Partial offloading in device-to-device-assisted MEC network: A utility optimization approach

Abstract

In order to meet the growing demand for faster and more efficient communication and computing in internet of things networks, the device-to-device (D2D)-assisted multi-access edge computing (MEC) network has emerged as a promising solution. However, most existing literature overlooks the monetary cost associated with computing and communication resources when it comes to computation offloading. Motivated by this, we first formulate a novel utility function which is defined as the tradeoff between the benefit of the delay reduction via partial offloading and the cost of the purchased computing and communication resources. Then, we propose the partial offloading-based utility optimization (POUO) algorithm to solve the utility optimization problem. Afterward, we determine the resource allocation policy and offloading ratios while taking into account the constraints of the utility optimization problem. In addition, the offloading pairing decision in the device-to-edge server (D2E) offloading mode is modeled as a knapsack problem and solved using a greedy algorithm. For D2D offloading mode, we transform the offloading pairing decision into an assignment problem and apply the Hungarian algorithm to solve it. Finally, we validate and evaluate the proposed POUO algorithm across a range of system parameters. Simulation results show that the POUO algorithm improves the completed tasks ratio, the total utility, and the average delay.