Benchmarking approach for designing a mapreduce performance model

Abstract

In MapReduce environments, many of the programs are reused for processing a regularly incoming new data. A typical user question is how to estimate the completion time of these programs as a function of a new dataset and the cluster resources. In this work1 , we offer a novel performance evaluation framework for answering this question. We observe that the execution of each map (reduce) tasks consists of specific, well-defined data processing phases. Only map and reduce functions are custom and their executions are user-defined for different MapReduce jobs. The executions of the remaining phases are generic and depend on the amount of data processed by the phase and the performance of underlying Hadoop cluster. First, we design a set of parameterizable microbenchmarks to measure generic phases and to derive a platform performance model of a given Hadoop cluster. Then using the job past executions, we summarize job's properties and performance of its custom map/reduce functions in a compact job profile. Finally, by combining the knowledge of the job profile and the derived platform performance model, we offer a MapReduce performance model that estimates the program completion time for processing a new dataset. The evaluation study justifies our approach and the proposed framework: we are able to accurately predict performance of the diverse set of twelve MapReduce applications. The predicted completion times for most experiments are within 10% of the measured ones (with a worst case resulting in 17% of error) on our 66-node Hadoop cluster.