Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

TL;DR

This paper introduces a multi-agent deep reinforcement learning method for energy-aware task offloading in multi-server mobile edge computing, optimizing the trade-off between computation time and system lifetime.

Contribution

It presents a novel multi-agent deep reinforcement learning framework for energy-efficient computation offloading in multi-server mobile edge computing environments.

Findings

Outperforms baseline algorithms in computation time and system lifetime trade-offs.

Demonstrates effectiveness of deep reinforcement learning in resource allocation.

Shows improved system performance through simulation results.

Abstract

We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium. We propose a multi-agent deep reinforcement learning algorithm, where each server is equipped with an agent, observing the status of its associated users and selecting the best user for offloading at each step. We consider computation time (i.e., task completion time) and system lifetime as two key performance indicators, and we numerically demonstrate that our approach outperforms baseline algorithms in terms of the trade-off between computation time and system lifetime.