Latest revision as of 04:25, 2 February 2021

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.

Original document

The different versions of the original document can be found in:

• http://granules.cs.colostate.edu/papers/Pallickara-GranulesAndMR.pdf

• http://xplorestaging.ieee.org/ielx5/5281774/5289120/05289160.pdf?arnumber=5289160,

http://dx.doi.org/10.1109/clustr.2009.5289160

• https://dblp.uni-trier.de/db/conf/cluster/cluster2009.html#PallickaraEF09,

http://grids.ucs.indiana.edu/ptliupages/projects/narada/papers/Pallickara-GranulesAndMR.pdf,

http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000005289160,

https://ieeexplore.ieee.org/document/5289160,

http://ieeexplore.ieee.org/document/5289160,

https://doi.org/10.1109/CLUSTR.2009.5289160,

https://academic.microsoft.com/#/detail/2150603860