Title :
Granules: A lightweight, streaming runtime for cloud computing with support, for Map-Reduce
Author :
Pallickara, Shrideep ; Ekanayake, Jaliya ; Fox, Geoffrey
fDate :
Aug. 31 2009-Sept. 4 2009
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;
Conference_Titel :
Cluster Computing and Workshops, 2009. CLUSTER '09. IEEE International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-5011-4
Electronic_ISBN :
1552-5244
DOI :
10.1109/CLUSTR.2009.5289160