On-Device Deep Reinforcement Learning for Decentralized Task Offloading Performance trade-offs in the training process

TL;DR

This paper develops a decentralized deep reinforcement learning approach for task offloading in edge computing, analyzing its performance and trade-offs in real 5G-connected testbed environments.

Contribution

It introduces a decentralized DRL agent for offloading decisions and evaluates its performance and training trade-offs on real edge devices with 5G connectivity.

Findings

Decentralized DRL effectively manages task offloading.

Training locally reduces latency but increases energy consumption.

Training remotely saves energy but may increase latency.

Abstract

Allowing less capable devices to offload computational tasks to more powerful devices or servers enables the development of new applications that may not run correctly on the device itself. Deciding where and why to run each of those applications is a complex task. Therefore, different approaches have been adopted to make offloading decisions. In this work, we propose a decentralized Deep Reinforcement Learning (DRL) agent to address the selection of computing locations. Unlike most existing work, we analyze it in a real testbed composed of various edge devices running the agent to determine where to execute each task. These devices are connected to a Multi-Access Edge Computing (MEC) server and a Cloud server through 5G communications. We evaluate not only the agent's performance in meeting task requirements but also the implications of running this type of agent locally, assessing the trade-offs of training locally versus remotely in terms of latency and energy consumption.