Aug 2026· Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication· 1 citation· 18 references
Computer Science
TL;DR
Evaluation results show that, while these mechanisms consume a small amount of link bandwidth, CBFC can greatly reduce receive buffer utilization, and LLR can substantially mitigate network performance degradation caused by packet corruption.
Abstract
The ever-increasing demand for network performance in large language models promotes the advent of many Scale-up networking schemes. To consistently deliver superior low latency and high bandwidth, these schemes have widely adopted Credit-based Flow Control (CBFC) and Link Layer Retry (LLR) to ensure link-level lossless transmission. However, there is currently a lack of evaluations for these mechanisms in Scale-up domains. This paper builds an FPGA-based Scale-up network to evaluate these lossless mechanisms. Evaluation results show that, while these mechanisms consume a small amount of link bandwidth, CBFC can greatly reduce receive buffer utilization, and LLR can substantially mitigate network performance degradation caused by packet corruption.
Due to the high operational complexity and limited deployment scale of lossless RDMA networks, the community has been exploring efficient RDMA communication over lossy fabrics. State-of-the-art (SOTA) lossy RDMA solutions implement a simplified selective repeat mechanism in RDMA NICs (RNICs) to enhance loss recovery ef...
Wenxue Li, Xiangzhou Liu, Yunxuan Zhang et al.· IEEE Transactions on Network...· 0 citations
This work introduces CSIG, a protocol that delivers precise, multi-bit bottleneck congestion signals via a fixed-length Ethernet header, and proposes Fast Ramp-Up, a congestion control primitive that leverages these bottleneck signals to reduce median RPC latency by 20% and unclaimed bandwidth by 60% in production.
Abhiram Ravi, Nandita Dukkipati, Weiwu Pang et al.· Conference on Applications,...· 0 citations
A closed-loop control architecture is presented that applies an adaptive safety margin to the network estimates, ensuring the system meets a deadline-constrained reliability target, and it is demonstrated that the system converges towards the target loss rate, even under model mismatch, while also quickly adapting to s...
Moritz Miodek, Marlene Böhmer, T. Herfet· Euromicro Conference on Real...· 0 citations
An experimental evaluation of ALTQ/CBQ demonstrating its sensitivity to a wide range of parameters and link layer driver design issues is attempted and suggestions for effective deployment in real networks are made.
Quanta is presented, a redundancy-aware simulation framework that eliminates repeated execution in packet-level simulation and reduces the dependence of simulation cost on physical network scale.
Jiajun Luan, Hao Li, Yihan Dang et al.· Asia-Pacific Workshop on Net...· 0 citations
This work proposes a deployment strategy that reduces the need to upgrade expensive core routers by using existing networking mechanisms such as BGP communities, SRv6, and priority queues with rate caps, and demonstrates the feasibility of this approach through large scale experiments on the FABRIC testbed.
Harkirat Singh, Fatih Berkay Sarpkaya, Hakan Gulec et al.· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
