Integer particle swarm optimization based task scheduling for device-edge-cloud cooperative computing to improve SLA satisfaction

• Published in: PeerJ. Computer science 2022-02, Vol.8, p.e893-e893, Article e893
• Main Authors: Wang, Bo, Cheng, Junqiang, Cao, Jie, Wang, Changhai, Huang, Wanwei
• Format: Article
• Language: English
• Subjects: Cloud computing; Computer Architecture; Deadlines; Distributed and Parallel Computing; Edge cloud; Efficiency; Genetic algorithms; Heuristic; Integers; Internet of Things; Nonlinear programming; Optimization; Particle swarm optimization; Scheduling; Task offloading; Task scheduling; User satisfaction
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.893

Task scheduling helps to improve the resource efficiency and the user satisfaction for Device-Edge-Cloud Cooperative Computing (DE3C), by properly mapping requested tasks to hybrid device-edge-cloud resources. In this paper, we focused on the task scheduling problem for optimizing the Service-Level Agreement (SLA) satisfaction and the resource efficiency in DE3C environments. Existing works only focused on one or two of three sub-problems (offloading decision, task assignment and task ordering), leading to a sub-optimal solution. To address this issue, we first formulated the problem as a binary nonlinear programming, and proposed an integer particle swarm optimization method (IPSO) to solve the problem in a reasonable time. With integer coding of task assignment to computing cores, our proposed method exploited IPSO to jointly solve the problems of offloading decision and task assignment, and integrated earliest deadline first scheme into the IPSO to solve the task ordering problem for each core. Extensive experimental results showed that our method achieved upto 953% and 964% better performance than that of several classical and state-of-the-art task scheduling methods in SLA satisfaction and resource efficiency, respectively.