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Performance Prediction in Dynamic Clouds using Transfer Learning
Ericsson Res, Stockholm, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering. Swedish Inst Comp Sci RISE SICS, Stockholm, Sweden..
Ericsson Res, Stockholm, Sweden..
2019 (English)In: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019, IEEE, 2019, p. 242-250, article id 8717847Conference paper, Published paper (Refereed)
Abstract [en]

Learning a performance model for a cloud service is challenging since its operational environment changes during execution, which requires re-training of the model in order to maintain prediction accuracy. Training a new model from scratch generally involves extensive new measurements and often generates a data-collection overhead that negatively affects the service performance. In this paper, we investigate an approach for re-training neural-network models, which is based on transfer learning. Under this approach, a limited number of neural-network layers are re-trained while others remain unchanged. We study the accuracy of the re-trained model and the efficiency of the method with respect to the number of re-trained layers and the number of new measurements. The evaluation is performed using traces collected from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions. We study model re-training after changes in load pattern, infrastructure configuration, service configuration, and target metric. We find that our method significantly reduces the number of new measurements required to compute a new model after a change. The reduction exceeds an order of magnitude in most cases.

Place, publisher, year, edition, pages
IEEE, 2019. p. 242-250, article id 8717847
Keywords [en]
Service Management, Performance Prediction, Machine Learning, Neural Networks, Transfer Learning
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-254134ISI: 000469937200055Scopus ID: 2-s2.0-85067071723ISBN: 9783903176157 (print)OAI: oai:DiVA.org:kth-254134DiVA, id: diva2:1330835
Conference
2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019; Arlington; United States; 8 April 2019 through 12 April 2019
Note

QC 20190626

Available from: 2019-06-26 Created: 2019-06-26 Last updated: 2019-06-26Bibliographically approved

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Stadler, Rolf

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CiteExportLink to record
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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