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

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Abstract

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.

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Acknowledgements

The funding was provided by The Fundamental Research Funds for the Central Universities, Southwest University for Nationalities (Grant No. 2015NZYQN28), National Natural Science Foundation of China (Grant Nos. 11461006 and 11371003) and Special Fund for Scientific and Technological Bases and Talents of Guangxi (Grant No. 2016AD05050).

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Authors and Affiliations

  1. The Key Laboratory for Computer Systems of the State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, 610041, China

    Ying Xie

  2. School of Computer Science and Technology, Southwest Minzu University, Chengdu, 610041, China

    Ying Xie

  3. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610041, China

    Ying Xie

  4. University of the Chinese Academy of Sciences, Beijing, 100049, China

    Ying Xie

  5. Guangxi Key Laboratory of Hybrid Computational and IC Design Analysis, Guangxi University for Nationalities, Nanning, 53006, Guangxi, China

    Jinzhao Wu

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  1. Ying Xie
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  2. Jinzhao Wu
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Correspondence to Ying Xie.

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Xie, Y., Wu, J. Multi-objective constraint task scheduling algorithm for multi-core processors. Cluster Comput 22, 953–964 (2019). https://doi.org/10.1007/s10586-018-2884-6

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  • DOI: https://doi.org/10.1007/s10586-018-2884-6

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