DocumentCode :
610410
Title :
Workload management for Big Data analytics
Author :
Aboulnaga, A. ; Babu, Sarath
Author_Institution :
Cheriton Sch. of Comput. Sci., Univ. of Waterloo, Waterloo, ON, Canada
fYear :
2013
fDate :
8-12 April 2013
Firstpage :
1249
Lastpage :
1249
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.
Keywords :
data analysis; parallel databases; MapReduce systems; big data analytics; complex user requests; parallel database systems; service level objective; workload management; Admission control; Big data; Databases; Educational institutions; Processor scheduling; Resource management; Tutorials;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Engineering (ICDE), 2013 IEEE 29th International Conference on
Conference_Location :
Brisbane, QLD
ISSN :
1063-6382
Print_ISBN :
978-1-4673-4909-3
Electronic_ISBN :
1063-6382
Type :
conf
DOI :
10.1109/ICDE.2013.6544915
Filename :
6544915
Link To Document :
https://search.ricest.ac.ir/dl/search/defaultta.aspx?DTC=49&DC=610410
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