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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, p. 349-350Conference 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. p. 349-350
Keywords [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 Sciences
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, id: diva2:1059892
Conference
IEEE International Conference on Autonomic Computing (ICAC), Würzburg
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge FoundationAvailable from: 2016-12-26 Created: 2016-12-26 Last updated: 2021-05-05Bibliographically approved

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Casalicchio, EmilianoLundberg, LarsShirinbad, Sogand

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