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
An Energy-Aware Adaptation Model for Big Data Platforms
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: 2016 IEEE International Conference on Autonomic Computing (ICAC) / [ed] IEEE, IEEE, 2016, 349-350 p.Conference paper, Published paper (Refereed)
Abstract [en]

Platforms for big data includes mechanisms and tools to model, organize, store and access big data (e.g. Apache Cassandra, Hbase, Amazon SimpleDB, Dynamo, Google BigTable). The resource management for those platforms is a complex task and must account also for multi-tenancy and infrastructure scalability. Human assisted control of Big data platform is unrealistic and there is a growing demand for autonomic solutions. In this paper we propose a QoS and energy-aware adaptation model designed to cope with the real case of a Cassandra-as-a-Service provider.

Place, publisher, year, edition, pages
IEEE, 2016. 349-350 p.
Keyword [en]
Big Data;fault tolerant computing;power aware computing;quality of service;resource allocation;Amazon SimpleDB;Apache Cassandra;Big Data platforms;Cassandra-as-a-Service provider;Dynamo;Google BigTable;Hbase;energy-aware adaptation model;human assisted control;infrastructure scalability;multitenancy;resource management;Adaptation models;Big data;Cloud computing;Optimization;Runtime;Scalability;Throughput;Apache Cassandra;Autonomic computing;Big Data;Cloud computing;Green computing
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-13669DOI: 10.1109/ICAC.2016.13ISI: 000390681200054ISBN: 978-1-5090-1654-9 (print)OAI: oai:DiVA.org:bth-13669DiVA: diva2:1059892
Conference
IEEE International Conference on Autonomic Computing (ICAC), Würzburg
Available from: 2016-12-26 Created: 2016-12-26 Last updated: 2017-01-20Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

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

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 63 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