Graph Neural Network-Based Traffic Prediction and Topology Optimization for Optical Data Center Networks
Keywords:
graph neural networks; optical data center networks; traffic prediction; topology optimization; reconfigurable optical interconnects; socio-technical infrastructure; sustainability; network governanceAbstract
Optical data center networks are increasingly expected to support workloads that fluctuate rapidly across racks, pods, and geographically distributed facilities. Static or quasi-static topologies often waste capacity during off-peak periods and become congestion bottlenecks during synchronized training, storage migration, or bursty inference workloads. This paper examines how graph neural networks can be used not only for traffic prediction but also for topology optimization in optical data center networks. The central argument is that prediction and reconfiguration should be treated as a coupled socio-technical control problem rather than as separate machine learning and networking tasks. Graph representations can encode racks, switches, transceivers, wavelength channels, and service dependencies as interrelated entities, while temporal graph learning can capture evolving demand patterns. When integrated with reconfigurable optical switching, such models may inform circuit provisioning, wavelength assignment, load balancing, and failure-aware rerouting. However, the paper also emphasizes structural trade-offs involving latency, energy, hardware complexity, telemetry overhead, fairness across tenants, and governance of automated infrastructure. Through conceptual analysis, cross-domain comparison, and deployment-oriented discussion, the paper identifies architectural requirements for robust and sustainable optical data center control. It concludes that graph-based intelligence is most valuable when embedded within transparent, auditable, and policy-aware orchestration frameworks that balance performance gains against operational risk, tenant equity, and long-term infrastructure resilience.
References
1. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
2. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. In International Conference on Learning Representations.
3. Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems (pp. 1024–1034).
4. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24.
5. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57–81.
6. Yu, B., Yin, H., & Zhu, Z. (2018). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (pp. 3634–3640).
7. Zhou, Zichuan, et al. "Low-latency wavelength-switched clock-synchronized intra-data center interconnects enabled by hollow core fiber." Optics Express 31.15 (2023): 24739-24748.
8. Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H. (2020). T-GCN: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 21(9), 3848–3858.
9. Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019). Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 1, pp. 922–929).
10. Chen, C., Li, K., Teo, S. G., Zou, X., Wang, K., Wang, J., & Zeng, Z. (2019). Gated residual recurrent graph neural networks for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 1, pp. 485–492).
11. Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International Conference on Learning Representations.
12. Wang, X., Ma, Y., Wang, Y., Jin, W., Wang, X., Tang, J., Jia, C., & Yu, J. (2020). Traffic flow prediction via spatial temporal graph neural network. In Proceedings of The Web Conference 2020 (pp. 1082–1092).
13. Kachris, C., & Tomkos, I. (2012). A survey on optical interconnects for data centers. IEEE Communications Surveys & Tutorials, 14(4), 1021–1036.
14. Zhou, D., & Yi, X. (2026). DISTRIBUTED AI-ASSISTED RISK ASSESSMENT FOR ZERO-TRUST MICROSERVICES: A TAXONOMY AND CONCEPTUAL FRAMEWORK. AI and Data Science Journal, 3(2), 1-13.
15. Chen, K., Singla, A., Singh, A., Ramachandran, K., Xu, L., Zhang, Y., Wen, X., & Chen, Y. (2014). OSA: An optical switching architecture for data center networks with unprecedented flexibility. IEEE/ACM Transactions on Networking, 22(2), 498–511.
16. Mellette, W. M., McGuinness, R., Roy, A., Forencich, A., Papen, G., Snoeren, A. C., & Porter, G. (2017). RotorNet: A scalable, low-complexity, optical datacenter network. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication (pp. 267–280).
17. Ballani, H., Costa, P., Behrendt, R., Cletheroe, D., Haller, I., Jozwik, K., Karinou, F., Lange, S., Li, K., Mellette, W. M., Pappa, G., Popa, D., Sun, Y., Thomsen, B., & Williams, C. (2020). Sirius: A flat datacenter network with nanosecond optical switching. In Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication (pp. 782–797).
18. Porter, G., Strong, R., Farrington, N., Forencich, A., Chen-Sun, P., Rosing, T., & Vahdat, A. (2013). Integrating microsecond circuit switching into the data center. ACM SIGCOMM Computer Communication Review, 43(4), 447–458.
19. Jiang, W., & Luo, J. (2022). Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 207, 117921.
20. Kachris, C., Kanonakis, K., & Tomkos, I. (2013). Optical interconnection networks in data centers: Recent trends and future challenges. IEEE Communications Magazine, 51(9), 39–45.
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