Optimizing capacity allocation for big data applications in cloud datacenters

Sebastiano Spicuglia, Lydia Y. Chen, Robert Birke, Walter Binder

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

7 Citations (Scopus)

Abstract

To operate systems cost-effectively, cloud providers not only multiplex applications on the shared infrastructure but also dynamically allocate available resources, such as power and cores. Data intensive applications based on the MapReduce paradigm rapidly grow in popularity and importance in the Cloud. Such big data applications typically have high fan-out of components and workload dynamics. It is no mean feat to deploy and further optimize application performance within (stringent) resource budgets. In this paper, we develop a novel solution, OptiCA, that eases the deployment of big data applications on cloud and the control of application components so that desired performance metrics can be best achieved for any given resource budgets, in terms of core capacities. The control algorithm of OptiCA distributes the available core budget across co-executed applications and components, based on their 'effective' demands obtained through non-intrusive profiling. Our proposed solution is able to achieve robust performance, i.e., with very minor degradation, in cases where resource budget decreases rapidly.

Original languageEnglish
Title of host publicationProceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages511-517
Number of pages7
ISBN (Electronic)9783901882760
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event14th IFIP/IEEE International Symposium on Integrated Network Management, IM 2015 - Ottawa, Canada
Duration: 11 May 201515 May 2015

Conference

Conference14th IFIP/IEEE International Symposium on Integrated Network Management, IM 2015
CountryCanada
CityOttawa
Period11/05/1515/05/15

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