Today’s network is notorious for its complexity and uncertainty. Network operators often rely on network models for efficient network planning, operation, and optimization. The network model is responsible for understanding the complex relationships between network performance metrics (e.g., delay and jitter) and network characteristics (e.g., traffic and configuration). However, we still lack a systematic approach to developing accurate and lightweight network models that are aware of the impact of network configurations (i.e., expressiveness) and provide fine-grained flow-level temporal predictions (i.e., granularity). In this paper, we propose xNet, a data-driven network modeling framework based on graph neural networks (GNN). It is worth noting that xNet is not a dedicated network model designed for a specific network scenario with constraint considerations. On the contrary, xNet provides a general approach to modeling the network characteristics of concern with relation graph representations and configurable GNN blocks. xNet learns the state transition functions between time steps and rolls them out to obtain the full fine-grained prediction trajectory. We implement and instantiate xNet with three use cases. The experimental results show that xNet can accurately predict different performance metrics (i.e. temporal and steady-state QoS) in different scenarios, with performance comparable to state-of-the-art domain-specific models. Compared with traditional packet-level simulators, xNet achieves a speed improvement of more than two orders of magnitude, demonstrating its promising application in real-time optimization of network configurations.

Today’s network is notorious for its complexity and uncertainty. Network operators often rely on network models to achieve efficient network planning, operation, and optimization. The network model is responsible for understanding the complex relationships between the network performance metrics (e.g., latency) and the network characteristics (e.g., traffic). However, we still lack a systematic approach to developing accurate and lightweight network models that are aware of the impact of network configurations (i.e., expressiveness) and provide fine-grained flow-level temporal predictions (i.e., granularity).In this paper, we propose xNet, a data-driven network modeling framework based on graph neural networks (GNN). Unlike the previous proposals, xNet is not a dedicated network model designed for specific network scenarios with constraint considerations. On the contrary, xNet provides a general approach to modeling the network characteristics of concern with relation graph representations and configurable GNN blocks. xNet learns the state transition function between time steps and rolls it out to obtain the full fine-grained prediction trajectory. We implement and instantiate xNet with three use cases. The experiment results show that xNet can accurately predict different performance metrics while achieving over two orders of magnitude of speedup compared with the conventional packet-level simulator.