AI-Enhanced Multiplexing Strategies for Scalable Data Center Interconnects
Keywords:
data center interconnects, multiplexing, artificial intelligence, optical switching, scalability, resource governance, sustainability, network architectureAbstract
Data center interconnects increasingly determine the achievable scale, efficiency, resilience, and sustainability of modern computational infrastructure. As workloads diversify across artificial intelligence training, inference serving, storage disaggregation, in-memory analytics, and latency-sensitive applications, static provisioning and coarse-grained multiplexing no longer suffice. This paper examines how artificial intelligence can enhance multiplexing strategies for scalable data center interconnects from a system-level perspective. It argues that the central challenge is not merely increasing raw link capacity but orchestrating heterogeneous multiplexing domains across frequency, wavelength, space, time, and flow classes under dynamic demand, failure, and policy constraints. The analysis considers architectural trade-offs between electrical and optical switching, centralized and distributed control, deterministic and statistical resource allocation, and hardware-level and software-level adaptation. It further explores governance questions concerning robustness, fairness, operational transparency, energy accounting, and deployment risk. By treating artificial intelligence as a control and inference layer rather than a substitute for sound interconnect design, the paper identifies pathways for scalable multiplexing that remain comprehensible, auditable, and sustainable. The conclusion emphasizes that future data center interconnects will require hybrid multiplexing regimes where machine learning anticipates demand, optical fabrics reconfigure rapidly, and policy mechanisms preserve service-level guarantees across tenants and applications.
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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.
