PROJECT TITLE :

RFHOC: A Random-Forest Approach to Auto-Tuning Hadoop's Configuration

ABSTRACT:

Hadoop is a widely-used implementation framework of the MapReduce programming model for massive-scale data processing. Hadoop performance but is considerably littered with the settings of the Hadoop configuration parameters. Unfortunately, manually tuning these parameters is very time-consuming, if in any respect sensible. This paper proposes an approach, known as RFHOC, to automatically tune the Hadoop configuration parameters for optimized performance for a given application running on a given cluster. RFHOC constructs two ensembles of performance models using a random-forest approach for the map and reduce stage respectively. Leveraging these models, RFHOC employs a genetic algorithm to automatically search the Hadoop configuration space. The analysis of RFHOC using five typical Hadoop programs, every with five totally different input information sets, shows that it achieves a performance speedup by a factor of 2.eleven on average and up to seven.4 over the recently proposed cost-based optimization (CBO) approach. In addition, RFHOC's performance profit increases with input knowledge set size.


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