Game Theory based Joint Task Offloading and Resources Allocation Algorithm for Mobile Edge Computing

TL;DR

This paper introduces a game theory-based algorithm for joint task offloading and resource allocation in mobile edge computing, effectively reducing energy consumption and latency while managing inter-cell interference.

Contribution

It proposes a novel game-theoretic approach that jointly optimizes task offloading, CPU capacity, and power control, with proven convergence and efficiency in MEC systems.

Findings

The algorithm reduces energy consumption and latency.

It guarantees the existence of a Nash equilibrium.

Simulation results confirm improved MEC performance.

Abstract

Mobile edge computing (MEC) has emerged for reducing energy consumption and latency by allowing mobile users to offload computationally intensive tasks to the MEC server. Due to the spectrum reuse in small cell network, the inter-cell interference has a great effect on MEC performances. In this paper, for reducing the energy consumption and latency of MEC, we propose a game theory based approach to join task offloading decision and resources allocation together in the MEC system. In this algorithm, the offloading decision, the CPU capacity adjustment, the transmission power control, and the network interference management of mobile users are regarded as a game. In this game, based on the best response strategy, each mobile user makes their own utility maximum rather than the utility of the whole system. We prove that this game is an exact potential game and the Nash equilibrium (NE) of this game exists. For reaching the NE, the best response approach is applied. We calculate the best response of these three variables. Moreover, we investigate the properties of this algorithm, including the convergence, the computational complexity, and the Price of anarchy (PoA). The theoretical analysis shows that the inter-cell interference affects on the performances of MEC greatly. The NE of this game is Pareto efficiency. Finally, we evaluate the performances of this algorithm by simulation. The simulation results illustrate that this algorithm is effective in improving the performances of the multi-user MEC system.