边缘计算中协作计算卸载与动态任务调度

在边缘计算场景中,通过将部分待执行任务卸载到边缘服务器执行能够达到降低移动设备的负载、提升移动应用性能和减少设备开销的目的.对于时延敏感任务,只有在截止期限内完成才具有实际意义.但是边缘服务器的资源往往有限,当同时接收来自多个设备的数据传输及处理任务时,可能造成任务长时间的排队等待,导致部分任务因超时而执行失败,因此无法兼顾多个设备的性能目标.鉴于此,在计算卸载的基础上优化边缘服务器端的任务调度顺序.一方面,将时延感知的任务调度建模为一个长期优化问题,并使用基于组合多臂赌博机的在线学习方法动态调整服务器的调度顺序.另一方面,由于不同的任务执行顺序会改变任务卸载性能提升程度,因而影响任务卸载决策的有效性.为了增加卸载策略的鲁棒性,采用了带有扰动回报的深度Q学习方法决定任务执行位置.仿真算例证明了该策略可在平衡多个用户目标的同时减少系统的整体开销.

Cooperative Computation Offloading and Dynamic Task Scheduling in Edge Computing

In edge computing scenarios, some tasks to be performed will be offloaded to the edge server, which can reduce the load of mobile devices, enhance the performance of mobile applications, and lower the cost of mobile devices. For delay-sensitive tasks, it is critical to ensure they are completed within the deadlines. However, the limited resource of edge servers results in the fact that when data transmission and task processing from multiple devices are received at the same time, some tasks have to wait in queue before they are scheduled. As a result, the long waiting time may cause time-out failure, which will also make it impossible to balance the performance goals of several devices. Therefore, this study optimizes the task scheduling order on the edge server based on computation offloading. Firstly, the task scheduling is modeled as a long-term optimization issue, and the online learning method based on a combination multi-arm bandit is employed to dynamically adjust the scheduling order of the server. Secondly, the dynamically changing order of task execution will lead to different levels of performance enhancement for task offloading, which will influence the validity of offloading decisions. The deep-Q learning method with perturbed reward is adopted to determine the execution sites for tasks to improve the robustness of offloading strategies. Simulation results show that the proposed strategy can balance multiple user objectives and lower the system cost simultaneously.