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University of New Orleans
An Efficient Platform for Large-Scale MapReduce Processing
Abstract
dc:description.abstractIn this thesis we proposed and implemented the MMR, a new and open-source MapRe- duce model with MPI for parallel and distributed programing. MMR combines Pthreads, MPI and the Google's MapReduce processing model to support multi-threaded as well as dis- tributed parallelism. Experiments show that our model signi cantly outperforms the leading open-source solution, Hadoop. It demonstrates linear scaling for CPU-intensive processing and even super-linear scaling for indexing-related workloads. In addition, we designed a MMR live DVD which facilitates the automatic installation and con guration of a Linux cluster with integrated MMR library which enables the development and execution of MMR applications.
Degree
thesis:*- Name thesis:degree_name
- M.S.
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Computer Science
- Year
- 2009
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Wang, Liqiang
- Contributors dc:contributor
-
- Roussev, Vassil
- Tu, Shengru
- Richard III, Golden G.
Subjects
dc:subject × 4Identifiers
dc:identifier.*- Repository record dc:identifier
- https://scholarworks.uno.edu/td/963
- OAI identifier oai:identifier
- oai:scholarworks.uno.edu:td-1944