  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.
