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

TL;DR

This paper proposes an online joint radio and computational resource management algorithm for multi-user mobile-edge computing systems, aiming to minimize long-term power consumption while ensuring task buffer stability, with proven performance tradeoffs and delay reduction mechanisms.

Contribution

It introduces a novel online algorithm for dynamic resource management in MEC systems, providing closed-form solutions and delay-improvement strategies, along with rigorous performance analysis.

Findings

The algorithm achieves an $O(1/V)$ power consumption and $O(V)$ delay tradeoff.

Simulation results validate the theoretical analysis and parameter impacts.

The delay-improved mechanism effectively reduces execution delay.

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 as 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 $\left[O\left(1\slash V\right),O\left(V\right)\right]$ tradeoff with $V$ as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.