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Schnell, robust, einfach zu nutzen, skalierbar, weit einsetzbar und inklusive Monitoring: Das verspricht MapReduce, ein Framework von Google zur nebenläufigen Berechnung sehr großer Datenmengen auf Rechnerclustern. Ein mutiges Versprechen. Dieser Artikel wird zeigen, ob MapReduce es einlöst.

Data analytics is becoming increasingly prominent in a variety of application areas ranging from extracting business intelligence to processing data from scientific studies. MapReduce programming paradigm lends itself well to these data-intensive analytics jobs, given its ability to scale-out and leverage several machines to parallely process data. In this work we argue that such MapReduce-based analytics are particularly synergistic with the pay-as-you-go model of a cloud platform. However, a key challenge facing end-users in this environment is the ability to provision MapReduce applications to minimize the incurred cost, while obtaining the best performance. This paper firstmotivates the importance of optimally provisioning a MapReduce job, and demonstrates that existing approaches can result in far from optimal provisioning. We then present a preliminary approach that improves MapReduce provisioning by analyzing and comparing resource consumption of the application at hand with a database of similar resource consumption signatures of other applications.