Joint Optimal Software Caching, Computation Offloading and Communications Resource Allocation for Mobile Edge Computing

TL;DR

This paper develops a comprehensive joint optimization framework for software caching, computation offloading, and resource allocation in mobile edge computing, addressing practical aspects like software fetching and multicasting.

Contribution

It introduces a novel joint caching, offloading, and resource allocation model that considers software fetching and multicasting, with low-complexity algorithms for solving the NP-hard problem.

Findings

Proposed algorithms outperform existing schemes in resource utilization.

Joint optimization reduces energy consumption under caching and deadline constraints.

Effective handling of software fetching and multicasting improves MEC efficiency.

Abstract

As software may be used by multiple users, caching popular software at the wireless edge has been considered to save computation and communications resources for mobile edge computing (MEC). However, fetching uncached software from the core network and multicasting popular software to users have so far been ignored. Thus, existing design is incomplete and less practical. In this paper, we propose a joint caching, computation and communications mechanism which involves software fetching, caching and multicasting, as well as task input data uploading, task executing (with non-negligible time duration) and computation result downloading, and mathematically characterize it. Then, we optimize the joint caching, offloading and time allocation policy to minimize the weighted sum energy consumption subject to the caching and deadline constraints. The problem is a challenging two-timescale mixed integer nonlinear programming (MINLP) problem, and is NP-hard in general. We convert it into an equivalent convex MINLP problem by using some appropriate transformations and propose two low-complexity algorithms to obtain suboptimal solutions of the original non-convex MINLP problem. Specifically, the first suboptimal solution is obtained by solving a relaxed convex problem using the consensus alternating direction method of multipliers (ADMM), and then rounding its optimal solution properly. The second suboptimal solution is proposed by obtaining a stationary point of an equivalent difference of convex (DC) problem using the penalty convex-concave procedure (Penalty-CCP) and ADMM. Finally, by numerical results, we show that the proposed solutions outperform existing schemes and reveal their advantages in efficiently utilizing storage, computation and communications resources.