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

TL;DR

This paper introduces an impatience-based queuing strategy for MEC systems to reduce latency outages by enabling users to intelligently decide between offloading and local computation, improving QoE in dynamic environments.

Contribution

It proposes a novel impatience-based queuing approach for MEC, including an optimal online learning method for imperfect queue information scenarios.

Findings

Efficient in reducing latency outage risk under perfect queue information.

Effective online learning strategy for practical imperfect queue information.

Improves user experience in dynamic MEC environments.

Abstract

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.