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Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field
Umeå University, Faculty of Science and Technology, Department of Computing Science. UMass, Amherst, MA, USA.
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2019 (English)In: IEEE Transactions on Parallel and Distributed Systems, ISSN 1045-9219, E-ISSN 1558-2183, Vol. 30, no 4, p. 800-813Article in journal (Refereed) Published
Abstract [en]

Auto-scalers for clouds promise stable service quality at low costs when facing changing workload intensity. The major public cloud providers provide trigger-based auto-scalers based on thresholds. However, trigger-based auto-scaling has reaction times in the order of minutes. Novel auto-scalers from literature try to overcome the limitations of reactive mechanisms by employing proactive prediction methods. However, the adoption of proactive auto-scalers in production is still very low due to the high risk of relying on a single proactive method. This paper tackles the challenge of reducing this risk by proposing a new hybrid auto-scaling mechanism, called Chameleon, combining multiple different proactive methods coupled with a reactive fallback mechanism. Chameleon employs on-demand, automated time series-based forecasting methods to predict the arriving load intensity in combination with run-time service demand estimation to calculate the required resource consumption per work unit without the need for application instrumentation. We benchmark Chameleon against five different state-of-the-art proactive and reactive auto-scalers one in three different private and public cloud environments. We generate five different representative workloads each taken from different real-world system traces. Overall, Chameleon achieves the best scaling behavior based on user and elasticity performance metrics, analyzing the results from 400 hours aggregated experiment time.

Place, publisher, year, edition, pages
IEEE Computer Society, 2019. Vol. 30, no 4, p. 800-813
Keywords [en]
Auto-scaling, elasticity, workload forecasting, service demand estimation, IaaS cloud, benchmarking, metrics
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-157732DOI: 10.1109/TPDS.2018.2870389ISI: 000461343700007Scopus ID: 2-s2.0-85053342911OAI: oai:DiVA.org:umu-157732DiVA, id: diva2:1303790
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-04-10Bibliographically approved

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Ali-Eldin, Ahmed

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CiteExportLink to record
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