Workload management for big data analytics

Ashraf Aboulnaga, Shivnath Babu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Parallel database systems and MapReduce systems (most notably Hadoop) are essential components of today's infrastructure for Big Data analytics. These systems process multiple concurrent workloads consisting of complex user requests, where each request is associated with an (explicit or implicit) service level objective. For example, the workload of a particular user or application may have a higher priority than other workloads. Or a particular workload may have strict deadlines for the completion of its requests.

Original languageEnglish
Title of host publicationICDE 2013 - 29th International Conference on Data Engineering
Number of pages1
DOIs
Publication statusPublished - 15 Aug 2013
Event29th International Conference on Data Engineering, ICDE 2013 - Brisbane, QLD, Australia
Duration: 8 Apr 201311 Apr 2013

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other29th International Conference on Data Engineering, ICDE 2013
CountryAustralia
CityBrisbane, QLD
Period8/4/1311/4/13

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Aboulnaga, A., & Babu, S. (2013). Workload management for big data analytics. In ICDE 2013 - 29th International Conference on Data Engineering [6544915] (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2013.6544915