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

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 queueing and end-to-end delay.

In-network placement of delay-constrained computing tasks in a softwarized intelligent edge

“…At the end of the process, the prediction error is estimated as the mean average of the 10 individual errors achieved during each training session. Thus, the above procedure allows to obtain a performance evaluation of the observed model unbiased by any specific partitioning of the dataset into training and validation subsets uniformly distributed in [10,100] ms [54], [55], [56] Average task computing workload (l) 10 • 10 6 CPU cycles [57] Input data size ( s j )…”