Abstract-More and more Internet companies rely on large scale data analysis as part of their core services for tasks such as log analysis, feature extraction or data filtering. Map-Reduce, through its Hadoop implementation, has proved to be an efficient model for dealing with such data. One important challenge when performing such analysis is to predict the performance of individual jobs. In this paper, we propose a simple framework to predict the performance of Hadoop jobs. It is composed of a dynamic light-weight Hadoop job analyzer, and a prediction module using locally weighted regression methods. Our framework makes some theoretical cost models more practical, and also well fits for the diversification of the jobs and clusters. It can also help those users who want to predict the cost when applying for an ondemand cloud service. At the end, we do some experiments to verify our framework.
Finally, Song et al.'s method for predicting the performance of Hadoop jobs by using locally weighted regression methods REF does not consider the cost of executing Hadoop applications.
A Hadoop MapReduce Performance Prediction Method
2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing
2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing
Computer Science