13 papers selected.
PRED: Performance-oriented Random Early Detection for Consistently Stable Performance in Datacenters
Xinle Du, Tong Li 0014, Guangmeng Zhou, Zhuotao Liu et al.
TL;DR — PRED redesigns AQM by making drop probability a direct function of per-flow performance targets rather than queue length, eliminating the instability of classic RED in modern datacenter workloads.
Why notable — RED has been a cornerstone of congestion control for decades; PRED’s performance-centric reformulation challenges a long-held design axiom and demonstrates significantly lower tail latency at scale. It opens the door to intent-driven AQM as a first-class primitive in datacenter switches.
Rajomon: Decentralized and Coordinated Overload Control for Latency-Sensitive Microservices
Jiali Xing, Akis Giannoukos, Paul Loh, Shuyue Wang et al.
TL;DR — Rajomon introduces a token-based, decentralized overload control mechanism that coordinates admission across microservice call graphs without a central bottleneck.
Why notable — Microservice overload propagation is a persistent pain point in production clouds; Rajomon’s approach of spreading load-shedding decisions across the call graph while retaining global coherence is a practical and principled contribution. The system is evaluated on realistic cloud benchmarks and shows clear SLO improvement over prior centralized and uncoordinated schemes.
Unlocking ECMP Programmability for Precise Traffic Control
Yadong Liu, Yunming Xiao, Xuan Zhang, Weizhen Dang et al.
TL;DR — This work exposes fine-grained ECMP programming interfaces that allow operators to steer individual flows through specific paths in multipath datacenter fabrics with near-zero overhead.
Why notable — ECMP’s coarse hashing has been a limiting factor in datacenter traffic engineering for years; this paper shows that commodity switch primitives can be composed to achieve precise per-flow placement, delivering measurable improvements in load balance without topology changes.
eTran: Extensible Kernel Transport with eBPF
Zhongjie Chen, Qingkai Meng 0001, ChonLam Lao, Yifan Liu et al.
TL;DR — eTran uses eBPF to let applications plug in custom transport logic—including RDMA-like zero-copy paths—directly into the Linux kernel data path without modifying kernel source.
Why notable — The ability to safely extend kernel networking with eBPF has been widely anticipated; eTran demonstrates that full transport protocols (not just classifiers) can be realised this way, achieving performance competitive with kernel-bypass while retaining OS isolation and deployability.
White-Boxing RDMA with Packet-Granular Software Control
Chenxingyu Zhao, Jaehong Min, Ming Liu 0027, Arvind Krishnamurthy
TL;DR — This paper decomposes RDMA semantics and re-implements them in software at packet granularity, exposing hooks that allow transport policy (retransmission, congestion, multipath) to be modified without hardware changes.
Why notable — RDMA’s black-box nature has long frustrated datacenter operators trying to deploy custom congestion control or network telemetry; white-boxing RDMA at packet granularity is a principled answer that simultaneously improves debuggability and extensibility, with demonstrated line-rate performance.
NDD: A Decision Diagram for Network Verification
Zechun Li, Peng Zhang 0011, Yichi Zhang, Hongkun Yang
TL;DR — NDD is a new symbolic data structure that compactly represents the forwarding behavior of large networks, enabling network verification queries orders of magnitude faster than BDD-based predecessors.
Why notable — Network verification tools like Batfish and ARC rely on set representations whose size can explode on real networks; NDD’s topology-aware decomposition dramatically reduces verification time for common queries (reachability, loop detection) and is likely to be adopted as a backend in production verification pipelines.
VEP: A Two-stage Verification Toolchain for Full eBPF Programmability
Xiwei Wu, Yueyang Feng, Tianyi Huang, Xiaoyang Lu et al.
TL;DR — VEP combines abstract interpretation with deductive verification to prove safety properties of eBPF programs that the Linux kernel verifier rejects, enabling a much wider class of programs to run safely in the kernel.
