Publicado em: 03/01/2013

Dissertação de Mestrado em Processamento Paralelo e Distribuído

UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL
INSTITUTO DE INFORMÁTICA
PROGRAMA DE POS-GRADUAÇÃO EM COMPUTAÇÃO
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DEFESA DE DISSERTAÇÃO DE MESTRADO

Aluno: Pedro de Botelho Marcos
Orientador: Prof. Dr. Cláudio Fernando Resin Geyer

Título: Maresia – A Decentralized Approach for MapReduce Model
Linha de Pesquisa: Processamento Paralelo e Distribuído

Data: 09/01/2013
Hora: 14h
Local: Sala 220 (conselhos). Prédio 43412 – Instituto de Informática

Banca Examinadora:
Prof. Dr. Antonio Marinho Pilla Barcellos (UFRGS)
Profa. Dra. Luciana Bezerra Arantes (LIP6/CNRS)
Profa. Dra. Taisy Silva Weber (UFRGS)

Presidente da Banca: Prof. Dr. Cláudio Fernando Resin Geyer

Abstract:
During the last years, the amount of data generated by applications grew considerably. To become relevant, however, these data should be processed. With this goal, new programming models for parallel and distributed processing were proposed. An example is the MapReduce model, which was proposed by Google. This model, nevertheless, has Single Points of Failure (SPOF), which can compromise the execution of a job. Thus, this work presents a new architecture, inspired by Chord, to avoid the SPOFs on MapReduce. The evaluation was performed through an analytical model and an experimental setup. The results show the feasibility of using the proposed architecture to execute MapReduce jobs.

Keywords: Distributed systems, mapreduce, fault tolerance, P2P.