Autori: Bing Tang, Mircea Moca, Stephane Chevalier, Haiwu He, Gilles Fedak

Editorial: IEEE, 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, p.193-200, 2010.

Rezumat:

MapReduce is an emerging programming model for dataintensive
application proposed by Google, which has attracted a
lot of attention recently. MapReduce borrows ideas from functional
programming, where programmer defines Map and Reduce tasks to
process large set of distributed data. In this paper we propose an
implementation of the MapReduce programming model. We present
the architecture of the prototype based on BitDew, a middleware for
large scale data management on Desktop Grid. We describe the set of
features which makes our approach suitable for large scale and loosely
connected Internet Desktop Grid: massive fault tolerance, replica
management, barriers-free execution, latency-hiding optimisation as
well as distributed result checking. We also present performance
evaluation of the prototype both against micro-benchmarks and real
MapReduce application. The scalability test shows that we achieve
linear speedup on the classic WordCount benchmark. Several scenarios
involving lagger hosts and host crashes demonstrate that the prototype
is able to cope with an experimental context similar to real-world
Internet.

Cuvinte cheie: Desktop Grid computing, MapReduce, aplicatii pentru prelucrarea volumelor mari de date // Desktop Grid computing, MapReduce, data-intensive application

URL: http://www.ieee.org