A Novel Probabilistic-Performance-Aware Approach to Multi-workflow Scheduling in the Edge Computing Environment

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.