Flow Ordering and Hierarchical Bottleneck Identification for High-Speed Data Networks - Phase II Final Report
Under this SBIR Phase II, Reservoir Labs has developed G2 Analytics, a new technology that allows network operators to analyze bottleneck and flow performance with high precision. G2 delivers a new analytical approach and framework to resolve a variety of key problems found in modern communication networks, including: traffic engineering, routing, flow scheduling, network design, capacity planning, resiliency analysis, network slicing, or service level agreement (SLA) management, among others. G2 leverages the bottleneck structure of congestion-controlled communication networks, a recent mathematical discovery by the Reservoir team [RL19b, RL20a, RL20b, RL21a]. Bottleneck structures reveal how perturbations on flows and links propagate through the network, providing an analytical framework to measure (qualitatively and quantitatively) the ripple effects induced as they traverse the network. Leveraging the mathematics of bottleneck structures, Reservoir Labs is developing the G2 technology to provide network operators with a framework to design, optimize and troubleshoot network performance.
- Research Organization:
- Reservoir Labs
- Sponsoring Organization:
- USDOE Advanced Scientific Computing Research (ASCR)
- Contributing Organization:
- Yale University Columbia University
- DOE Contract Number:
- SC0019523
- OSTI ID:
- 1841087
- Type / Phase:
- SBIR (Phase II)
- Report Number(s):
- DOE-RESERVOIR-SC0019523PII
- Resource Relation:
- Related Information: [Res21a] 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," ACM SIGCOMM, New York, August 2021. [Res21b] 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," Technical Report. [RES20b] Noah Amsel, Jordi Ros-Giralt, Sruthi Yellamraju, Brendan von Hofe, Richard Lethin, "Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis," International Workshop on Innovating the Network for Data Intensive Science (INDIS), Supercomputing, Denver, Nov 2020. [RES20a] Jordi Ros-Giralt, Atul Bohara, Sruthi Yellamraju, Harper Langston, Richard Lethin, Yuang Jiang, Leandros Tassiulas, Josie Li, Ying Lin, Yuanlong Tan, Malathi Veeraraghavan, "On the Bottleneck Structure of Congestion-Controlled Networks," ACM SIGMETRICS, Boston, June 2020. [RES22] G2 Source Code
- Country of Publication:
- United States
- Language:
- English
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