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Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2020-01-30 , DOI: 10.1142/s021800142051012x
Hui Xie 1 , Li Wei 1 , Dong Liu 1 , Luda Wang 1
Affiliation  

Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

中文翻译:

基于机器学习方法的异构计算系统任务调度

异构计算系统(HCS)的任务调度问题日益流行,现已成为该领域的研究热点。HCS 的任务调度问题,本质上可以描述为将任务分配给适当的处理器执行,已被证明是 NP 完全的。然而,现有的调度算法存在缺乏全局视图的固有局限性。在这里,我们报告了一种在异构计算环境中基于多逻辑回归理论(称为 MLRS)的新型任务调度算法。首先,我们收集最佳调度计划作为历史训练集,然后建立调度模型,通过该模型可以预测接下来的调度动作。通过对实验结果的分析,
更新日期:2020-01-30
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