World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , 

World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

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    Article are invited for publication in WJERT Coming Issue

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    NOVEMBER 2021 Issue has been successfully launched on 1 November 2021.




Indumathi. S, Pavithra. T. N, Shruti. M. Masali, Thriveni. M. D*, Malatesh. S. H


Big data is defined as large amount of data which requires new technologies and architectures to make possible to extract value from it by capturing and analysis process. Big data has emerged because we are living in a society which makes increasing use of data intensive technologies. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data concept means a datasets which continue to grow so much that it becomes difficult to manage it using existing database management concepts and tools. MapReduce is a framework using which we can write applications to process huge amounts of data, in parallel, on large clusters of commodity hardware in a reliable manner. MapReduce has become one standard for big data processing. However job scheduling in this model is always a challenge for the research fraternity and several job scheduling algorithms have a already have been proposed by researchers addressing various issues. All such algorithms mostly emphasize on meeting of job deadline without taking cognizance of additional released resources being available through intermediate release of resources during processing. In this paper, we propose a new job scheduling algorithm which satisfies job deadline for all scheduled jobs along with utilization of the unused resources so that more number of jobs can be scheduled and the overall system efficiency will be increased.

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