DBOS Network Sensing: A Web Services Approach to Collaborative Awareness
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915560797765632 |
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| author | Lockton, Sophia Kepner, Jeremy Stonebraker, Michael Jananthan, Hayden Anderson, LaToya Arcand, William Bestor, David Bergeron, William Bonn, Alex Burrill, Daniel Byun, Chansup Davis, Timothy Gadepally, Vijay Houle, Michael Hubbell, Matthew Jones, Michael Luszczek, Piotr Michaleas, Peter Milechin, Lauren Milner, Chasen Morales, Guillermo Mullen, Julie Pelletier, Michel Poliakov, Alex Prout, Andrew Reuther, Albert Rosa, Antonio Yee, Charles Pentland, Alex |
| author_facet | Lockton, Sophia Kepner, Jeremy Stonebraker, Michael Jananthan, Hayden Anderson, LaToya Arcand, William Bestor, David Bergeron, William Bonn, Alex Burrill, Daniel Byun, Chansup Davis, Timothy Gadepally, Vijay Houle, Michael Hubbell, Matthew Jones, Michael Luszczek, Piotr Michaleas, Peter Milechin, Lauren Milner, Chasen Morales, Guillermo Mullen, Julie Pelletier, Michel Poliakov, Alex Prout, Andrew Reuther, Albert Rosa, Antonio Yee, Charles Pentland, Alex |
| contents | 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 ${>}10^5$, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DBOS Network Sensing: A Web Services Approach to Collaborative Awareness Lockton, Sophia Kepner, Jeremy Stonebraker, Michael Jananthan, Hayden Anderson, LaToya Arcand, William Bestor, David Bergeron, William Bonn, Alex Burrill, Daniel Byun, Chansup Davis, Timothy Gadepally, Vijay Houle, Michael Hubbell, Matthew Jones, Michael Luszczek, Piotr Michaleas, Peter Milechin, Lauren Milner, Chasen Morales, Guillermo Mullen, Julie Pelletier, Michel Poliakov, Alex Prout, Andrew Reuther, Albert Rosa, Antonio Yee, Charles Pentland, Alex Networking and Internet Architecture Cryptography and Security Databases Distributed, Parallel, and Cluster Computing Operating Systems 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 ${>}10^5$, 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. |
| title | DBOS Network Sensing: A Web Services Approach to Collaborative Awareness |
| topic | Networking and Internet Architecture Cryptography and Security Databases Distributed, Parallel, and Cluster Computing Operating Systems |
| url | https://arxiv.org/abs/2509.09898 |