Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud Systems

Resource-constrained edge devices can not efficiently handle the explosive growth of mobile data and the increasing computational demand of modern-day user applications. Task offloading allows the migration of complex tasks from user devices to the remote edge-cloud servers thereby reducing their computational burden and energy consumption while also improving the efficiency of task processing. However, obtaining the optimal offloading strategy in a multi-task offloading decision-making process is an NP-hard problem. Existing Deep learning techniques with slow learning rates and weak adaptability are not suitable for dynamic multi-user scenarios. In this article, we propose a novel deep meta-reinforcement learning-based approach to the multi-task offloading problem using a combination of first-order meta-learning and deep Q-learning methods. We establish the meta-generalization bounds for the proposed algorithm and demonstrate that it can reduce the time and energy consumption of IoT applications by up to 15%. Through rigorous simulations, we show that our method achieves near-optimal offloading solutions while also being able to adapt to dynamic edge-cloud environments.

I. Introduction

With the advent of Internet of Things (IoT) and rapid advancements in communication technologies, there has been an unprecedented growth in the volume of data generated by IoT and end-user devices. AI-enabled Intelligent IoT devices have increased demand for computational resources while also trying to operate under stringent latency and capacity constraints which cannot be adequately addressed by a conventional centralized cloud computing architecture [1]. Edge computing emerges as an extension of cloud computing which shifts the function of cloud services to the proximity of end users. Edge caching, edge training, edge inference, and edge offloading are four fundamental components of edge intelligence [2]. Edge offloading helps to migrate complex and computation-intensive tasks to nearby cloudlets by utilizing powerful decision-making capabilities [3] of edge intelligence.