Impatient Queuing for Intelligent Task Offloading in Multi-Access Edge Computing
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by
Bin Han, Vincenzo Sciancalepore, Yihua Xu, Di Feng, Hans D. Schotten
2021
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 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.
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