A Risk-Sensitive Task Offloading Strategy for Edge Computing in Industrial Internet of Things

TL;DR

This paper proposes a risk-sensitive task offloading strategy for edge computing in industrial IoT, using Conditional Value at Risk to optimize delay performance and reduce worst-case risks in mission-critical scenarios.

Contribution

It introduces a novel risk-aware optimization framework for task offloading in edge computing, incorporating tail delay analysis and a heuristic algorithm for non-convex problems.

Findings

The proposed algorithm reduces delay risk in edge computing systems.

Simulation confirms improved worst-case delay performance.

The method effectively balances average and tail delay metrics.

Abstract

Edge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems, and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission-critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss, and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer non-linear programming problem, a decomposition into sub-problems is performed and a two-stage heuristic algorithm is proposed. Simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queuing and end-to-end delay.