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A service-agnostic method for predicting service metrics in real time
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2680-9065
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2018 (English)In: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, Vol. 28, no 2, article id e1991Article in journal (Refereed) Published
Abstract [en]

We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real-time, client-side service metrics for video streaming and key-value store services. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, our method is service agnostic in the sense that it takes as input operating systems and network statistics instead of service-specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the design and implementation of a real-time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning.

Place, publisher, year, edition, pages
Wiley , 2018. Vol. 28, no 2, article id e1991
Keywords [en]
quality of service, cloud computing, real-time network analytics, statistical learning, machine learning
National Category
Telecommunications Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-225070DOI: 10.1002/nem.1991ISI: 000427120900006Scopus ID: 2-s2.0-85029351383OAI: oai:DiVA.org:kth-225070DiVA, id: diva2:1193964
Funder
VINNOVA, 2013-03895
Note

QC 20180328

Available from: 2018-03-28 Created: 2018-03-28 Last updated: 2018-03-28Bibliographically approved

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Yanggratoke, RerngvitStadler, Rolf

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf