Impatient Queuing for Intelligent Task Offloading in Multi-Access Edge Computing

Multi-access edge computing (MEC) emerges as an essential part of the upcoming Fifth Generation (5G) and future beyond-5G mobile communication systems. It brings computation power to the edge of cellular networks, which is close to the energy-constrained user devices, and therewith allows the users to offload tasks to the edge computing nodes for a low-latency computation with low battery consumption. However, due to the high dynamics of user demand and server load, task congestion may occur at the edge nodes, leading to long queuing delay. Such delays can significantly degrade the quality of experience (QoE) of some latency-sensitive applications, raise the risk of service outage, and cannot be efficiently resolved by conventional queue management solutions. In this article, we study an latency-outage critical scenario, where the users intend to reduce the risk of latency outage. We propose an impatience-based queuing strategy for such users to intelligently choose between MEC offloading and local computation, allowing them to rationally renege from the task queue. The proposed approach is demonstrated by numerical simulations as efficient for generic service model, when a perfect queue information is available. For the practical case where the users obtain no perfect queue information, we design a optimal online learning strategy to enable its application in Poisson service scenarios.