NetChain: Scale-Free Sub-RTT Coordination

By Xin Jin, Xiaozhou Li, Haoyu Zhang, Nate Foster, Jeongkeun Lee, Robert Soulé, Changhoon Kim, Ion Stoica. Symposium on Networked Systems Design and Implementation, 2018

305 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5fafd836d3f2a2691bc012eb25e06cf80a8abe4d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Anuj Kalia
2. Michael Kaminsky
3. D. Andersen
4. Chon-In Lao
5. Yanfang Le
6. Kshiteej S. Mahajan
7. Yixi Chen
8. Aditya Akella

Citations in the corpus

• ATP: In-network Aggregation for Multi-tenant Learning (2021, background): “These limits lead to small packet sizes for in-network computation and storage applications: the payload size of SwitchML and NetCache is 128B [40, 41, 46, 56] 1.”
• Scaling Distributed Machine Learning with In-Network Aggregation (2019, unknown): “costs, in-network aggregation avoids end-host processing required to perform aggregation and, therefore, provides “sub-RTT” latency [43], which the contrasted approaches cannot achieve.”
• Datacenter RPCs can be General and Fast (2018, methodology): “3 μs, showing that latency with commodity Ethernet NICs and software networking is much lower than the widely-believed value of 10–100 μs [35, 51].”