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Learning end-to-end application QoS from openflow switch statistics
RISE - Research Institutes of Sweden, ICT, SICS. Federal University of Uberlandia, Brazil; KTH Royal Institute of Technology, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.ORCID-id: 0000-0001-6039-8493
2017 (engelsk)Inngår i: 2017 IEEE Conference on Network Softwarization: Softwarization Sustaining a Hyper-Connected World: en Route to 5G, NetSoft 2017, Institute of Electrical and Electronics Engineers Inc. , 2017Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We use statistical learning to estimate end-to-end QoS metrics from device statistics, collected from a server cluster and an OpenFlow network. The results from our testbed, which runs a video-on-demand service and a key-value store, demonstrate that the learned models can estimate QoS metrics like frame rate or response time with errors bellow 10% for a given client. Interestingly, we find that service-level QoS metrics seem "encoded" in network statistics and it suffices to collect OpenFlow per port statistics to achieve accurate estimation at small overhead for data collection and model computation.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2017.
Emneord [en]
Machine Learning, Network Analytics, Open-Flow, Quality of Service, Software-Defined Networking, Learning systems, Software defined networking, Statistics, Video on demand, Accurate estimation, End-to-end application, Model computation, Open flow, Openflow networks, Openflow switches, Statistical learning, Video on demand services
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Identifikatorer
URN: urn:nbn:se:ri:diva-38065DOI: 10.1109/NETSOFT.2017.8004198Scopus ID: 2-s2.0-85029372779ISBN: 9781509060085 (tryckt)OAI: oai:DiVA.org:ri-38065DiVA, id: diva2:1296509
Konferanse
2017 IEEE Conference on Network Softwarization, NetSoft 2017, 3 July 2017 through 7 July 2017
Tilgjengelig fra: 2019-03-15 Laget: 2019-03-15 Sist oppdatert: 2019-03-19bibliografisk kontrollert

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