A bridging model for parallel computation
Communications of the ACM
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Adapting scientific computing problems to clouds using MapReduce
Future Generation Computer Systems
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The importance of fault tolerance for the parallel computing field is ever increasing, as the mean time between failures is predicted to decrease significantly for future highly parallel systems. The current trend of using commodity hardware to reduce the cost of clusters forces users to ensure that their applications are fault tolerant. When it comes to embarrassingly parallel data-intensive algorithms, MapReduce has gone a long way in simplifying the creation of such applications. However, this does not apply to iterative communication-intensive algorithms common in the scientific computing domain. In this work we propose a new programming model inspired by Bulk Synchronous Parallel (BSP) for creating new a fault tolerant distributed computing framework. We strive to retain the advantages that MapReduce provides, yet efficiently support a larger assortment of algorithms, such as the aforementioned iterative ones.