Why notable — The Linux eBPF verifier’s conservatism blocks many useful programs; VEP’s two-stage approach (lightweight in-kernel check plus offline proof) expands the programmability frontier without weakening safety guarantees, directly affecting every operator who writes eBPF for networking or observability.
MTP: Transport for In-Network Computing
Tao Ji, Rohan Vardekar, Balajee Vamanan, Brent E. Stephens et al.
TL;DR — MTP is a transport protocol co-designed with programmable switches that allows in-network compute operations (aggregation, filtering) to be expressed as first-class transport primitives with reliability and flow-control guarantees.
Why notable — Existing transports treat the network as a dumb pipe and retrofit in-network compute as an afterthought; MTP shows that rethinking transport abstractions around programmable hardware can yield substantial throughput gains for distributed ML and key-value workloads.
State-Compute Replication: Parallelizing High-Speed Stateful Packet Processing
Qiongwen Xu, Sebastiano Miano, Xiangyu Gao, Tao Wang 0088 et al.
TL;DR — This paper introduces a replication model for stateful packet processing that partitions state across multiple pipeline replicas and reconciles updates at line rate, breaking the single-pipeline bottleneck of P4 programs.
Why notable — Stateful P4 programs that require per-flow counters or heavy-hitter detection are bottlenecked by single-instance state; the state-compute replication model is a general technique applicable across telemetry, load balancing, and security use cases, with a prototype running at 100 Gbps.
Efficient Direct-Connect Topologies for Collective Communications
Liangyu Zhao, Siddharth Pal, Tapan Chugh, Weiyang Wang et al.
TL;DR — This paper derives a family of direct-connect network topologies mathematically optimized for the all-reduce and all-to-all collectives used in large-scale ML training, achieving better bandwidth and lower diameter than fat-tree alternatives at the same port count.
Why notable — As AI training clusters scale to thousands of GPUs, interconnect topology becomes a first-order performance determinant; the paper’s analytical framework for topology synthesis fills a long-standing gap between graph theory and practical cluster networking, and the proposed topologies outperform fat-trees on real collective benchmarks.
SimAI: Unifying Architecture Design and Performance Tuning for Large-Scale Large Language Model Training with Scalability and Precision
Xizheng Wang, Qingxu Li, Yichi Xu, Gang Lu et al.
TL;DR — SimAI is a high-fidelity simulator that jointly models compute, network, and storage interactions in LLM training clusters, enabling architects to evaluate topology and parallelism strategy choices without running full cluster experiments.
Why notable — Designing training infrastructure for frontier LLMs is prohibitively expensive to explore empirically; SimAI’s validated accuracy against production Alibaba clusters makes it a practical tool for the community and provides rare quantitative insight into how network architecture choices affect end-to-end training throughput.
Minder: Faulty Machine Detection for Large-scale Distributed Model Training
Yangtao Deng, Xiang Shi, Zhuo Jiang, Xingjian Zhang 0009 et al.
TL;DR — Minder continuously monitors collective-communication timing patterns during LLM training to pinpoint faulty nodes—including subtle slow or flapping hardware—without requiring dedicated diagnostic jobs.
Why notable — GPU cluster faults that cause training slowdowns rather than outright crashes are notoriously hard to attribute; Minder’s passive monitoring approach catches a wide class of hardware anomalies (including network stragglers) with sub-minute detection latency, and its deployment at scale in Alibaba’s training infrastructure gives the results strong operational credibility.
Efficient Multi-WAN Transport for 5G with OTTER
Mary Hogan, Gerry Wan, Yiming Qiu, Sharad Agarwal et al.
TL;DR — OTTER is a multi-path transport system for 5G user-plane traffic that dynamically bonds heterogeneous WAN links (e.g., terrestrial + satellite) while hiding link asymmetry and jitter from applications.
Why notable — 5G deployments increasingly rely on diverse backhaul paths with wildly different characteristics; OTTER’s scheduler, validated on a Microsoft production 5G deployment, demonstrates that significant throughput and reliability gains are achievable through principled multi-WAN design, informing both operator practice and future transport standards.