DEMO: Optimizing Distributed AI Training: A Quantitative Tool for Bottleneck-Aware Network Design

Repository for the submission for SIGCOMM 2025 Demo.

Useful links and references

• BStruct-Mininet link
• J. Ros-Giralt, A. Bohara, S. Yellamraju, H. Langston, R. Lethin, Y. Jiang, L. Tassiulas, J. Li, Y. Lin, Y. Tan, M. Veeraraghavan, "On the Bottleneck Structure of Congestion-Controlled Networks," ACM SIGMETRICS, Boston, June 2020. link
• Jordi Ros-Giralt, Noah Amsel, Sruthi Yellamraju, James Ezick, Richard Lethin, Yuang Jiang, Aosong Feng, Leandros Tassiulas, Zhenguo Wu, Min Yeh Teh, Keren Bergman, "Designing Data Center Networks Using Bottleneck Structures," Accepted for publication at ACM SIGCOMM 2021. link
• Jordi Ros-Giralt, Noah Amsel, Sruthi Yellamraju, James Ezick, Richard Lethin, Yuang Jiang, Aosong Feng, Leandros Tassiulas, Zhenguo Wu, Min Yeh Teh, Keren Bergman, "A Quantitative Theory of Bottleneck Structures for Data Networks," Oct 2022 link