GraphBLAS on the Edge: Anonymized High Performance Streaming of Network Traffic
Michael Jones, Jeremy Kepner, Daniel Andersen, Aydin Buluc, Chansup, Byun, K Claffy, Timothy Davis, William Arcand, Jonathan Bernays, David, Bestor, William Bergeron, Vijay Gadepally, Micheal Houle, Matthew Hubbell,, Hayden Jananthan, Anna Klein, Chad Meiners, Lauren Milechin

TL;DR
This paper demonstrates that GraphBLAS enables the construction of anonymized hypersparse traffic matrices at over 50 million packets per second on edge devices, facilitating high-speed, privacy-preserving network analysis.
Contribution
It introduces the use of GraphBLAS for real-time construction of anonymized hypersparse traffic matrices on edge devices, showing high performance in a near worst-case scenario.
Findings
Constructed traffic matrices at over 50 million packets/sec
Performance scales with buffers, threads, and cores
Edge devices can handle high-speed anonymized traffic analysis
Abstract
Long range detection is a cornerstone of defense in many operating domains (land, sea, undersea, air, space, ..,). In the cyber domain, long range detection requires the analysis of significant network traffic from a variety of observatories and outposts. Construction of anonymized hypersparse traffic matrices on edge network devices can be a key enabler by providing significant data compression in a rapidly analyzable format that protects privacy. GraphBLAS is ideally suited for both constructing and analyzing anonymized hypersparse traffic matrices. The performance of GraphBLAS on an Accolade Technologies edge network device is demonstrated on a near worse case traffic scenario using a continuous stream of CAIDA Telescope darknet packets. The performance for varying numbers of traffic buffers, threads, and processor cores is explored. Anonymized hypersparse traffic matrices can be…
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Taxonomy
TopicsDistributed systems and fault tolerance · Advanced Data Storage Technologies · Age of Information Optimization
