datacenter network operators

Abstract: Datacenter congestion control (CC) and load balancing (LB) evolve rapidly as workload demands change, but deployment depends on RDMA hardware and transport interfaces. Emerging programmable RDMA engines tie policy code to vendor APIs. Operators must therefore port each policy for every platform they deploy. We address this with PCM, a programming model that separates portable congestion management from platform integration on datacenter NICs. Developers write event-driven handlers that they can debug in simulation and reuse on real hardware. PCM exposes flow telemetry as signals and uses triggers to support per-event reactions and coarser responses to accumulated feedback. Vendors provide optimized backends that map signals and output controls to native NIC mechanisms with defined feedback and control semantics. We demonstrate PCM's expressivity by implementing eleven well-known datacenter CC and LB algorithms. We deploy PCM across four distinct datapaths: an Ultra Ethernet simulator, CPU-based UCCL, and on-NIC offloads on NVIDIA ConnectX-8 and Microsoft 1P. On CX-8, PCM-Swift achieves processing capacity equivalent to one reaction per three 4 KiB packets at 800 Gbit/s, using one-fifth of the NIC's hardware threads. In UCCL, it matches native Swift's 1.56 Tbit/s goodput with 64 KiB chunk-level control. PCM's design informed ongoing Ultra Ethernet work toward a portable congestion management interface.

Abstract: Recent advances in AI models and agentic technologies make AI for network operations (NetOps) within reach. However, scalability remains a key bottleneck of agentic NetOps when analyzing hyperscale networks, which comprise hundreds of datacenters, each housing thousands of network devices. The scalability challenge is rooted in the requirement of many NetOps tasks that must conduct global reasoning on how a local change of device behavior affects all relevant routing paths, known as routing-path analysis. This paper studies this scalability problem and evaluates how different agentic approaches, namely in-context learning, iterative reasoning, and agent skills, can scale routing-path analysis to large, complex networks. We present RoutingBench for evaluating agentic routing-path analysis, with varying network size and complexity, for various types of device changes. Our results show that agentic analysis is promising: agent skills curated with a principle termed "explore more; digest less" enables routing-path analysis on hyperscale networks of 50K routers with an accuracy of 99.5%, significantly outreaching the scalability of traditional symbolic analysis. Meanwhile, RoutingBench also reveals the boundary of AI agent capability on complex inter-datacenter networks and compound changes, posing open challenges for AI and agentic research.

Abstract: Reconfigurable optical data center networks (RODCNs) have emerged as a promising solution for scaling DCN capacity, yet their scheduling mechanisms remain a performance bottleneck: traffic-oblivious schemes inherently limit throughput, while the state-of-the-art traffic-aware scheme, NegotiaToR, uses single-iteration iSLIP as its scheduling engine, which limits throughput to around 60% and treats all source-destination pairs with equal priority regardless of queue length. We propose QPS-ToR, which replaces NegotiaToR's scheduling logic with SW-QPS, a sliding-window algorithm originally proposed for crossbar scheduling that achieves around 90% throughput with a single low-complexity iteration. QPS-ToR operates within NegotiaToR's existing workflow, requiring only a revision of the scheduling cycle from three-step Request-Grant-Accept (RGA) to two-step Request-Grant (RG) with a sliding window mechanism. In flow-level simulations with 128 ToRs under realistic datacenter workloads, QPS-ToR achieves up to 36% higher throughput and 82% lower flow completion time (FCT) compared to NegotiaToR, and consistently outperforms RotorNet, a representative traffic-oblivious scheme, on the parallel network topology.