TimeGNN-Augmented Hybrid-Action MARL for Fine-Grained Task Partitioning and Energy-Aware Offloading in MEC

2026-01-08

arXiv.org

AI Summary

This work addresses the challenges of task scheduling and resource allocation in mobile edge computing, where limited resources, unstable power supply, and high system dynamics pose significant difficulties. To tackle these issues, the authors propose TG-DCMADDPG, a multi-agent reinforcement learning framework that integrates a Temporal Graph Neural Network (TimeGNN) to model multidimensional temporal states and introduces a discrete-continuous hybrid action space within a multi-agent deep deterministic policy gradient algorithm. This approach jointly optimizes fine-grained task partitioning, transmission power allocation, and scheduling priorities. Experimental results demonstrate that the proposed method significantly accelerates policy convergence, improves energy efficiency and latency performance, enhances task completion rates, and exhibits strong scalability and practical applicability.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

With the rapid growth of IoT devices and latency-sensitive applications, the demand for both real-time and energy-efficient computing has surged, placing significant pressure on traditional cloud computing architectures. Mobile edge computing (MEC), an emerging paradigm, effectively alleviates the load on cloud centers and improves service quality by offloading computing tasks to edge servers closer to end users. However, the limited computing resources, non-continuous power provisioning (e.g., battery-powered nodes), and highly dynamic systems of edge servers complicate efficient task scheduling and resource allocation. To address these challenges, this paper proposes a multi-agent deep reinforcement learning algorithm, TG-DCMADDPG, and constructs a collaborative computing framework for multiple edge servers, aiming to achieve joint optimization of fine-grained task partitioning and offloading. This approach incorporates a temporal graph neural network (TimeGNN) to model and predict time series of multi-dimensional server state information, thereby reducing the frequency of online interactions and improving policy predictability. Furthermore, a multi-agent deterministic policy gradient algorithm (DC-MADDPG) in a discrete-continuous hybrid action space is introduced to collaboratively optimize task partitioning ratios, transmission power, and priority scheduling strategies. Extensive simulation experiments confirm that TG-DCMADDPG achieves markedly faster policy convergence, superior energy-latency optimization, and higher task completion rates compared with existing state-of-the-art methods, underscoring its robust scalability and practical effectiveness in dynamic and constrained MEC scenarios.

Problem

• Mobile Edge Computing
• Task Offloading
• Energy Efficiency
• Fine-Grained Task Partitioning
• Dynamic Resource Allocation

Innovation

• TimeGNN
• Hybrid-Action MARL
• Fine-Grained Task Partitioning
• Energy-Aware Offloading
• MEC