  Multi-access edge computing (MEC) emerges as an essential part of the
upcoming Fifth Generation (5G) and future beyond-5G mobile communication
systems. It adds computational power towards the edge of cellular networks,
much closer to energy-constrained user devices, and therewith allows the users
to offload tasks to the edge computing nodes for low-latency applications with
very-limited battery consumption. However, due to the high dynamics of user
demand and server load, task congestion may occur at the edge nodes resulting
in 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 a latency-outage critical scenario, where users
intend to limit 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 to be efficient
for generic service model, when a perfect queue status information is
available. For the practical case where the users obtain only imperfect queue
status information, we design an optimal online learning strategy to enable its
application in Poisson service scenarios.
