Scheduling optimization for upstream dataflows in edge computing  

作　　者：Haohao Wang Mengmeng Sun Lianming Zhang Pingping Dong Yehua Wei Jing Mei 

机构地区：[1]College of Information Science and Engineering,Hunan Normal University,Changsha,410081,China [2]Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing,Hunan Normal University,Changsha,410081,China [3]Hunan Xiangjiang Artificial Intelligence Academy,Hunan Normal University,Changsha,410081,China

出　　处：《Digital Communications and Networks》2023年第6期1448-1457,共10页数字通信与网络（英文版）

基　　金：This work were supported in part by the National Natural Science Foundation of China(No.61572191);Natural Science Foundation of Hunan Province(Nos.2022JJ30398,2022JJ40277 and 2022JJ40278);Scientific Research Fund of Hunan Provincial Education Department(No.17A130).

摘　　要：Edge computing can alleviate the problem of insufficient computational resources for the user equipment,improve the network processing environment,and promote the user experience.Edge computing is well known as a prospective method for the development of the Internet of Things(IoT).However,with the development of smart terminals,much more time is required for scheduling the terminal high-intensity upstream dataflow in the edge server than for scheduling that in the downstream dataflow.In this paper,we study the scheduling strategy for upstream dataflows in edge computing networks and introduce a three-tier edge computing network architecture.We propose a Time-Slicing Self-Adaptive Scheduling(TSAS)algorithm based on the hierarchical queue,which can reduce the queuing delay of the dataflow,improve the timeliness of dataflow processing and achieve an efficient and reasonable performance of dataflow scheduling.The experimental results show that the TSAS algorithm can reduce latency,minimize energy consumption,and increase system throughput.

关 键 词：Edge computing Time-slicing Dataflow scheduling Dynamic analysis 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]