Network modeling using graph neural networks
(English) Network modeling is central to the field of computer networks. Models are useful in researching new protocols and mechanisms, allowing administrators to estimate their performance before their actual deployment in production networks. Network models also help to find optimal network config...
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| Formato: | tesis doctoral |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/422057 |
| Acesso em linha: | https://hdl.handle.net/2117/422057 https://dx.doi.org/10.5821/dissertation-2117-422057 |
| Access Level: | acceso abierto |
| Palavra-chave: | 004 Àrees temàtiques de la UPC::Informàtica |
| Resumo: | (English) Network modeling is central to the field of computer networks. Models are useful in researching new protocols and mechanisms, allowing administrators to estimate their performance before their actual deployment in production networks. Network models also help to find optimal network configurations, without the need to test them in production networks. Arguably, the most prevalent way to build these network models is through the use of discrete event simulation (DES) methodologies which provide excellent accuracy. State-of-the-art network simulators include a wide range of network, transport, and routing protocols, and are able to simulate realistic scenarios. However, this comes at a very high computational cost that depends linearly on the number of packets being simulated. As a result, they are impractical in scenarios with realistic traffic volumes or large topologies. In addition, and because they are computationally expensive, they do not work well in real-time scenarios. Another network modeling alternative is Queuing Theory (QT) where networks are represented as inter-connected queues that are evaluated analytically. While QT solves the main limitation of DES, it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In this context, Machine Learning (ML) has recently emerged as a practical solution to achieve data-driven models that can learn complex traffic models while being extremely accurate and fast. More specifically, Graph Neural Networks (GNNs) have emerged as an excellent tool for modeling graph-structured data showing outstanding accuracy when applied to computer networks. However, some challenges still persist: 1. Queues and Scheduling Policies: Modeling queues, scheduling policies, and Quality-of-Service (QoS) mappings within GNN architectures poses another challenge, as these elements are fundamental to network behavior. 2. Traffic Models: Accurately modeling realistic traffic patterns, which exhibit strong autocorrelation and heavy tails, remains a challenge for GNN-based solutions. 3. Training and Generalization: ML models, including GNNs, require representative training data that covers diverse network operational scenarios. Creating such datasets from real production networks is unfeasible, necessitating controlled testbeds. The challenge lies in designing GNNs capable of accurate estimation in unseen networks, encompassing different topologies, traffic, and configurations. 4. Generalization to Larger Networks: Real-world networks are often significantly larger than testbeds. Scaling GNNs to handle networks with hundreds or thousands of nodes is a pressing challenge, one that requires leveraging domain-specific network knowledge and novel architectural approaches. This dissertation represents a step forward in harnessing Graph Neural Networks (GNN models) for network modeling, by proposing a new GNN-based architecture with a focus on addressing these critical challenges while being fast and accurate. |
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