Offloading Schemes in Mobile Edge Computing for Ultra-Reliable Low Latency Communications

By Jianhui Liu, Qi Zhang. 2018

183 citations recorded by the source corpus.

The ultra-reliable low latency communications (uRLLC) in the fifth generation mobile communication system aims to support diverse emerging applications with strict requirements of latency and reliability. Mobile edge computing (MEC) is considered as a promising solution to reduce the latency of computation-intensive tasks leveraging powerful computing units at short distance. The state-of-art work on task offloading to MEC mainly focuses on the tradeoff between latency and energy consumption, rather than reliability. In this paper, the tradeoff between the latency and reliability in task offloading to MEC is studied. A framework is provided, where user equipment partitions a task into sub-tasks and offloads them to multiple nearby edge nodes (ENs) in sequence. In this framework, we formulate an optimization problem to jointly minimize the latency and offloading failure probability. Since the formulated problem is nonconvex, we design three algorithms based on heuristic search, reformulation linearization technique and semi-definite relaxation, respectively, and solve the problem through optimizing EN candidates selection, offloading ordering and task allocation. Compared with the previous work, the numerical simulation results show that the proposed algorithms strike a good balance between the latency and reliability in uRLLC. Among them, the Heuristic Algorithm achieves the best performance in terms of the latency and reliability with the minimal complexity.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, background): “To further reduce latency, as done in [49], the training process can partly be offloaded to other proximal edge nodes with higher computational power.”
Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019, unknown): “A user offloads either all components of an application (binary offloading) [96]–[98] or some components of an application to the edge server (partial offloading) [99], [100].”
Resource Scheduling in Edge Computing: A Survey (2021, background): “Pa rt ia l O ffl oa di ng [79] Revenue a) Task-destination association; b) offloading workload amount determining; c) energy harvesting; [80] Delay, energy consumption a) Computing resource allocation; b) offloading ratio determining; [81] Latency a) Task-destination association; b) offloading ratio”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, background): “In multicell MEC networks, Liu and Zhang [93] investigated how to improve the tradeoff between latency and reliability, where a typical user is considered.”