Energy Minimization for Mobile Edge Computing Networks with Time-Sensitive Constraints

TL;DR

This paper proposes an iterative optimization framework for energy-efficient task offloading and resource allocation in OFDMA-based mobile edge computing networks with time-sensitive constraints, balancing latency and energy consumption.

Contribution

It introduces a novel decomposition approach and an iterative algorithm to jointly optimize offloading and resource allocation in MEC networks with partial offloading capabilities.

Findings

Significant energy savings demonstrated in simulations.

Effective handling of time-sensitive tasks with partial offloading.

Closed-form solutions for offloading decisions derived.

Abstract

Mobile edge computing (MEC) provides users with a high quality experience (QoE) by placing servers with rich services close to the end users. Compared with local computing, MEC can contribute to energy saving, but results in increased communication latency. In this paper, we jointly optimize task offloading and resource allocation to minimize the energy consumption in an orthogonal frequency division multiple access (OFDMA)-based MEC networks, where the time-sensitive tasks can be processed at both local users and MEC server via partial offloading. Since the optimization variables of the problem are strongly coupled, we first decompose the original problem into two subproblems named as offloading selection (PO), and subcarriers and computing resource allocation (PS), and then propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution for PO, and deal with PS by an alternating way in the dual domain due to its NP-hardness. Simulation results demonstrate