Joint Computation Offloading and User Association in Multi-Task Mobile Edge Computing

Abstract

Computation intensive and delay-sensitive applications impose severe requirements on mobile devices of providing required computation capacity and ensuring latency. Mobile edge computing (MEC) is a promising technology that can alleviate computation limitation of mobile users and prolong their lifetime through computation offloading. However, computation offloading in an MEC environment faces severe issues due to dense deployment of MEC servers. Moreover, a mobile user has multiple mutually dependent tasks, which make offloading policy design even more challenging. To address the above-mentioned problems in this paper, we first propose a novel two-tier computation offloading framework in heterogeneous networks. Then, we formulate joint computation offloading and user association problem for multi-task mobile edge computing system to minimize overall energy consumption. To solve the optimization problem, we develop an efficient computation offloading algorithm by jointly optimizing user association and computation offloading where computation resource allocation and transmission power allocation are also considered. Numerical results illustrate fast convergence of the proposed algorithm, and demonstrate the superior performance of our proposed algorithm compared to state of the art solutions.