Joint Communication and Computation Offloading for Ultra-Reliable and Low-Latency With Multi-Tier Computing

In this paper, we study joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. The formulated JCCO problem belongs to a difficult class of mixed-integer non-convex optimization problem, making it computationally intractable. In addition, a strong coupling between binary and continuous variables and the large size of hierarchical edge-cloud systems make the problem even more challenging to solve optimally. To address these challenges, we first decompose the original problem into two subproblems based on the unique structure of the underlying problem and leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. To speed up optimal user association searching, we incorporate a penalty function into the objective function to resolve uncertainties of a binary nature. Two sub-optimal designs for given user association policies based on channel conditions and random user associations are also investigated to serve as state-of-the-art benchmarks. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

Automated summary

This paper studies joint communication and computation offloading for hierarchical edge-cloud systems with ultra-reliable and low latency communications. The goal is to minimize end-to-end latency of computational tasks among multiple industrial IoT devices by optimizing offloading probabilities, processing rates, user association policies, and power control. The formulated problem is computationally intractable due to its mixed-integer non-convex nature and the coupling between binary and continuous variables. To address this, the paper decomposes the problem into two subproblems and uses an alternating optimization approach with convex approximate functions. Numerical results demonstrate the effectiveness of the proposed algorithms in terms of latency and convergence speed.