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Author

Mingwei Xu

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Book Open access Aug 2026

CubeTrace: Microscopic Network Tracing for Heterogeneous Cloud Gateways

Modern cloud gateways have evolved to include diverse network functions and heterogeneous hardware, such as programmable switches and FPGAs, to handle increasing workloads and minimize forwarding latency. Existing network tracing tools, however, operate primarily at device granularity and cannot pinpoint which function on which hardware component causes packet losses or latency spikes. To bridge this gap, we present CubeTrace, a unified, function-level flow tracing system that enables microscopic tracing inside heterogeneous cloud gateways. CubeTrace standardizes tracing units as cubes across different hardware platforms, regardless of their varied underlying implementations, and operates at flow granularity for reliability reasons. This introduces a new tracing abstraction for heterogeneous gateways while maintaining low overhead. Moreover, the collected flow-cube data by CubeTrace can be decoded into packet-level representations and integrated with well-established distributed tracing frameworks, enabling the use of off-the-shelf analysis tools. Our evaluations demonstrate that CubeTrace introduces minimal overhead, consuming less than 1% of memory resources and adding less than 1% to forwarding latency. Having been deployed in a large-scale cloud gateway, CubeTrace has significantly improved problem localization, reducing resolution times from hours or even days to just minutes.

Yunming Xiao, Yinchao Yang, Jiaqi Zheng et al. · 1 citation
Review Open access Aug 2026

Collective Communication for Distributed LLM Systems: Planning, Runtime Adaptation, and Computation Coordination

This paper presents a tutorial-style, collective-centric taxonomy for collective communication, which organizes recent advances into three layers: communication planning, which generates topology-aware collective schedules; communication execution and adaptation, which maps these schedules onto GPU runtimes and hardware in real clusters; and computation-communication coordination, which turns collective optimization into end-to-end training and inference benefits.

Xuebin Song, Menghao Zhang, Yue Liu et al. · 0 citations
Book Open access Aug 2026

Turbo: Efficiently Serving Long-Context Large Language Models with In-Network Aggregation

This work proposes Turbo, a first-of-its-kind in-network aggregation system that accelerates long-context inference by offloading query broadcast and attention aggregation to switches and introduces a rolling forward scheme that propagates states to enable cross-stage updates.

Ying Wan, Yuchen Xu, Chuwen Zhang et al. · 0 citations