Energy-Efficient Optical Switching for Large-Scale AI Computing Clusters Using Adaptive Wavelength Allocation
Keywords:
optical switching, adaptive wavelength allocation, AI computing clusters, energy efficiency, data center infrastructure, sustainability, governance, multi-tenant fairnessAbstract
Large-scale artificial intelligence computing clusters are increasingly constrained by the energy and bandwidth demands of dense accelerator-to-accelerator communication. Optical switching offers a promising path beyond electrical packet switching, but its benefits depend on how wavelengths are allocated across tenants, workloads, and time. This paper examines energy-efficient optical switching for large-scale AI clusters through adaptive wavelength allocation, treating wavelength assignment as a system-level control problem rather than a purely physical-layer concern. It argues that adaptive allocation can reduce energy per useful computation by aligning optical resources with traffic dynamics, yet it also introduces structural trade-offs involving latency, fairness, robustness, infrastructure, and governance. The paper analyzes architectural implications for reconfigurable optical fabrics, wavelength-selective switching, clock synchronization, and hollow-core fiber, and it connects these technical choices to sustainability metrics, rebound effects, and policy accountability. It further considers multi-tenant fairness, failure modes, carbon-aware scheduling, and deployment pathways informed by power grids, spectrum governance, and interconnection network design. The central conclusion is that adaptive wavelength allocation can improve the energy efficiency of AI clusters only when paired with transparent telemetry, arbitration rules, lifecycle accounting, and resilient control mechanisms. Without such socio-technical safeguards, efficiency gains may be captured by incremental demand, hidden in operational complexity, or distributed inequitably across users and regions.
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