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OnlineElastMan: self-trained proactive elasticity manager for cloud-based storage services
KTH Royal Institute of Technology, Stockholm, Sweden.
KTH Royal Institute of Technology, Stockholm, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.
KTH Royal Institute of Technology, Stockholm, Sweden.
2017 (English)In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543Article in journal (Refereed) In press
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/de-allocates 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 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
Springer New York LLC , 2017.
Keyword [en]
Cloud storage, Elasticity controller, Online training, SLO, Time series analysis, Workload prediction, Controllers, Costs, Elasticity, Managers, Model predictive control, Cloud storages, Service level objective, Service provisioning, Storage services, Training process, Workload patterns, Workload predictions, Storage management
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-29764DOI: 10.1007/s10586-017-0899-zScopus ID: 2-s2.0-85019724244OAI: oai:DiVA.org:ri-29764DiVA: diva2:1108430
Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2017-06-12Bibliographically approved

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CiteExportLink to record
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