As mobile edge computing (MEC) finds widespread use for relieving the computational burden of compute- and interaction-intensive applications on end user devices, understanding the resulting delay and cost performance is drawing significant attention. While most existing works focus on singletask offloading in single-hop MEC networks, next generation applications (e.g., industrial automation, augmented/virtual reality) require advance models and algorithms for dynamic configuration of multi-task services over multi-hop MEC networks. In this work, we leverage recent advances in dynamic cloud network control to provide a comprehensive study of the performance of multi-hop MEC networks, addressing the key problems of multi-task offloading, timely packet scheduling, and joint computation and communication resource allocation. We present a fully distributed algorithm based on Lyapunov control theory that achieves throughput-optimal performance with delay and cost guarantees. Simulation results validate our theoretical analysis and provide insightful guidelines on the interplay between communication and computation resources in MEC networks.
翻译:由于移动边缘计算(MEC)广泛用于减轻终端用户设备计算和互动密集型应用的计算负担,理解由此造成的延迟和成本性能正在引起人们的极大注意。虽然大多数现有工程侧重于单式自动卸载的单式自动卸载式移动式移动式移动式移动式移动式边缘计算网络网络,但下一代应用(例如工业自动化、增强/虚拟现实)需要预先模型和算法,以对多式多式移动式移动式移动式移动式计算机网络网络进行动态配置。在这项工作中,我们利用动态云网络控制的最新进展,对多式移动式移动式移动式移动式移动式移动式网络的性能进行全面研究,解决多式任务卸载、及时包装时间安排以及联合计算和通信资源分配等关键问题。我们介绍了基于Lyapunov控制理论的全面分布的算法,该理论以延迟和成本保证的方式实现超载-优化性能性能。模拟结果证实了我们的理论分析,并就多式移动式移动式移动式移动式移动式移动式网络资源之间的相互作用提供了深刻的指导方针。