A Task Scheduling Algorithm Based on Clustering Pre-processing in Space-Based Information Network

作     者：Yufei WANG Jun LIU Shengnan ZHANG Sai XU Jingyi WANG Yufei WANG;Jun LIU;Shengnan ZHANG;Sai XU;Jingyi WANG

作者机构：School of Computer Science and Engineering Northeastern University 

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

年 卷 期：2024年第33卷第1期

页      面：217-230页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 12[管理学] 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] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0835[工学-软件工程] 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 Natural Science Foundation of China (Grant Nos. 62071134 and 61671141) the Fundamental Research Funds for the Central Universities (Grant Nos. N2116015 and N2116020) 

主　　题：Scheduling algorithms Heuristic algorithms Clustering methods Clustering algorithms Simulated annealing Dynamic scheduling Search problems 

摘      要：With the diversification of space-based information network task requirements and the dramatic increase in demand, the efficient scheduling of various tasks in space-based information network becomes a new challenge. To address the problems of a limited number of resources and resource heterogeneity in the space-based information network, we propose a bilateral pre-processing model for tasks and resources in the scheduling pre-processing stage. We use an improved fuzzy clustering method to cluster tasks and resources and design coding rules and matching methods to match similar categories to improve the clustering effect. We propose a space-based information network task scheduling strategy based on an ant colony simulated annealing algorithm for the problems of high latency of space-based information network communication and high resource dynamics. The strategy can efficiently complete the task and resource matching and improve the task scheduling performance. The experimental results show that our proposed task scheduling strategy has less task execution time and higher resource utilization than other algorithms under the same experimental conditions. It has significantly improved scheduling performance.