Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems

TL;DR

This paper introduces a GNN-based collaborative perception mechanism for distributed systems, enhancing perception accuracy and scheduling efficiency amid dynamic topologies and bandwidth constraints.

Contribution

It proposes a novel GNN-based perception model with message-passing and state-update modules, improving system-wide awareness and adaptive scheduling in distributed environments.

Findings

Outperforms mainstream algorithms in various metrics

Achieves rapid convergence and efficient response to system changes

Enhances perception and scheduling under limited bandwidth

Abstract

This paper addresses the limitations of multi-node perception and delayed scheduling response in distributed systems by proposing a GNN-based multi-node collaborative perception mechanism. The system is modeled as a graph structure. Message-passing and state-update modules are introduced. A multi-layer graph neural network is constructed to enable efficient information aggregation and dynamic state inference among nodes. In addition, a perception representation method is designed by fusing local states with global features. This improves each node's ability to perceive the overall system status. The proposed method is evaluated within a customized experimental framework. A dataset featuring heterogeneous task loads and dynamic communication topologies is used. Performance is measured in terms of task completion rate, average latency, load balancing, and transmission efficiency. Experimental results show that the proposed method outperforms mainstream algorithms under various conditions, including limited bandwidth and dynamic structural changes. It demonstrates superior perception capabilities and cooperative scheduling performance. The model achieves rapid convergence and efficient responses to complex system states.