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Zhaochen Zhang

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Book Open access Aug 2026

Anytest: Localizing the Root Cause of Hardware Transport Performance Anomalies

RoCEv2-based RDMA fabrics are the backbone of modern high-performance data-center workloads, yet large-scale deployments increasingly suffer transport-layer network performance anomalies (NPAs) such as throughput collapse, persistent unfairness, and latency inflation even without link failures. Localizing root causes of NPAs is one of today's hardest operational challenges: RoCEv2 transport logic is offloaded into proprietary NIC/switch hardware with limited observability; available counters miss μs-scale dynamics; and similar symptoms can originate from sender, receiver, or switch behaviors in the tightly coupled RoCEv2 system. We present Anytest, an in-situ black-box testing tool that localizes root causes of transport-layer NPAs on commodity RoCEv2 RNICs and Ethernet switches without re-cabling or hardware modification. Anytest decomposes RoCEv2 network system into logical roles and isolates the hardware under test by emulating the other roles with protocol-correct DPDK endpoints. This enables deterministic injection of transport events and μs-resolution measurements. We overcome non-trivial technical challenges to implement Anytest's DPDK-based endpoints, which realize protocol correctness while enforcing μs-level packet timing at the hardware line rate. Integrated into a trace-reproduce-localize workflow, Anytest has been deployed in production for ~1 year, reducing mean localization effort to 3.1 person-hours.

Zhaochen Zhang, Jiaqi Gao, Sheng Cheng et al. · 0 citations
Conference Jul 2026

STON: Scaling Torus-Based AI Training Clusters via Optical Circuit Switches

Torus networks are deployed in production AI training clusters for their path diversity and low latency, but 2D Torus scales poorly: electrical packet switches compromise latency, and high-dimensional Torus introduces excessive routing complexity. We present STON (Scalable TOrus Network), a hierarchical architecture that treats a 2D Torus as a supernode and interconnects supernodes with a reconfigurable Optical Circuit Switch (OCS) for AlltoAll-dominated large-scale training networks. STON comprises three coordinated modules: (1) fragmentaware task placement, which minimizes inter-supernode traffic by reducing job fragmentation; (2) non-disruptive logical topology mapping, governed by two principles that prevent OCS reconfiguration from disrupting running tasks or partitioning multisupernode jobs; and (3) compute-phase traffic forwarding, which ensures reachability when direct OCS circuits are unavailable. STON reduces average FCT by 42.2%-61.1% across synthetic workloads and by 52.6% on a one-day Kalos production trace (under an AlltoAll traffic model for all jobs), with 95th-percentile tail latency reduced by up to 74.5%, versus a static direct-connect baseline using the same OCS hardware.

Qinwei Yang, Peirui Cao, Ruyi Zhang et al. · 0 citations