English

DBOS Network Sensing: A Web Services Approach to Collaborative Awareness

Networking and Internet Architecture 2025-10-21 v1 Cryptography and Security Databases Distributed, Parallel, and Cluster Computing Operating Systems

Abstract

DBOS (DataBase Operating System) is a novel capability that integrates web services, operating system functions, and database features to significantly reduce web-deployment effort while increasing resilience. Integration of high performance network sensing enables DBOS web services to collaboratively create a shared awareness of their network environments to enhance their collective resilience and security. Network sensing is added to DBOS using GraphBLAS hypersparse traffic matrices via two approaches: (1) Python-GraphBLAS and (2) OneSparse PostgreSQL. These capabilities are demonstrated using the workflow and analytics from the IEEE/MIT/Amazon Anonymized Network Sensing Graph Challenge. The system was parallelized using pPython and benchmarked using 64 compute nodes on the MIT SuperCloud. The web request rate sustained by a single DBOS instance was >105{>}10^5, well above the required maximum, indicating that network sensing can be added to DBOS with negligible overhead. For collaborative awareness, many DBOS instances were connected to a single DBOS aggregator. The Python-GraphBLAS and OneSparse PostgreSQL implementations scaled linearly up to 64 and 32 nodes respectively. These results suggest that DBOS collaborative network awareness can be achieved with a negligible increase in computing resources.

Keywords

Cite

@article{arxiv.2509.09898,
  title  = {DBOS Network Sensing: A Web Services Approach to Collaborative Awareness},
  author = {Sophia Lockton and Jeremy Kepner and Michael Stonebraker and Hayden Jananthan and LaToya Anderson and William Arcand and David Bestor and William Bergeron and Alex Bonn and Daniel Burrill and Chansup Byun and Timothy Davis and Vijay Gadepally and Michael Houle and Matthew Hubbell and Michael Jones and Piotr Luszczek and Peter Michaleas and Lauren Milechin and Chasen Milner and Guillermo Morales and Julie Mullen and Michel Pelletier and Alex Poliakov and Andrew Prout and Albert Reuther and Antonio Rosa and Charles Yee and Alex Pentland},
  journal= {arXiv preprint arXiv:2509.09898},
  year   = {2025}
}

Comments

8 pages, 10 figures, 37 references, accepted to IEEE HPEC 2025

R2 v1 2026-07-01T05:32:51.832Z