Workload management for Big Data analytics

A. Aboulnaga; S. Babu

doi:10.1109/ICDE.2013.6544915

2013 29th IEEE International Conference on Data Engineering (ICDE 2013)

Workload management for Big Data analytics

Year: 2013, Pages: 1249-1249

DOI Bookmark: 10.1109/ICDE.2013.6544915

Authors

A. Aboulnaga, Cheriton Sch. of Comput. Sci., Univ. of Waterloo, Waterloo, ON, Canada
S. Babu, Dept. of Comput. Sci., Duke Univ., Durham, NC, USA

Abstract

Parallel database systems and MapReduce systems 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.

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Workload management for Big Data analytics

Authors

Abstract

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