异构边缘资源的任务卸载和协同调度

Task Offloading and Cooperative Scheduling for Heterogeneous Edge Resources

边缘计算广泛应用于物联网、车联网和在线游戏等新兴领域,通过网络边缘部署计算资源为终端设备提供低延迟计算服务.针对如何进行任务卸载以权衡任务执行时间与传输时间、如何调度多个不同截止期任务以最小化总延迟时间等挑战性问题,提出1种异构边缘协同的任务卸载和调度框架,包括边缘网络拓扑节点排序、边缘节点内任务排序、任务卸载策略、任务调度和结果调优等算法组件;设计多种任务卸载策略和任务调度策略;借助多因素方差分析(multi-factor analysis of variance,ANOVA)技术在大规模随机实例上校正算法算子和参数,得到统计意义上的最佳调度算法.基于EdgeCloudSim仿真平台,将所提出调度算法与其3个变种算法从边缘节点数量、任务数量、任务分布、截止期取值区间等角度进行性能比较.实验结果表明,所提出调度算法在各种情形下性能都优于对比算法.

Edge computing is commonly applied in emerging fields such as the Internet of things,the Internet of vehicles,and online games.Edge computing provides low-latency computing services for terminal devices by deploying computing resources at network edges.How to offload tasks to balance execution time and communication time and how to schedule tasks with different deadlines with the objective of minimizing the total tardiness are challenging problems.In this paper,a task offloading and scheduling framework is proposed for the heterogeneous edge computing.There are five components included in the framework:sequencing edge network nodes,sequencing offloaded task,task offloading strategies,task scheduling and the solution improvement.Multiple task offloading and task scheduling strategies are designed and embedded.ANOVA(multi-factor analysis of variance)is used to calibrate the algorithmic components and parameters over a large number of random instances.The algorithm with the best component combination is obtained.Based on the EdgeCloudSim simulation platform,several variants of the proposed algorithm are compared with the proposed algorithm from the perspectives of the number of edge nodes,the number of tasks,the distribution of tasks,and the interval of deadlines.Experimental results show that the proposed algorithm outperforms the other comparisons in all cases.