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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. (Distributed Systems)
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2018 (English)In: IEEE Transactions on Parallel and Distributed Systems, ISSN 1045-9219, E-ISSN 1558-2183Article in journal (Refereed) Accepted
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.

Place, publisher, year, edition, pages
IEEE, 2018.
National Category
Computer Systems
Research subject
Computer Science; Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-154291OAI: oai:DiVA.org:umu-154291DiVA, id: diva2:1270986
Funder
Swedish Research CouncilAvailable from: 2018-12-14 Created: 2018-12-14 Last updated: 2018-12-14

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