13 papers selected.

MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs

Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang et al.

TL;DR — ByteDance’s full production account of training LLMs at 10,000+ GPUs, with novel co-design of the network stack, fault tolerance, and collective communication to sustain near-linear scaling.

Harmony: A Congestion-free Datacenter Architecture

Saksham Agarwal, Qizhe Cai, Rachit Agarwal 0001, David B. Shmoys et al.

TL;DR — Proposes rethinking datacenter fabrics to eliminate congestion by construction rather than managing it reactively, achieving line-rate throughput without per-packet feedback from Cornell and Google.

DINT: Fast In-Kernel Distributed Transactions with eBPF

Yang Zhou 0008, Xingyu Xiang, Matthew Kiley, Sowmya Dharanipragada et al.

TL;DR — Demonstrates that eBPF programs executing entirely inside the kernel can enforce linearizable, ACID-compliant distributed transactions at dramatically lower latency than user-space approaches.

Making Kernel Bypass Practical for the Cloud with Junction

Joshua Fried, Gohar Irfan Chaudhry, Enrique Saurez, Esha Choukse et al.

TL;DR — Junction (MIT + Microsoft) is the first system to bring full kernel-bypass networking to multi-tenant cloud VMs without requiring application modifications or sacrificing isolation.

SIEVE is Simpler than LRU: an Efficient Turn-Key Eviction Algorithm for Web Caches

Yazhuo Zhang, Juncheng Yang, Yao Yue, Ymir Vigfusson et al.

TL;DR — Surprising finding that a single-queue eviction policy requiring almost no state changes outperforms LRU and all modern approximations on production web-cache workloads.

A large-scale deployment of DCTCP

Abhishek Dhamija, Balasubramanian Madhavan, Hechao Li, Jie Meng et al.

TL;DR — A rare, candid production report from Meta on deploying DCTCP at hyperscale, surfacing unexpected interactions with heterogeneous hardware, cross-traffic, and operational constraints.

Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed

Jinzhu Yan, Haotian Xu, Zhuotao Liu, Qi Li 0002 et al.

TL;DR — Executes neural-network inference for traffic classification directly in the programmable switch data plane at line rate, eliminating the round-trip to a CPU-based classifier.

Horus: Granular In-Network Task Scheduler for Cloud Datacenters

Parham Yassini, Khaled Diab 0001, Saeed Mahloujifar, Mohamed Hefeeda

TL;DR — Offloads microsecond-granularity task scheduling decisions into programmable switches, reducing load-balancing latency by orders of magnitude compared to software schedulers.

CAPA: An Architecture For Operating Cluster Networks With High Availability

Bingzhe Liu, Colin Scott, Mukarram Tariq, Andrew D. Ferguson et al.

TL;DR — Google’s production architecture for maintaining five-nines availability in Jupiter-scale cluster networks, detailing how control-plane redundancy and fast failover are achieved in practice.

Revisiting Congestion Control for Lossless Ethernet

Yiran Zhang, Qingkai Meng 0001, Chaolei Hu, Fengyuan Ren

TL;DR — Identifies fundamental flaws in PFC-based lossless Ethernet that cause cascading head-of-line blocking and proposes a redesigned congestion control that avoids them.

Sirius: Composing Network Function Chains into P4-Capable Edge Gateways

Jiaqi Gao, Jiamin Cao, Yifan Li, Mengqi Liu 0001 et al.

TL;DR — Alibaba’s production system for composing arbitrary NF chains into P4-programmed edge gateways, replacing a fleet of dedicated middleboxes and cutting per-packet processing cost significantly.

CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters

Sudarsanan Rajasekaran, Manya Ghobadi, Aditya Akella

TL;DR — Shows that ignoring network topology when scheduling ML training jobs causes severe collective-communication contention, and that topology-aware co-scheduling yields substantial throughput gains.

Cloudcast: High-Throughput, Cost-Aware Overlay Multicast in the Cloud

Sarah Wooders, Shu Liu, Paras Jain 0001, Xiangxi Mo et al.

TL;DR — Berkeley/Penn system that builds overlay multicast trees across cloud regions optimized for egress cost, enabling high-throughput data dissemination at a fraction of unicast cloud egress fees.