Hadoop map reduce for mobile clouds
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Hadoop Map Reduce for Mobile Clouds To buy this project in ONLINE, Contact: Email: jpinfotechprojects@gmail.com, Website: https://www.jpinfotech.org
![Hadoop MapReduce for Mobile Clouds
ABSTRACT:
The new generations of mobile devices have high processing power and storage,
but they lag behind in terms of software systems for big data storage and
processing. Hadoop is a scalable platform that provides distributed storage and
computational capabilities on clusters of commodity hardware. Building Hadoop
on a mobile network enables the devices to run data intensive computing
applications without direct knowledge of underlying distributed systems
complexities. However, these applications have severe energy and reliability
constraints (e.g., caused by unexpected device failures or topology changes in a
dynamic network). As mobile devices are more susceptible to unauthorized access,
when compared to traditional servers, security is also a concern for sensitive data.
Hence, it is paramount to consider reliability, energy efficiency and security for
such applications. The MDFS (Mobile Distributed File System) [1] addresses these
issues for big data processing in mobile clouds. We have developed the Hadoop
MapReduce framework over MDFS and have studied its performance by varying
input workloads in a real heterogeneous mobile cluster. Our evaluation shows that
the implementation addresses all constraints in processing large amounts of data in
mobile clouds. Thus, our system is a viable solution to meet the growing demands
of data processing in a mobile environment.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:](https://image.slidesharecdn.com/hadoopmapreduceformobileclouds-190821050133/85/Hadoop-map-reduce-for-mobile-clouds-1-320.jpg)
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Hadoop map reduce for mobile clouds
- 1. Hadoop MapReduce for Mobile Clouds ABSTRACT: The new generations of mobile devices have high processing power and storage, but they lag behind in terms of software systems for big data storage and processing. Hadoop is a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware. Building Hadoop on a mobile network enables the devices to run data intensive computing applications without direct knowledge of underlying distributed systems complexities. However, these applications have severe energy and reliability constraints (e.g., caused by unexpected device failures or topology changes in a dynamic network). As mobile devices are more susceptible to unauthorized access, when compared to traditional servers, security is also a concern for sensitive data. Hence, it is paramount to consider reliability, energy efficiency and security for such applications. The MDFS (Mobile Distributed File System) [1] addresses these issues for big data processing in mobile clouds. We have developed the Hadoop MapReduce framework over MDFS and have studied its performance by varying input workloads in a real heterogeneous mobile cluster. Our evaluation shows that the implementation addresses all constraints in processing large amounts of data in mobile clouds. Thus, our system is a viable solution to meet the growing demands of data processing in a mobile environment. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:
- 2. System : i3 Processor Hard Disk : 500 GB. Monitor : 15’’ LED Input Devices : Keyboard, Mouse Ram : 4GB. SOFTWARE REQUIREMENTS: Operating system : Windows 7/UBUNTU. Coding Language : Java 1.7 ,Hadoop 0.8.1 IDE : Eclipse Database : MYSQL REFERENCE: Johnu George, Chien-An Chen, Radu Stoleru, Member, IEEE, Geoffrey G. Xie Member, IEEE, “Hadoop MapReduce for Mobile Clouds”, IEEE Transactions on Cloud Computing, 2019.