Delay and Throughput Analysis of Computation Offloading in Mobile Edge Computing: A Queueing Network Approach
TL;DR
A product-form queueing-network (PFQN) model with an approximation to capture the computation and communication dynamics of tree-structured task execution in a multi-tier MEC system is developed and results show that the proposed PFQN approximation provides accurate delay estimates.
Abstract
Mobile edge computing (MEC) enables mobile devices to offload computation to nearby edge servers and to the cloud in order to reduce end-to-end delay for applications such as AR/VR, real-time inference, and sensor-driven analytics. In this paper, we study static computation offloading when each task consists of multiple dependent subtasks represented by a rooted directed tree. We develop a product-form queueing-network (PFQN) model with an approximation to capture the computation and communication dynamics of tree-structured task execution in a multi-tier MEC system. Based on this model, we derive closed-form expressions for effective server utilizations and waiting times, and then construct a recursive algorithm for evaluating the average delay of general tree-structured tasks. We formulate the static offloading design problem as the minimization of the rate-weighted average task delay over the routing probabilities, and solve it through a differentiable optimization framework based on softmax parameterization, log-sum-exp smoothing, and a stability barrier on server utilizations. Numerical results show that the proposed PFQN approximation provides accurate delay estimates and that the delay-optimized static policy consistently outperforms the considered baseline algorithms in terms of both average task delay and empirical maximum stable throughput.
