Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems

TLDR

Mobile‑edge computing offloads intensive tasks to nearby servers, but effective offloading requires dynamic management of radio and computational resources to handle fluctuating demands and fading channels. The paper proposes an online joint radio and computational resource management algorithm that minimizes the long‑term weighted sum power consumption of mobile devices and the MEC server while maintaining task buffer stability. The algorithm derives closed‑form optimal CPU frequencies, transmit power, and bandwidth allocations using a Gauss‑Seidel method, determines optimal server CPU frequencies and scheduling decisions in closed form, and introduces a delay‑improved mechanism to reduce execution delay. Analysis shows an O(1/V) power–delay tradeoff, and simulations validate the theoretical results and illustrate the influence of key parameters.

Abstract

Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective of minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an [O (1/V) , O (V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.