Change search
CiteExportLink to record
Permanent link

Direct link
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
OnlineElastMan: Self-Trained Proactive Elasticity Manager for Cloud-Based Storage Services
KTH, School of Information and Communication Technology (ICT).
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
2016 (English)In: 2016 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 50-59Conference paper, Published paper (Refereed)
Abstract [en]

The pay-as-you-go pricing model and the illusion of unlimited resources in the Cloud initiate the idea to provision services elastically. Elastic provisioning of services allocates/deallocates resources dynamically in response to the changes of the workload. It minimizes the service provisioning cost while maintaining the desired service level objectives (SLOs). Model-predictive control is often used in building such elasticity controllers that dynamically provision resources. However, they need to be trained, either online or offline, before making accurate scaling decisions. The training process involves tedious and significant amount of work as well as some expertise, especially when the model has many dimensions and the training granularity is fine, which is proved to be essential in order to build an accurate elasticity controller. In this paper, we present OnlineElastMan, which is a self-trained proactive elasticity manager for cloud-based storage services. It automatically trains and evolves itself while serving the workload. Experiments using OnlineElastMan with Cassandra indicate that OnlineElastMan continuously improves its provision accuracy, i.e., minimizing provisioning cost and SLO violations, under various workload patterns.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 50-59
Keywords [en]
Elasticity Controller, Cloud Storage, Workload prediction, SLO, Online Training, Time series analysis
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-199801DOI: 10.1109/ICCAC.2016.11ISI: 000390252000006ISBN: 978-1-5090-3536-6 (print)OAI: oai:DiVA.org:kth-199801DiVA, id: diva2:1066763
Conference
IEEE International Conference on Cloud and Autonomic Computing (ICCAC), SEP 12-16, 2016, Augsburg, GERMANY
Note

QC 20170119

Available from: 2017-01-19 Created: 2017-01-16 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Vlassov, Vladimir

Search in DiVA

By author/editor
Liu, YingGureya, DaharewaVlassov, Vladimir
By organisation
School of Information and Communication Technology (ICT)Software and Computer systems, SCS
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 13 hits
CiteExportLink to record
Permanent link

Direct link
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