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 from Device Statistics in a Container-Based Environment
KTH, School of Electrical Engineering (EES), Communication Networks.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Service assurance is critical for high-demand services running on telecom clouds. While service performance metrics may not always be available in real time to telecom operators or service providers, service performance prediction becomes an important building block for such a system. However, it is generally hard to achieve. 

In this master thesis, we propose a machine-learning based method that enables performance prediction for services running in virtualized environments with Docker containers. This method is service agnostic and the prediction models built by this method use only device statistics collected from the server machine and from the containers hosted on it to predict the values of the service-level metrics experienced on the client side. 

The evaluation results from the testbed, which runs a Video-on-Demand service using containerized servers, show that such a method can accurately predict different service-level metrics under various scenarios and, by applying suitable preprocessing techniques, the performance of the prediction models can be further improved. 

In this thesis, we also show the design of a proof-of-concept of a Real-Time Analytics Engine that uses online learning methods to predict the service-level metrics in real time in a container-based environment.

Place, publisher, year, edition, pages
2015.
Keyword [en]
Quality of service, analytics, machine learning, container, Docker
National Category
Communication Systems Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-175889OAI: oai:DiVA.org:kth-175889DiVA: diva2:864152
Subject / course
Communication Networks
Educational program
Master of Science - Network Services and Systems
Presentation
2015-10-02, SICS, Kista, Stockholm, 09:48 (English)
Supervisors
Examiners
Available from: 2015-11-10 Created: 2015-10-26 Last updated: 2016-11-15Bibliographically approved

Open Access in DiVA

fulltext(2874 kB)331 downloads
File information
File name FULLTEXT01.pdfFile size 2874 kBChecksum SHA-512
e4a590a539c4cbe34a1c107dc0122a933b5682dcff38b67f5278a7e95579f8b030dfc18775ddd96dbc914e5d99da3c35a25e3f9e5487c901917c001581d81313
Type fulltextMimetype application/pdf

By organisation
Communication Networks
Communication SystemsComputer Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 331 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

urn-nbn

Altmetric score

urn-nbn
Total: 1005 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