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(ICWS 2020) Scheduling Multi-workflows over Edge Computing Resources with Time-varying Performance, a Novel

About This Webinar

Abstract: The edge computing paradigm is featured by the ability to off-load computing tasks from mobile devices to edge clouds and provide high cost-efficient computing resources, storage and network services closer to the edge. A key question for workflow scheduling in the edge computing environment is how to guarantee user-perceived quality of services when the supporting edge services and resources are with unstable, time-variant, and fluctuant performance. In this work, we study the workflow scheduling problem in the multi-user edge computing environment and propose a Deep-Q-Network (DQN) -based multi-workflow scheduling approach which is capable of handling time-varying performance of edge services. To validate our proposed approach, we conduct a simulative case study and compare ours with other existing methods. Results clearly demonstrate that our proposed method beats its peers in terms of convergence speed and workflow completion time.

Authors: Hang Liu and Yuyin Ma (Chongqing University, China); Peng Chen (Xihua University, China); Yunni Xia (Chongqing University, China); Wanbo Zheng (Kunming University of Science and Technology, China); Xiaobo Li (Chongqing Animal Husbandry Techniques Extension Center, China)

Email: cquhang@cqu.edu.cn, mayuyin@cqu.edu.cn, chenpeng@hotmail.com, xiayunni@hotmail.com, 20144664@cqu.edu.cn, xiaobo@163.com

Who can view: Everyone
Webinar Price: Free
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HANG LIU received the B.S. degree in network engineering from Chongqing University, Chongqing, China,
in 2018, where he is currently pursuing the master’s degree in computer technology. His current research
interests include edge computing, game theory, and workflow scheduling.
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