MapReduce (MR) has been one of the popular computing framework for BigData analysis and processing application in last decade; further Hadoop is an open source platform which is widely used for MR framework. Moreover, existing HMR aka Hadoop-MR model faces major issues like I/O overhead and memory overhead. In this research work, we focus on developing memory and performance awarescheduler design named as MPA-HMR for efficient utilization of system resources and data processing in real time.MPAS-HMR is developed for analyzing the Global Memory Management; thus minimizing the Disk I/O seek. Moreover, MPAS method are evaluated on the Microsoft Azure HDInsight cloud platform in consideration with text mining applications, also comparative analysis with the existing model is carried out. Further, comparative analysis shows that our model outperforms existing model in terms of computation time and computing cost.