Time estimation for large scale of data processing in Hadoop MapReduce scenario
Master thesis
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http://hdl.handle.net/11250/137521Utgivelsesdato
2011Metadata
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Sammendrag
The appearance of MapReduce technology gave rise to a strong blast in IT industry. Large
companies such as Google, Yahoo and Facebook are using this technology to facilitate
their data processing[12]. As a representative technology aimed at processing large dataset
in parallel, MapReduce received great focus from various organizations. Handling large
problems, using a large amount of resources is inevitable. Therefore, how to organize them
effectively becomes an important problem. It is a common strategy to obtain some learning
experience before deploying large scale of experiments. Following this idea, this mater
thesis aims at providing some learning models towards MapReduce. These models help us
to accumulate learning experience from small scale of experiments and finally lead us to
estimate execution time of large scales of experiment.
Beskrivelse
Masteroppgave i informasjons- og kommunikasjonsteknologi IKT590 2011 – Universitetet i Agder, Grimstad