MAP Reduce In Mobile Clouds Using Hadoop: MDFS (Mobile Distributed File System) Addresses Issues for Big Data Processing in Mobile Clouds

K. Swarupa Rani, K. Anitha

Abstract


Hadoop is a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware. Building Hadoop on a mobile net-work enables the devices to run data intensive computing applications without direct knowledge of underlying distributed systems complexities.  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) 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.


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