Ultra-fast Optical Network Throughput Prediction using Graph Neural Networks
Ultra-fast Optical Network Throughput Prediction using Graph Neural Networks
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DOI：
10.23919/ondm54585.2022.9782853
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
R. Matzner;Ruijie Luo;G. Zervas;P. Bayvel
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文献类型：
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作者：
R. Matzner;Ruijie Luo;G. Zervas;P. Bayvel
光网络的关键性能指标之一是最大可达吞吐量。然而，确定它是一个NP难优化问题，通常通过计算昂贵的整数线性规划（ILP）公式来解决。与顺序加载结合的启发式是可扩展的，但不精确。因此，需要光网络的超快速性能评估。本文首次提出了消息传递神经网络（MPNN）来研究光网络的结构与最大可达吞吐量之间的关系。我们证明了MPNN可以准确地预测最大可实现的吞吐量，同时减少了5个数量级的ILP相比，计算时间。
One of the key performance metrics for optical networks is the maximum achievable throughput. Determining it however, is an NP-hard optimisation problem, often solved via computationally expensive integer linear programming (ILP) formulations. Heuristics, in conjunction with sequential loading, are scalable but non-exact. There is, thus, a need for ultra-fast performance evaluation of optical networks. For the first time, we propose message passing neural networks (MPNN), to learn the relationship between the structure and the maximum achievable throughput of optical networks. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by 5-orders of magnitude compared to the ILP.