Joint Optimization of Radio Resources and Code Partitioning in Mobile Edge Computing

TL;DR

This paper introduces a joint optimization framework for radio resource management and code partitioning in mobile edge computing, aiming to minimize energy consumption while satisfying latency constraints.

Contribution

It proposes a novel method that jointly optimizes call graph partitioning and radio parameters, including transmit power and constellation size, for energy-efficient offloading.

Findings

Global optimal solutions are achievable for both single and multi-channel transmission.

The proposed suboptimal strategy offers a good tradeoff between complexity and performance.

Numerical results demonstrate significant performance gains under various conditions.

Abstract

The aim of this paper is to propose a computation offloading strategy for mobile edge computing. We exploit the concept of call graph, which models a generic computer program as a set of procedures related to each other through a weighted directed graph. Our goal is to derive the optimal partition of the call graph establishing which procedures are to be executed locally or remotely. The main novelty of our work is that the optimal partition is obtained jointly with the selection of radio parameters, e.g., transmit power and constellation size, in order to minimize the energy consumption at the mobile handset, under a latency constraint taking into account transmit time and execution time. We consider both single and multi-channel transmission strategies and we prove that a globally optimal solution can be achieved in both cases. Finally, we propose a suboptimal strategy aimed at solving a relaxed version of the original problem in order to tradeoff complexity and performance of the proposed framework. Finally, several numerical results illustrate under what conditions in terms of call graph topology, communication strategy, and computation parameters, the proposed offloading strategy provides large performance gains.