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ProRenaTa: Proactive and reactive tuning to scale a distributed storage system
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. Université Catholique de Louvain, Belgium.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. Universitat Politècnica de Catalunya, Spain.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
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2015 (English)In: Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015, 453-464 p.Conference paper, Published paper (Refereed)
Abstract [en]

Provisioning tasteful services in the Cloud that guarantees high quality of service with reduced hosting cost is challenging to achieve. There are two typical auto-scaling approaches: predictive and reactive. A prediction based controller leaves the system enough time to react to workload changes while a feedback based controller scales the system with better accuracy. In this paper, we show the limitations of using a proactive or reactive approach in isolation to scale a tasteful system and the overhead involved. To overcome the limitations, we implement an elasticity controller, ProRenaTa, which combines both reactive and proactive approaches to leverage on their respective advantages and also implements a data migration model to handle the scaling overhead. We show that the combination of reactive and proactive approaches outperforms the state of the art approaches. Our experiments with Wikipedia workload trace indicate that ProRenaTa guarantees a high level of SLA commitments while improving the overall resource utilization.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. 453-464 p.
Series
IEEE-ACM International Symposium on Cluster Cloud and Grid Computing, ISSN 2376-4414
Keyword [en]
Auto-scaling, Elasticity, Resource utilization, SLA, Workload prediction, Cluster computing, Controllers, Digital storage, Grid computing, Multiprocessing systems, Quality of service, Distributed storage system, Prediction-based, Pro-active approach, Resource utilizations, State-of-the-art approach, Workload predictions, Distributed computer systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-176116DOI: 10.1109/CCGrid.2015.26ISI: 000380493100046Scopus ID: 2-s2.0-84941206623ISBN: 978-1-4799-8006-2 (print)OAI: oai:DiVA.org:kth-176116DiVA: diva2:874716
Conference
15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015, Hilton Shenzhen Shekou Nanhai HotelShenzhen, China, 4 May 2015 through 7 May 2015
Note

QC 20151127

Available from: 2015-11-27 Created: 2015-11-02 Last updated: 2016-09-21Bibliographically approved

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