In-network computing: solutions for graph analytics
Autor
Prof. Robert Soulé
USI
This project made several advances in the analysis of large graphs (networks) using In-network computing, namely the processing of data while in transit and before storage.
The initial project proposed exploring data structures for doing efficient transformations on graph data. However, during the course of our study, we shifted in a research direction that was not anticipated in the original project plan: exploring and evaluating uses of in-network computing. In-network computing is a special form of hardware acceleration in which programmable network hardware is used to accelerate or offload application logic.
This shift proved to be quite fruitful, as we had a number of strong research results, including publications at well-respected scientific venues, including SOSR, HotNets, NSDI, and CoNEXT. Our work was able to demonstrate that in-network computing can achieve significant performance improvements for distributed applications, sometimes up to five orders of magnitude increases in throughput. One highlight was that our publication on Packet Subscriptions, which accelerated publish-subscribe communication patterns, received the Best Paper award at CoNEXT 2020.