DocumentCode
1925983
Title
Granules: A lightweight, streaming runtime for cloud computing with support, for Map-Reduce
Author
Pallickara, Shrideep ; Ekanayake, Jaliya ; Fox, Geoffrey
fYear
2009
fDate
Aug. 31 2009-Sept. 4 2009
Firstpage
1
Lastpage
10
Abstract
Cloud computing has gained significant traction in recent years. The Map-Reduce framework is currently the most dominant programming model in cloud computing settings. In this paper, we describe Granules, a lightweight, streaming-based runtime for cloud computing which incorporates support for the Map-Reduce framework. Granules provides rich lifecycle support for developing scientific applications with support for iterative, periodic and data driven semantics for individual computations and pipelines. We describe our support for variants of the Map-Reduce framework. The paper presents a survey of related work in this area. Finally, this paper describes our performance evaluation of various aspects of the system, including (where possible) comparisons with other comparable systems.
Keywords
graph theory; pipeline processing; program diagnostics; Granules; Map-Reduce; cloud computing; data driven semantics; Application software; Cloud computing; Computer science; Concurrent computing; Hardware; Machine learning algorithms; Parallel processing; Parallel programming; Pipelines; Runtime; cloud computing; cloud runtimes; content distribution networks; map-reduce; streaming;
fLanguage
English
Publisher
ieee
Conference_Titel
Cluster Computing and Workshops, 2009. CLUSTER '09. IEEE International Conference on
Conference_Location
New Orleans, LA
ISSN
1552-5244
Print_ISBN
978-1-4244-5011-4
Electronic_ISBN
1552-5244
Type
conf
DOI
10.1109/CLUSTR.2009.5289160
Filename
5289160
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