Halley: A Predictive Control Plane for Optical AI Data Center Networks
Halley: A Predictive Control Plane for Optical AI Data Center Networks
Halley framework enhances the effective throughput of optical networks in AI data centers through predictive failure and resource control, reducing capacity requirements, and featuring formal guarantees. It includes four components: LightReserve adjusts laser pool scale to provide coverage limits, FoldFit schedules switches within an 11/9 optimal value range, ThermaLead performs one-step advance control on micro-rings, and TriageGuard prioritizes protection for high-risk modules.
Published on Nov 3
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Abstract
Halley is a predictive control framework for AI data center optical networks that improves effective throughput and reduces capacity needs by forecasting failures and controlling resources with formal guarantees.
A frontier AI model is trained in an AI data center, on tens of thousands of accelerators joined by a dedicated network, and every training step ends with all of them exchanging data at once. Between racks, that network is optical, and each of its four layers, from lasers to switches, runs a fixed or after-the-fact policy. Lasers sharing an enclosure, cooling, and power can fail together. At a fixed spare ratio, correlated failure provably caps a pool's availability however large it grows. In this paper, we present a comprehensive predictive control plane framework, called Halley, that reads each resource's next state and acts before the failure occurs. It has four components, each carrying a formal guarantee: LightReserve that sizes the laser pool to provide a coverage bound, FoldFit that schedules the switch within 11/9 of the optimal, ThermaLead that holds a microring with one-step-ahead control, and TriageGuard that protects the highest-risk modules first. Halley gives each layer a predictive controller that defers to the deployed policy when unsure. Composed from simulations at that point, effective goodput, the share of ideal throughput kept, rises from 65.2\% to 76.5\% if a window that exhausts its spares is lost. If only the dark channels' bandwidth is lost, it reaches 67.4\%. Per megawatt, that is up to 1.03--1.17 times the training; for the same training, up to 3.3--14.7\% less data center capacity. Halley's fallback circuits are formally verified and are shown to achieve close timing on a commercial FPGA, and pass a 130~nm signoff.
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