Before revision:  This derived model can be used for predicting total CPU requirements of the same application when using MapReduce framework on the same platform. Our approach aims to eliminate error-prone manual processes and presents a fully automated solution. Three standard applications (WordCount, Exim Mainlog parsingand Terasort)are used to evaluate our modeling technique on pseudo-distributed MapReduce platforms . Results show that our automated model generation procedure can effectively characterize total CPU clock tick of these applications with average prediction error of 3.5\% , 4.05\% and 2.75\%, respectively .
After revision:  This derived model can be used for predicting total CPU requirements of the same jobs with different input data size. We validate the accuracy of our models using three realistic applications (WordCount, Exim Mainlog parsingand Terasort)are used to evaluate our modeling technique on pseudo-distributed MapReduce platforms . Results show that our automated model generation procedure can effectively characterize total CPU clock tick of these applications with average prediction error of 3.5\% , 4.05\% and 2.75\%, respectively .