OnlineElastMan: self-trained proactive elasticity manager for cloud-based storage services View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-09

AUTHORS

Ying Liu, Daharewa Gureya, Ahmad Al-Shishtawy, Vladimir Vlassov

ABSTRACT

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. More... »

PAGES

1977-1994

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10586-017-0899-z

DOI

http://dx.doi.org/10.1007/s10586-017-0899-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085708522


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