论文标题
MEC中异构时间关键物联网服务的计算资源分配
Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC
论文作者
论文摘要
移动边缘计算（MEC）是处理新兴互联网（IoT）用例（例如虚拟现实（VR），增强现实（AR），自动驾驶汽车的简短延迟（IoT），在短期延迟中处理计算密集型任务的有前途解决方案之一。由于MEC系统中异质服务的共存，任务到达间隔和所需的执行时间可能会因服务而异。为具有随机到达的服务安排计算资源和在边缘服务器（ES）的运行时安排计算资源是一项挑战。在本文中，我们建议在用户和ESS之间进行灵活的计算卸载框架。基于框架，我们提出了一种基于Lyapunov的算法，以动态分配ES的异质时间关键服务的计算资源。提出的算法将平均超时概率最小化，而没有任何任务到达过程的任何先验知识和所需的运行时。数值结果表明，与ES上使用的标准排队模型相比，所提出的算法至少降低了超时概率的35％，并且在各种情况下，计算资源对非导致排队模型的近似利用效率。
Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks within short latency for emerging Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. Due to the coexistence of heterogeneous services in MEC system, the task arrival interval and required execution time can vary depending on services. It is challenging to schedule computation resource for the services with stochastic arrivals and runtime at an edge server (ES). In this paper, we propose a flexible computation offloading framework among users and ESs. Based on the framework, we propose a Lyapunov-based algorithm to dynamically allocate computation resource for heterogeneous time-critical services at the ES. The proposed algorithm minimizes the average timeout probability without any prior knowledge on task arrival process and required runtime. The numerical results show that, compared with the standard queuing models used at ES, the proposed algorithm achieves at least 35% reduction of the timeout probability, and approximated utilization efficiency of computation resource to non-cause queuing model under various scenarios.
