Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network

TLDR

Recent immersive and autonomous applications generate delay‑sensitive, computation‑intensive tasks, and while mobile edge computing in ultra‑dense networks promises low latency, the distributed edge resources and mobile battery constraints make task offloading difficult. The study investigates task offloading in ultra‑dense networks using software‑defined networking to minimize delay and preserve user battery life. The authors formulate the problem as a mixed‑integer nonlinear program, decompose it into task placement and resource‑allocation subproblems, and devise an efficient offloading scheme based on the solutions. Simulations show the scheme shortens task duration by 20 % and saves 30 % of energy compared with random and uniform offloading.

Abstract

With the development of recent innovative applications (e.g., augment reality, self-driving, and various cognitive applications), more and more computation-intensive and data-intensive tasks are delay-sensitive. Mobile edge computing in ultra-dense network is expected as an effective solution for meeting the low latency demand. However, the distributed computing resource in edge cloud and energy dynamics in the battery of mobile device makes it challenging to offload tasks for users. In this paper, leveraging the idea of software defined network, we investigate the task offloading problem in ultra-dense network aiming to minimize the delay while saving the battery life of user's equipment. Specifically, we formulate the task offloading problem as a mixed integer non-linear program which is NP-hard. In order to solve it, we transform this optimization problem into two sub-problems, i.e., task placement sub-problem and resource allocation sub-problem. Based on the solution of the two sub-problems, we propose an efficient offloading scheme. Simulation results prove that the proposed scheme can reduce 20% of the task duration with 30% energy saving, compared with random and uniform task offloading schemes.