Optimal Schedule of Mobile Edge Computing for Internet of Things Using Partial Information

TLDR

Mobile edge computing enables IoT devices to offload complex tasks to powerful infrastructure, but scheduling is difficult because of stochastic arrivals, wireless channel variability, congested air interface, and the prohibitive feedback burden from thousands of devices. The study aims to produce asymptotically optimal scheduling policies that remain effective even with outdated network information, thereby reducing the need for frequent feedback. By designing a perturbed Lyapunov function to balance throughput and fairness, the authors formulate a per‑slot knapsack problem that is relaxed to handle outdated states, yielding an optimal schedule based on current backlog data. The relaxed schedule retains asymptotic optimality, lets devices self‑nominate for feedback, and simulation results show feedback can be cut to fewer than 60 of 5,000 devices without sacrificing performance.

Abstract

Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.