**Journal Title :**** International Journal of Modern Trends in Engineering and Science**

**Paper Title : ENERGY PROFICIENT FORECASTING OF MAP REDUCE FOR BIG DATA REAL TIME APPLIANCES**

**Author’s Name :** **A Kaliappan | M Sabarivel**

**Volume** **04 Issue 03 2017**

**ISSN no:** **2348-3121**

**Page no:** **24-29**

**Abstract** – **Now a days, data mining applications become hard and obsolete over time. Energy depletion is the main problem for more of the corporate firms. Supplementary workload and more computational processes will increase high energy cost. Incremental processing is a potential approach to rejuvenate 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 narrative 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** – **Big Data, EMRSA, Map Reduce, Incremental Processing
**

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