Abstract—The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications. In a large cluster, thousands of servers both host directly attached storage and execute user application tasks

designed to store very large data sets reliably and to stream those data sets at high bandwidth to user applications. Metadata management is critical to distributed file system. In HDFS architecture, a single master server manages all metadata, while a number of data servers store file data

Abstract- Hadoop provides a reliable shared storage and analysis system.The storage is provided by Hadoop Distributed File System(HDFS). Hadoop is a popular open source implementation of mapreduce, a powerful tool designed for deep analysis and transformation of very large data sets. Managing

between PVFS, a parallel file system used in HPC at large scale, and HDFS, the primary storage system used in cloud computing with Hadoop. We integrate PVFS into Hadoop and compare its performance to HDFS using a set of data-intensive computing benchmarks. We study how HDFS-specific optimizations can

With the growing use of large-scale distributed systems, the likelihood that at least one node is compromised is increasing. Large-scale systems that process sensitive data such as geographic data with defense implications, drug modeling, nuclear explosion modeling, and private genomic data would

large-scale, distributed systems

petascale to exascale. At exascale, basic functionality at high concurrency levels will suffer poor performance, and combined with system mean-time-to-failure in hours, will lead to a performance collapse for large-scale heroic applications. Storage has the potential to be

This thesis is motivated by the fact that there is an urgent need to run scientific many-task workflow applications efficiently and easily on large-scale machines. These applications run at large scale on supercomputers and perform large amount of storage I/O. The storage system is identified

iii ACKNOWLEDGEMENTS “Oh, yes, the acknowledgements. I think not. I did it. I did it all, by myself.” – Olin Shivers, scsh reference manual My time at Georgia Tech has been long and challenging, but ultimately rewarding. I can credit my initial graduate school trajectory to several key experiences. I’d like to thank former College of Computing Professor and Associate Dean Kurt Eiselt for introducing me to functional programming in the form of Scheme; he also offered me and some of my friends an enthralling glimpse into the sheer depth of computing as a field in his introductory CS1311X programming class. Early in my undergraduate experience, Jim Greenlee and Bill Leahy challenged me in various classes, and their standards encouraged me to excel. I’d also like to thank Professor Olin Shivers for both his shrewd dissertation advice and for his eye-opening compilers class; he uses his classes as a pulpit to encourage students to pursue advanced research in computer science. In my second year at Georgia Tech, I started doing undergraduate research with Professor Kishore Ramachandran. Little did I know at the time that I would be a member of his

organized on two key components: the Hadoop Distributed File System (HDSF) for file storage and MapReduce, a distributed processing system for intensive cloud data applications. The main features of this structure are scalability, increased fault tolerance, efficiency and high-performance for the whole