LBA-EC: Load Balancing Algorithm Based on Weighted Bipartite Graph for Edge Computing
作者机构：School of Computer Science Nanjing University of Posts and Telecommunications Institue of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC and Intelligent Processing Engineer Research Center
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2023年第32卷第2期
页 面：313-324页
核心收录：
学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 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] 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]
基 金：supported by the National Key R&D Program of China(2020YFB2104000, 2020YFB2104002) Natural Science Foundation of the Jiangsu Province (Higher Education Institutions)(BK20170900,19KJB520046, 20KJA520001) Innovative and Entrepreneurial Talents Projects of Jiangsu Province Jiangsu Planned Projects for Postdoctoral Research Funds (2019K024) Six Talent Peak Projects in Jiangsu Province (JY02) Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX19 0921, KYCX19 0906) Open Research Project of Zhejiang Lab (2021KF0AB05) NUPT DingShan Scholar Project and NUPTSF (NY219132)
主 题：Energy consumption Processor scheduling Load management Scheduling Bipartite graph Delays Servers
摘 要：Compared with cloud computing environment, edge computing has many choices of service providers due to different deployment environments. The flexibility of edge computing makes the environment more complex. The current edge computing architecture has the problems of scattered computing resources and limited resources of single computing node. When the edge node carries too many task requests, the makespan of the task will be delayed. We propose a load balancing algorithm based on weighted bipartite graph for edge computing(LBA-EC), which makes full use of network edge resources, reduces user delay, and improves user service experience. The algorithm is divided into two phases for task scheduling. In the first phase, the tasks are matched to different edge servers. In the second phase, the tasks are optimally allocated to different containers in the edge server to execute according to the two indicators of energy consumption and completion time. The simulations and experimental results show that our algorithm can effectively map all tasks to available resources with a shorter completion time.
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