AI-Driven Optimization of Wavelength-Switched Optical Interconnects for Low-Latency Data Center Networks
Keywords:
wavelength switching, optical interconnects, data center networks, artificial intelligence, low latency, hollow core fiber, clock synchronization, network governance, sustainability, optical switchingAbstract
The rapid expansion of cloud services, artificial intelligence workloads, and latency-sensitive applications has intensified pressure on data center networks to deliver extreme bandwidth with predictable low latency. Electrical packet switching, although highly mature and flexible, faces growing constraints in radix, energy per bit, and tail latency as port speeds and fabric scales increase. Wavelength-switched optical interconnects offer a promising architectural alternative by exploiting wavelength division multiplexing, reconfigurable optical switching, and circuit-like paths that can bypass contention and queuing in electronic fabrics. However, optical switching introduces new challenges in control complexity, synchronization, traffic prediction, fault management, and resource allocation. This paper examines how artificial intelligence can optimize wavelength-switched optical interconnects for low-latency data center networks from a system-level perspective. It analyzes architectural trade-offs between electrical and optical fabrics, discusses the role of machine learning in traffic forecasting, wavelength assignment, switch reconfiguration, and telemetry-driven control, and considers deployment governance, sustainability, robustness, fairness, and policy implications. Special attention is given to clock synchronization and hollow-core fiber as emerging enablers of low-latency wavelength-switched intra-data center interconnects. The paper argues that AI-driven optimization must be understood not merely as a control-layer innovation but as a socio-technical capability that shapes infrastructure economics, operational accountability, tenant fairness, and long-term sustainability.
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