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Energy-aware Auto-scaling Algorithms for Cassandra Virtual Data Centers
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-3118-5058
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2017 (English)In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543, no MayArticle in journal (Refereed) Published
Abstract [en]

Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra Virtual Data Centers (VDCs). This paper address the problem of energy efficient auto-scaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: \texttt{Opt}, \texttt{LocalOpt} and \texttt{LocalOpt-H}. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. \texttt{LocalOpt} consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energy-aware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely \texttt{BestFit} and \texttt{BestFit-H}. The main findings are: VDC allocation aiming at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2017. no May
Keyword [en]
Cloud Computing, Big Data, Autonomic Computing, Cassandra
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:bth-14152DOI: 10.1007/s10586-017-0912-6OAI: oai:DiVA.org:bth-14152DiVA: diva2:1093497
Projects
BigData@BTH
Funder
Knowledge Foundation, 20140032
Available from: 2017-05-07 Created: 2017-05-07 Last updated: 2017-05-22Bibliographically approved

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Casalicchio, EmilianoLundberg, LarsShirinbab, Sogand
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CiteExportLink to record
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Citation style
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