Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning (2008.02033v5)

Abstract: Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently offload heterogeneous tasks of mobile applications from user equipment (UE) to MEC hosts. Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts. However, these methods have weak adaptability to new environments because they have low sample efficiency and need full retraining to learn updated policies for new environments. To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples. We model mobile applications as Directed Acyclic Graphs (DAGs) and the offloading policy by a custom sequence-to-sequence (seq2seq) neural network. To efficiently train the seq2seq network, we propose a method that synergizes the first order approximation and clipped surrogate objective. The experimental results demonstrate that this new offloading method can reduce the latency by up to 25% compared to three baselines while being able to adapt fast to new environments.

• The paper introduces MRLCO, a Meta Reinforcement Learning method that quickly adapts task offloading in MEC with high sample efficiency.
• The approach models offloading as a sequence prediction problem using a custom seq2seq neural network and decomposes learning across multiple MDPs.
• Experimental results show up to a 25% reduction in latency compared to DRL and heuristic methods in dynamic MEC environments.

Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning

The paper "Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning" presents a novel approach to improve task offloading in Multi-access Edge Computing (MEC) systems using Meta Reinforcement Learning (MRL). By extending computational resources to the network edge, MEC aims to reduce service latency and network congestion, which is critical given the increasing demands of mobile applications. The primary challenge addressed is the need for efficient offloading of heterogeneous tasks from User Equipment (UE) to MEC hosts.

Core Contributions and Methodology

This work proposes a method that models task offloading as a sequence prediction problem using a sequence-to-sequence (seq2seq) neural network. The tasks of mobile applications are structured as Directed Acyclic Graphs (DAGs) which are then used to adapt the offloading policy efficiently. The proposed technique leverages MRL to allow rapid adaptation to new scenarios with minimal retraining, achieving better sample efficiency compared to traditional Deep Reinforcement Learning (DRL) approaches.

Key contributions include:

1. Meta Reinforcement Learning-Based Method (MRLCO): Introduces an MRL-based methodology known as MRLCO for fast adaptation to dynamic offloading scenarios in MEC. The method exhibits high sample efficiency for learning new tasks, enabling training with limited data and computational resources.
2. MDP Decomposition: A novel approach is introduced to decompose the learning of offloading policies across multiple Markov Decision Processes (MDPs). This entails learning an overarching meta-policy and rapidly adapting a specific policy for each MDP based on this meta-policy.
3. Seq2Seq Neural Network: A custom seq2seq neural network architecture is implemented to model offloading decisions as sequence predictions, enriching the policy network with attention mechanisms to handle varying task structures encoded in DAGs.
4. Training Optimization: The paper utilizes a training strategy that combines first-order MRL with a clipped surrogate objective, optimizing the stability and efficiency of the training process.

Experimental Evaluation

The experimental results underscore the efficacy of MRLCO in reducing latency by up to 25% compared to baseline methods, including conventional DRL and heuristic algorithms like HEFT and Greedy approaches. The experiments simulated various MEC environments with changes in DAG topologies, task numbers, and wireless transmission rates, demonstrating the robustness of the proposed solution. Furthermore, MRLCO showcases rapid convergence to effective offloading strategies with few gradient updates, a significant improvement over methods that require extensive retraining when conditions change.

Implications and Future Directions

The implications of this research are significant for the MEC landscape, providing a framework for rapidly deploying task offloading solutions that adapt to evolving network conditions and application demands. This is particularly crucial as mobile applications become more complex and varied.

Looking ahead, the research opens avenues for further exploration of MRL techniques in other MEC challenges, like dynamic resource allocation or real-time application adaptation. The combination of MRL with powerful neural architectures sets the stage for more generalized and efficient learning frameworks, potentially leading to autonomous MEC systems that can handle a broader range of applications and network scenarios with minimal human intervention.

In conclusion, this paper makes a substantial contribution to the field of edge computing by marrying advanced machine learning techniques with practical MEC challenges, charting a course for more intelligent and adaptive edge computing frameworks.