To overcome devices' limitations in performing computation-intense
applications, mobile edge computing (MEC) enables users to offload tasks to
proximal MEC servers for faster task computation. However, current MEC system
design is based on average-based metrics, which fails to account for the
ultra-reliable low-latency requirements in mission-critical applications. To
tackle this, this paper proposes a new system design, where probabilistic and
statistical constraints are imposed on task queue lengths, by applying extreme
value theory. The aim is to minimize users' power consumption while trading off
the allocated resources for local computation and task offloading. Due to
wireless channel dynamics, users are re-associated to MEC servers in order to
offload tasks using higher rates or accessing proximal servers. In this regard,
a user-server association policy is proposed, taking into account the channel
quality as well as the servers' computation capabilities and workloads. By
marrying tools from Lyapunov optimization and matching theory, a two-timescale
mechanism is proposed, where a user-server association is solved in the long
timescale while a dynamic task offloading and resource allocation policy is
executed in the short timescale. Simulation results corroborate the
effectiveness of the proposed approach by guaranteeing highly-reliable task
computation and lower delay performance, compared to several baselines.

本论文提出了一种基于极值理论的移动边缘计算系统设计，用于满足极其可靠的低延迟要求和减少用户能量消耗，同时考虑无线通道动态和服务器资源负载。论文提供了两种时间尺度的解决方案，即长时间尺度下的用户 - 服务器关联和短时间尺度下的动态任务卸载和资源分配策略，并通过仿真实验验证了该方法的有效性。