Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Predicting service metrics for cluster-based services using real-time analytics
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Communication Networks. (Kommunikationsnät, Communication Networks)ORCID iD: 0000-0002-2680-9065
(Ericsson Research, Sweden)
(Swedish Institute of Computer Science (SICS), Sweden)
(Ericsson Research, Sweden)
Show others and affiliations
2015 (English)In: IFIP/IEEE 11th International Conference on Network and Service Management, CNSM 2015, Barcelona, Spain, November 9-13, 2015, IEEE conference proceedings, 2015Conference paper, Published paper (Refereed)
Abstract [en]

Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time, client-side service metrics for a video streaming service running on the cluster. The method is service-agnostic in the sense that it takes as input operating-systems statistics instead of service-level metrics. We show that feature set reduction significantly improves prediction accuracy in our case, while simultaneously reducing model computation time. We also discuss 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
IEEE conference proceedings, 2015.
Keyword [en]
Quality of service, cloud computing, network analytics, statistical learning, machine learning
National Category
Communication Systems Computer Systems Telecommunications Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-172795DOI: 10.1109/CNSM.2015.7367349ISI: 000379333700019Scopus ID: 2-s2.0-84964055190OAI: oai:DiVA.org:kth-172795DiVA: diva2:849585
Conference
IFIP/IEEE 11th International Conference on Network and Service Management, CNSM 2015, Barcelona, Spain, November 9-13, 2015
Funder
VINNOVA, 2013-03895
Note

QC 20151002

Available from: 2015-08-28 Created: 2015-08-28 Last updated: 2016-08-12Bibliographically approved

Open Access in DiVA

fulltext(595 kB)240 downloads
File information
File name FULLTEXT01.pdfFile size 595 kBChecksum SHA-512
fa6f2f2cd58d39be32423267a19456ea9aa76c693826327979202684dc374f2251eea6ef684a888eeab3f5f6733adbfd7ac43038659d6117964b5ca324053066
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopusConference website

Search in DiVA

By author/editor
Yanggratoke, RerngvitStadler, Rolf
By organisation
ACCESS Linnaeus CentreCommunication Networks
Communication SystemsComputer SystemsTelecommunicationsComputer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 240 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 1525 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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