Research Article
A Novel Probabilistic-Performance-Aware Approach to Multi-workflow Scheduling in the Edge Computing Environment
@INPROCEEDINGS{10.1007/978-3-030-67537-0_38, author={Yuyin Ma and Ruilong Yang and Yiqiao Peng and Mei Long and Xiaoning Sun and Wanbo Zheng and Xiaobo Li and Yong Ma}, title={A Novel Probabilistic-Performance-Aware Approach to Multi-workflow Scheduling in the Edge Computing Environment}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Edge computing Workflow scheduling Probabilistic model Quality-of-service (QoS)}, doi={10.1007/978-3-030-67537-0_38} }- Yuyin Ma
Ruilong Yang
Yiqiao Peng
Mei Long
Xiaoning Sun
Wanbo Zheng
Xiaobo Li
Yong Ma
Year: 2021
A Novel Probabilistic-Performance-Aware Approach to Multi-workflow Scheduling in the Edge Computing Environment
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_38
Abstract
Edge computing is a decentralized computing infrastructure in which data, calculation, storage and applications are located somewhere between the data source and the computing facilities. While the edge servers enjoy the close proximity to the end-users to provide services at reduced latency and lower energy costs, we use from limitations in computational and radio resources, which calls for smart, quality-of-service (QoS) guaranteed and efficient task scheduling methods and strategies. For addressing the edge-environment-oriented multi-workflow scheduling problem, in this paper, we propose a probabilistic-QoS-aware approach to multi-workflow scheduling over edge servers with time-varying QoS. Our proposed method leveraged a probability-mass function-based QoS aggregation model and a discrete firefly algorithm for generating the multi-workflow scheduling plans. In order to prove the effectiveness of our proposed method, we conducted an experimental case study based on varying types of workflows and a real-world dataset for edge server positions. It can be seen that our method clearly outperforms its competitors in terms of completion time, cost, and deadline validation rate.
