DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing (2008.09930v1)

Abstract: With the continuous growth of mobile data and the unprecedented demand for computing power, resource-constrained edge devices cannot effectively meet the requirements of Internet of Things (IoT) applications and Deep Neural Network (DNN) computing. As a distributed computing paradigm, edge offloading that migrates complex tasks from IoT devices to edge-cloud servers can break through the resource limitation of IoT devices, reduce the computing burden and improve the efficiency of task processing. However, the problem of optimal offloading decision-making is NP-hard, traditional optimization methods are difficult to achieve results efficiently. Besides, there are still some shortcomings in existing deep learning methods, e.g., the slow learning speed and the failure of the original network parameters when the environment changes. To tackle these challenges, we propose a Deep Meta Reinforcement Learning-based offloading (DMRO) algorithm, which combines multiple parallel DNNs with Q-learning to make fine-grained offloading decisions. By aggregating the perceptive ability of deep learning, the decision-making ability of reinforcement learning, and the rapid environment learning ability of meta-learning, it is possible to quickly and flexibly obtain the optimal offloading strategy from the IoT environment. Simulation results demonstrate that the proposed algorithm achieves obvious improvement over the Deep Q-Learning algorithm and has strong portability in making real-time offloading decisions even in time-varying IoT environments.

• The paper introduces DMRO, a meta reinforcement learning algorithm that integrates deep neural networks with Q-learning for efficient task offloading.
• It demonstrates superior performance compared to traditional methods by significantly reducing computational delay and energy consumption.
• The dual-layer design combining meta-learning and reinforcement learning enables rapid adaptation to dynamic IoT environments.

A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing

The paper "DMRO: A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing" addresses the challenge of efficiently making offloading decisions in the Internet of Things (IoT) environments, where resource constraints are a significant concern. This research introduces the DMRO (Deep Meta Reinforcement Learning-based Offloading) algorithm, integrating deep learning, reinforcement learning, and meta-learning to enhance decision-making processes in edge-cloud computing scenarios.

Overview

Background and Motivation

Edge computing emerges as a vital paradigm to alleviate computational burdens on IoT devices by migrating complex tasks to more powerful edge or cloud servers. The primary challenge lies in determining the optimal offloading strategy to minimize both computational delay and energy consumption, an NP-hard problem. Although traditional optimization approaches and conventional deep learning methods have been employed to address this issue, they often suffer from inefficiency, slow learning speed, and lack of adaptability to dynamic environments.

Proposed Framework

The DMRO framework is designed to address these limitations by combining multiple parallel deep neural networks (DNNs) with Q-learning, enhanced by meta-learning capabilities. The proposed architecture consists of two layers:

1. Inner Model: This component employs a distributed deep reinforcement learning mechanism to make real-time, fine-grained task offloading decisions. It leverages multiple parallel DNNs to calculate possible offloading actions, selecting the optimal one based on predefined objective functions that consider delay and energy consumption.
2. Outer Model: This layer introduces meta-learning techniques to improve the adaptability of the neural networks. Through meta-training, the model learns initial parameters that enable rapid adaptation to new environments, enhancing the robustness and efficiency of the decision-making process.

Strong Numerical Results

Simulation results indicate that the DMRO algorithm outperforms traditional approaches such as Deep Q-Network (DQN) methods and full offloading strategies (local-only, edge-only, cloud-only) across various metrics. Particularly, the DMRO framework demonstrates superior efficiency in minimizing the weighted combination of delay and energy consumption.

Implications and Future Directions

Practical Implications

The implementation of the DMRO framework potentially transforms task handling in IoT environments, especially in scenarios with variable computational demands and network conditions. Its ability to quickly adapt to changing environments makes it suitable for real-time applications requiring dynamic resource management.

Theoretical Implications

The integration of meta-learning with reinforcement learning presents a significant advancement in the development of algorithms capable of self-adapting to new challenges. This approach encourages further exploration of meta-learning in other optimization problems within edge computing and beyond.

Speculation on Future Developments

Future research could explore the expansion of the DMRO framework to accommodate scenarios involving multiple edge and cloud servers, adding layers of complexity that reflect real-world applications. Additionally, integrating hardware-specific optimizations and extending the algorithm's adaptability to varying hardware architectures can be crucial areas for continuous innovation.

Overall, the paper lays a solid foundation for intelligent offloading in edge-cloud computing, offering insights that bridge the gap between theoretical research and practical applications in network resource optimization.