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Towards Machine Learning Inference in the Data Plane
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Recently, machine learning has been considered an important tool for various networkingrelated use cases such as intrusion detection, flow classification, etc. Traditionally, machinelearning based classification algorithms run on dedicated machines that are outside of thefast path, e.g. on Deep Packet Inspection boxes, etc. This imposes additional latency inorder to detect threats or classify the flows.With the recent advance of programmable data planes, implementing advanced function-ality directly in the fast path is now a possibility. In this thesis, we propose to implementArtificial Neural Network inference together with flow metadata extraction directly in thedata plane of P4 programmable switches, routers, or Network Interface Cards (NICs).We design a P4 pipeline, optimize the memory and computational operations for our dataplane target, a programmable NIC with Micro-C external support. The results show thatneural networks of a reasonable size (i.e. 3 hidden layers with 30 neurons each) can pro-cess flows totaling over a million packets per second, while the packet latency impact fromextracting a total of 46 features is 1.85μs.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Machine learning Data Plane SmartNIC Artificial Neural Network Inference Flow Classification
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kau:diva-72875OAI: oai:DiVA.org:kau-72875DiVA, id: diva2:1328601
Subject / course
Computer Science
Educational program
Engineering: Computer Engineering (300 ECTS credits)
Presentation
2019-06-03, 10:00 (English)
Supervisors
Examiners
Available from: 2019-06-26 Created: 2019-06-21 Last updated: 2019-06-27Bibliographically approved

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fulltext(738 kB)110 downloads
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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Language
  • de-DE
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  • en-US
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More languages
Output format
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