基于注意机制的图神经网络 RouteNet 模型性能评估

Future Internet Pub Date : 2024-03-29 DOI:10.3390/fi16040116
Binita Kusum Dhamala, Babu R. Dawadi, Pietro Manzoni, B. K. Acharya
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引用次数: 0

摘要

图表示法是公认的网络建模有效方法,它通过将实体表示为节点,将它们之间的关系表示为边,精确地说明了网络中各种实体之间错综复杂的动态交互。图神经网络(GNN)利用网络图数据的优势和专门用于分析图数据的深度学习技术,有效处理结构化图数据,为性能建模、路由优化和资源分配等各种用例提供精确预测,从而彻底改变了计算机网络领域。利用 GNN 的 RouteNet 模型已被有效地应用于确定计算机网络中每对源到目的地的服务质量(QoS)参数。然而,当前 GNN 模型的一个普遍问题是难以概括和捕捉网络数据中的复杂关系和模式。本研究旨在通过在原始 RouteNet 模型的架构中加入注意力层来增强基于 GNN 模型的预测能力。通过对比分析,评估了修改后的 RouteNet 模型与原始 RouteNet 模型的性能。对添加的注意力层的有效性进行了研究,以确定其对模型整体性能的影响。本研究的成果有助于推进基于 GNN 的网络性能预测,解决现有模型的局限性,并为预测网络延迟提供可靠的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
Graph representation is recognized as an efficient method for modeling networks, precisely illustrating intricate, dynamic interactions within various entities of networks by representing entities as nodes and their relationships as edges. Leveraging the advantage of the network graph data along with deep learning technologies specialized for analyzing graph data, Graph Neural Networks (GNNs) have revolutionized the field of computer networking by effectively handling structured graph data and enabling precise predictions for various use cases such as performance modeling, routing optimization, and resource allocation. The RouteNet model, utilizing a GNN, has been effectively applied in determining Quality of Service (QoS) parameters for each source-to-destination pair in computer networks. However, a prevalent issue in the current GNN model is their struggle with generalization and capturing the complex relationships and patterns within network data. This research aims to enhance the predictive power of GNN-based models by enhancing the original RouteNet model by incorporating an attention layer into its architecture. A comparative analysis is conducted to evaluate the performance of the Modified RouteNet model against the Original RouteNet model. The effectiveness of the added attention layer has been examined to determine its impact on the overall model performance. The outcomes of this research contribute to advancing GNN-based network performance prediction, addressing the limitations of existing models, and providing reliable frameworks for predicting network delay.
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