

摘      要：A task scheduling algorithm is an effective means to ensure multi-core processor system efficiency. This paper defines the task scheduling problem for multi-core processors and proposes a multi-objective constraint task scheduling algorithm based on artificial immune theory (MOCTS-AI). The MOCTS-AI uses vaccine extraction and vaccination to add prior knowledge to the problem and performs vaccine selection and population updating based on the Pareto optimum, thereby accelerating the convergence of the algorithm. In the MOCTS-AI, the crossover and mutation operators and the corresponding use probability for the task scheduling problem are designed to guarantee both the global and local search ability of the algorithm. Additionally, the antibody concentration in the the MOCTS-AI is designed based on the bivariate entropy. By designing the selection probability in consideration of the concentration probability and fitness probability, antibodies with high fitness and low concentration are selected, thereby optimizing the population and ensuring its diversity. A simulation experiment was performed to analyze the convergence of the algorithm and the solution diversity. Compared with other algorithms, the MOCTS-AI effectively optimizes the scheduling length, system energy consumption and system utilization.