Wang Bo, Cheng Junqiang, Cao Jie, Wang Changhai, Huang Wanwei
Zhengzhou University of Light Industry, Zhengzhou, China.
Europe-Aisa Hi-tech and Digital Technology Company Limited, Zhengzhou, China.
PeerJ Comput Sci. 2022 Feb 15;8:e893. doi: 10.7717/peerj-cs.893. eCollection 2022.
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.
任务调度通过将请求的任务合理映射到混合设备-边缘-云资源，有助于提高设备-边缘-云协同计算（DE3C）的资源效率和用户满意度。在本文中，我们聚焦于任务调度问题，以优化DE3C环境中的服务水平协议（SLA）满意度和资源效率。现有工作仅关注三个子问题（卸载决策、任务分配和任务排序）中的一两个，导致解决方案次优。为解决此问题，我们首先将该问题表述为二元非线性规划，并提出一种整数粒子群优化方法（IPSO），以便在合理时间内解决该问题。通过对计算核心的任务分配进行整数编码，我们提出的方法利用IPSO联合解决卸载决策和任务分配问题，并将最早截止时间优先方案集成到IPSO中，以解决每个核心的任务排序问题。大量实验结果表明，我们的方法在SLA满意度和资源效率方面分别比几种经典和最新的任务调度方法性能提高了高达953%和964%。
