Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning approach to optimize computation offloading in mobile-edge computing, dynamically adapting to environmental changes to reduce long-term costs.

Contribution

It proposes a deep Q-network-based algorithm for MEC offloading, effectively handling high-dimensional state spaces without prior statistical knowledge.

Findings

Significant reduction in average cost compared to baseline policies

Effective learning of optimal offloading policy in dynamic environments

Demonstrated robustness and adaptability of the proposed method

Abstract

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep $Q$-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.