AI-Driven Traffic Scheduling for Low-Latency Data Center Optical Networks
Keywords:
AI-driven traffic scheduling; data center optical networks; low-latency interconnects; optical circuit switching; reinforcement learning; governance; sustainability; robustness; fairnessAbstract
Data center optical networks are increasingly positioned as the low-latency substrate for distributed computation, storage disaggregation, and artificial intelligence workloads. Yet optical capacity alone does not guarantee predictable latency, because traffic scheduling must reconcile bursty demand, circuit setup delays, wavelength contention, and heterogeneous quality-of-service requirements. This paper examines artificial intelligence driven traffic scheduling for low-latency data center optical networks from a system-level perspective. It argues that scheduling intelligence should not be treated as a centralized optimizer inserted above existing hardware, but as a cross-layer control function embedded in the optical, electrical, and application planes. The discussion covers architectural foundations, including optical circuit switching, wavelength switching, hybrid electrical-optical fabrics, and hollow-core fiber interconnects. It then analyzes how reinforcement learning, predictive demand modeling, and online optimization can reduce tail latency while respecting fairness, robustness, and governance constraints. The paper also considers deployment realities, sustainability pressures, and the economic trade-offs between overprovisioning and fine-grained scheduling. Case illustrations compare intra-data center interconnects with wide-area traffic engineering and elastic optical routing, showing that transferable design principles include state abstraction, safety envelopes, and human oversight. The conclusion emphasizes that low-latency optical scheduling is a socio-technical problem: its success depends not only on algorithmic performance but also on observability, accountability, energy accounting, and institutional mechanisms for resolving conflicting operational priorities.
References
1. Al-Fares, M., Loukissas, A., & Vahdat, A. (2008). A scalable, commodity data center network architecture. ACM SIGCOMM Computer Communication Review, 38(4), 63-74. https://doi.org/10.1145/1402958.1402967
2. Farrington, N., Porter, G., Radhakrishnan, S., Bazzaz, H. H., Subramanya, V., Fainman, Y., Papen, G., & Vahdat, A. (2010). Helios: A hybrid electrical/optical switch architecture for modular data centers. ACM SIGCOMM Computer Communication Review, 40(4), 339-350. https://doi.org/10.1145/1851182.1851223
3. Kachris, C., & Tomkos, I. (2012). A survey on optical interconnects for data centers. IEEE Communications Surveys & Tutorials, 14(4), 1021-1036. https://doi.org/10.1109/SURV.2011.122111.00069
4. 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. https://doi.org/10.1109/TNET.2013.2253120
5. Zhou, Z., Dzieciol, H., Clark, K., Luo, Y., Richardson, D., Poletti, F., ... & Liu, Z. (2023). Low-latency wavelength-switched clock-synchronized intra-data center interconnects enabled by hollow core fiber. Optics Express, 31(15), 24739-24748.
6. Musumeci, F., Rottondi, C., Nag, A., Macaluso, I., Zibar, D., Ruffini, M., & Tornatore, M. (2019). A survey on application of machine learning techniques in optical communication. Journal of Optical Communications and Networking, 11(2), A1-A12. https://doi.org/10.1364/JOCN.11.0000A1
7. Zibar, D., Piels, M., Jones, R., & Schaeffer, C. G. (2016). Machine learning techniques in optical communication. Journal of Lightwave Technology, 34(6), 1442-1452. https://doi.org/10.1109/JLT.2015.2494940
8. Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2016). Resource management with deep reinforcement learning. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks (pp. 50-56). ACM. https://doi.org/10.1145/3005745.3005750
9. Chen, L., Lingys, J., Chen, K., & Liu, F. (2018). AuTO: Scaling deep reinforcement learning for datacenter-scale automatic traffic optimization. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (pp. 191-205). ACM. https://doi.org/10.1145/3230543.3230564
10. Jay, N., Rotman, N., Godfrey, P. B., Schapira, M., & Tamar, A. (2019). A deep reinforcement learning perspective on internet congestion control. In Proceedings of the 36th International Conference on Machine Learning (pp. 3050-3059). PMLR.
11. Xu, Z., Tang, J., Yin, C., Wang, Y., & Xue, G. (2018). Experience-driven networking: A deep reinforcement learning based approach. In IEEE INFOCOM 2018 (pp. 1871-1879). IEEE. https://doi.org/10.1109/INFOCOM.2018.8485853
12. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236
13. Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984-986. https://doi.org/10.1126/science.aba3758
14. Dayarathna, M., Wen, Y., & Fan, R. (2016). Data center energy consumption modeling: A survey. IEEE Communications Surveys & Tutorials, 18(1), 732-794. https://doi.org/10.1109/COMST.2015.2458273
15. Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
16. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679679
17. Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14-29. https://doi.org/10.1080/1369118X.2016.1154087
18. Ballani, H., Costa, P., Behrendt, R., Cletheroe, D., Haller, I., Jozwik, K., ... & Thomsen, B. (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). ACM. https://doi.org/10.1145/3387514.3406221
19. 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. https://doi.org/10.1145/2486001.2486006
20. Chen, X., Guo, J., Zhu, Z., Zhang, R., & He, Y. (2019). DeepRMSA: A deep reinforcement learning framework for routing, modulation and spectrum assignment in elastic optical networks. Journal of Lightwave Technology, 37(16), 4155-4163. https://doi.org/10.1109/JLT.2019.2923615
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
