Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Networking and Internet Architecture

arXiv:2309.02464 (cs)
[Submitted on 4 Sep 2023 (v1), last revised 8 Dec 2023 (this version, v2)]

Title:Deployment of Real-Time Network Traffic Analysis using GraphBLAS Hypersparse Matrices and D4M Associative Arrays

Authors:Michael Jones, Jeremy Kepner, Andrew Prout, Timothy Davis, William Arcand, David Bestor, William Bergeron, Chansup Byun, Vijay Gadepally, Micheal Houle, Matthew Hubbell, Hayden Jananthan, Anna Klein, Lauren Milechin, Guillermo Morales, Julie Mullen, Ritesh Patel, Sandeep Pisharody, Albert Reuther, Antonio Rosa, Siddharth Samsi, Charles Yee, Peter Michaleas
View PDF HTML (experimental)
Abstract:Matrix/array analysis of networks can provide significant insight into their behavior and aid in their operation and protection. Prior work has demonstrated the analytic, performance, and compression capabilities of GraphBLAS (this http URL) hypersparse matrices and D4M (this http URL) associative arrays (a mathematical superset of matrices). Obtaining the benefits of these capabilities requires integrating them into operational systems, which comes with its own unique challenges. This paper describes two examples of real-time operational implementations. First, is an operational GraphBLAS implementation that constructs anonymized hypersparse matrices on a high-bandwidth network tap. Second, is an operational D4M implementation that analyzes daily cloud gateway logs. The architectures of these implementations are presented. Detailed measurements of the resources and the performance are collected and analyzed. The implementations are capable of meeting their operational requirements using modest computational resources (a couple of processing cores). GraphBLAS is well-suited for low-level analysis of high-bandwidth connections with relatively structured network data. D4M is well-suited for higher-level analysis of more unstructured data. This work demonstrates that these technologies can be implemented in operational settings.
Comments: Accepted to IEEE HPEC, 8 pages, 8 figures, 1 table, 69 references. arXiv admin note: text overlap with arXiv:2203.13934. text overlap with arXiv:2309.01806
Subjects: Networking and Internet Architecture (cs.NI); Social and Information Networks (cs.SI)
Cite as: arXiv:2309.02464 [cs.NI]
  (or arXiv:2309.02464v2 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2309.02464
Related DOI: https://doi.org/10.1109/HPEC58863.2023.10363581

Submission history

From: Jeremy Kepner [view email]
[v1] Mon, 4 Sep 2023 20:31:50 UTC (473 KB)
[v2] Fri, 8 Dec 2023 21:27:29 UTC (333 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.NI
< prev   |   next >
new | recent | 2023-09
Change to browse by:
cs
cs.SI

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)