HadoopToSQL: a mapReduce query optimizer
Proceedings of the 5th European conference on Computer systems
Automatic optimization for MapReduce programs
Proceedings of the VLDB Endowment
Modeling and synthesizing task placement constraints in Google compute clusters
Proceedings of the 2nd ACM Symposium on Cloud Computing
Benchmarking MapReduce Implementations for Application Usage Scenarios
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
Cloud computing and mapreduce for reliability and scalability of ubiquitous learning systems
Proceedings of the compilation of the co-located workshops on DSM'11, TMC'11, AGERE!'11, AOOPES'11, NEAT'11, & VMIL'11
Evaluating parameter sweep workflows in high performance computing
Proceedings of the 1st ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies
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MapReduce is Google's programming model for easy development of scalable parallel applications which process huge quantity of data on many clusters. Due to its conveniency and efficiency, MapReduce is used in various applications (e.g., web search services and online analytical processing.) However, there are only few good benchmarks to evaluate MapReduce implementations by realistic testsets. In this paper, we present MRBench that is a benchmark for evaluating MapReduce systems. MRBench focuses on processing business oriented queries and concurrent data modifications. To this end, we build MRBench to deal with large volumes of relational data and execute highly complex queries. By MRBench, users can evaluate the performance of MapReduce systems while varying environmental parameters such as data size and the number of (Map/Reduce) tasks. Our extensive experimental results show that MRBench is a useful tool to benchmark the capability of answering critical business questions.