Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Energy-Aware Adaptation in Managed Cassandra Datacenters
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.
2016 (English)In: Proceedings - 2016 International Conference on Cloud and Autonomic Computing, ICCAC / [ed] Gupta I.,Diao Y., IEEE, 2016, 60-71 p.Conference paper (Refereed)
Abstract [en]

Today, Apache Cassandra, an highly scalable and available NoSql datastore, is 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 datacenters. As for all complex services, human assisted management of a multi-tenant cassandra datacenter is unrealistic. Rather, there is a growing demand for autonomic management solutions. In this paper, we present an optimal energy-aware adaptation model for managed Cassandra datacenters that modify the system configuration orchestrating three different actions: horizontal scaling, vertical scaling and energy aware placement. The model is built from a real case based on real application data from Ericsson AB. We compare the performance of the optimal adaptation with two heuristics that avoid system perturbations due to re-configuration actions triggered by subscription of new tenants and/or changes in the SLA. One of the heuristic is local optimisation and the second is a best fit decreasing algorithm selected as reference point because representative of a wide range of research and practical solutions. The main finding is that heuristic’s performance depends on the scenario and workload and no one dominates in all the cases. Besides, in high load scenarios, the suboptimal system configuration obtained with an heuristic adaptation policy introduce a penalty in electric energy consumption in the range [+25%, +50%] if compared with the energy consumed by an optimal system configuration.

Place, publisher, year, edition, pages
IEEE, 2016. 60-71 p.
Keyword [en]
Adaptation models;Cloud computing;Mathematical model;Optimization;Scalability;Throughput;Tuning;Autonomic computing;apache cassandra;big data;cloud computing;green computing;optimisation;self-adaptation
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-13670DOI: 10.1109/ICCAC.2016.12ISBN: 978-1-5090-3536-6 (print)OAI: oai:DiVA.org:bth-13670DiVA: diva2:1059893
Conference
International Conference on Cloud and Autonomic Computing, ICCAC 2016; Augsburg; Germany
Available from: 2016-12-26 Created: 2016-12-26 Last updated: 2017-02-22Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Casalicchio, EmilianoLundberg, LarsShirinbab, Sogand
By organisation
Department of Computer Science and Engineering
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 27 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf