Energy Efficient Scheduling of Map Reduce for Evolving Big Data Applications

Abstract: In recent years the data mining applications become stale and obsolete over time. Energy wastage is the major problem more of the IT firms. More workload and more computational will increase high energy cost. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose Energy Map Reduce Scheduling Algorithm, a novel incremental processing extension to Map Reduce, the most widely used framework for mining big data. Map reduce is a programming model for processing and generating large amount of data in parallel time. In this paper, EMRSA is algorithm provide more energy and less maps. Priority based scheduling is a task will allocate the schedules based on necessary and utilization of the Jobs. For reducing the maps, it will reduce the system work so easily energy has improved. Final results show the experimental comparison of the different algorithms involved in the paper.

Keywords: BigData, EMRSA, MapReduce, incremental processing.