Workload management for big data analytics
Publication
, Journal Article
Aboulnaga, A; Babu, S
Published in: Proceedings - International Conference on Data Engineering
August 15, 2013
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. © 2013 IEEE.
Duke Scholars
Published In
Proceedings - International Conference on Data Engineering
DOI
ISSN
1084-4627
Publication Date
August 15, 2013
Start / End Page
1249
Citation
APA
Chicago
ICMJE
MLA
NLM
Aboulnaga, A., & Babu, S. (2013). Workload management for big data analytics. Proceedings - International Conference on Data Engineering, 1249. https://doi.org/10.1109/ICDE.2013.6544915
Aboulnaga, A., and S. Babu. “Workload management for big data analytics.” Proceedings - International Conference on Data Engineering, August 15, 2013, 1249. https://doi.org/10.1109/ICDE.2013.6544915.
Aboulnaga A, Babu S. Workload management for big data analytics. Proceedings - International Conference on Data Engineering. 2013 Aug 15;1249.
Aboulnaga, A., and S. Babu. “Workload management for big data analytics.” Proceedings - International Conference on Data Engineering, Aug. 2013, p. 1249. Scopus, doi:10.1109/ICDE.2013.6544915.
Aboulnaga A, Babu S. Workload management for big data analytics. Proceedings - International Conference on Data Engineering. 2013 Aug 15;1249.
Published In
Proceedings - International Conference on Data Engineering
DOI
ISSN
1084-4627
Publication Date
August 15, 2013
Start / End Page
1249