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Data Scheduling and Mapreducing in Big Data

E.Ravi Kondal*, B.Mounika**

* Associate Professor, Department of Computer Science and Engineering, Noble College of Engineering & Technology for Women, Hyderabad, India.

** B. Tech Student, Department of Computer Science and Engineering, Noble College of Engineering & Technology for Women, Hyderabad, India.

Periodicity:February - April'2015
DOI : https://doi.org/10.26634/jcc.2.2.3445

Abstract

The volume of data usage is growing drastically day by day. Hence, it is not easy to maintain the data. In Big Data, huge amount of structured, semi-structured and unstructured data, produced daily by resources all over the world are stored in the computer. Mapreducing, a programming model, is used for implementing such large data sets. MapReduce program is used to collect data as per the request. To process the large volume of data, proper scheduling is used in order to achieve greater performance. Task scheduling plays a major role in Big Data cloud. Task scheduling contains a lot of rules to solve the problems of users and provides the quality of services to achieve the goal of that task to improve the resource utilization and turnaround time. Capacity and Delay Scheduling are used to improve the performance of the Big Data. This paper presents an overview of the Map-Reduce technique for shuffling and reducing the data and also the Capacity Scheduling and Delay Scheduling, for improving the reliability of the data

Keywords

Big Data, Map Reducing, Task Scheduling, Capacity Scheduling, Delay Scheduling.

How to Cite this Article?

Kondal, E. R., and Mounika, B. (2015). Data Scheduling and Mapreducing in Big Data. i-manager’s Journal on Cloud Computing, 2(2), 1-6. https://doi.org/10.26634/jcc.2.2.3445

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