Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system  

作　　者：Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang 

机构地区：[1]School of Software,Henan Institute of Science and Technology,Xinxiang 453003,China [2]School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China [3]Department of Computing and Informatics,Bournemouth University,Poole BH125BB,UK [4]School of Electronics and Computer Science,University of Southampton,Southampton SO171BJ,UK [5]Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266100,China

出　　处：《Digital Communications and Networks》2024年第6期1837-1850,共14页数字通信与网络(英文版)

基　　金：The financial support of the National Natural Science Foundation of China under grants 61901416 and 61571401(part of the Natural Science Foundation of Henan under grant 242300420269);the Young Elite Scientists Sponsorship Program of Henan under grant 2024HYTP026;the Innovative Talent of Colleges and the University of Henan Province under grant 18HASTIT021。

摘　　要：Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive tasks.One fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource utilization.Existing approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource availability.These approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider lock-in.Therefore,we present Edge Colla,which is based on the integration of edge resources running across multi-edge deployments.Edge Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize them.Extensive experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关 键 词：Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling 

分 类 号：TN9[电子电信—信息与通信工程]