Transfer Reinforcement Learning for Adaptive Task Offloading Over Distributed Edge Clouds

Kefan Shuai

doi:10.1109/TCC.2022.3192560

Transfer Reinforcement Learning for Adaptive Task Offloading Over Distributed Edge Clouds

2023-04-01

7

PRE

AI

DOI：10.1109/TCC.2022.3192560

Original

Original request for help

Save

Abstract

En 中文

In the big data era, resource-constrained mobile devices generate an overwhelmingly large amount of data with complex tasks that demand distributed execution. Offloading computation-intensive tasks to nearby edge clouds is promising to solve this problem. However, mobile end devices cannot handle heterogeneous or delay-sensitive tasks. These end devices are also energy constrained with weak adaptability to environment changes. To address and tackle these problems, we present a two-module transfer reinforcement learning (TRL) framework for adaptive task offloading. A domain adaptation module is used to align heterogeneous characteristics of mobile devices. The TRL makes offloading decisions with a deep reinforcement learning (DRL) module. We evaluate the performance of TRL through real-world experiments on edge clouds. Our experiment results show that TRL reduces the task processing time by a factor of 20% from using three well known DRL methods. Our method achieved (15.4 similar to 40)% reduction in task drop rate over these methods. With domain adaptation, the TRL results in (50 similar to 80)% reduction in model convergence time. These advantages in using the TRL framework make it appealing in real-life edge computing applications.

Keywords:

Task analysis

Cloud computing

Training

Edge computing

Adaptation models

Computational modeling

Artificial intelligence

domain adaptation

edge computing

reinforcement learning

and task offloading

Transfer Reinforcement Learning for Adaptive Task Offloading Over Distributed Edge Clouds

Abstract

Journal

Organization