Before revision:  In this paper, we present an approach to predict the total CPU utilization in terms of CPU clock tick of applications when running on MapReduce framework.
After revision: Recently, businesses have started using MapReduce as a popular computation framework for processing large amount of data, such as spam detection, and different data mining tasks, in both public and private clouds. Two of the challenging questions in such environments are (1) choosing suitable values for MapReduce configuration parameters -e.g., number of mappers, number of reducers, and DFS block size-, and (2) predicting the amount of resources that a user should lease from the service provider. Currently, the tasks of both choosing configuration parameters and estimating required resources are solely the users' responsibilities. In this paper, we present an approach to predict the total CPU utilization in terms of CPU clock tick of applications when running on MapReduce framework.