LEMO-MR: Low overhead and Elastic MapReduce Implementation Optimized for Memory and CPU-Intensive Applications

Provided by: Binghamton University
Topic: Storage
Format: PDF
Since its inception, MapReduce has frequently been associated with Hadoop and large-scale datasets. Its deployment at Amazon in the cloud, and its applications at Yahoo! and Facebook for large-scale distributed document indexing and database building, among other tasks, have thrust MapReduce to the forefront of the data processing application domain. The applicability of the paradigm however extends far beyond its use with data intensive applications and disk-based systems, and can also be brought to bear in processing small but CPU intensive distributed applications.

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