Evolution and analysis of distributed file systems in cloud storage: Analytical survey

Handling of Big Data and cloud computing are the two important prime concerns which have become more and more popular in recent years. Due to tremendous hike in data production, the need for the efficient processing, transaction storage or retrieval, and management of the structured and unstructured data have become one of the important issues of the IT industry. In this paper, we examine the concept of evolution of various distributed file systems, advantages, and limitations with respect to the cloud computing paradigm. Distributed File System is a client-based application which permits its users to access, process and modify the data which is stored on a remote server as if it existed on their local systems. The Google file system (GFS), a proprietary distributed file system was developed by Google to meet its own growing big data need, which involved the utilization of commodity hardware in managing the massive generation of raw data. Inspired by GFS, Hadoop Distributed File System (HDFS) was developed, which is an open-source community project. It is designed to store large chunks of data sets consistently as well as providing high bandwidth sets for streaming data on client based applications. In this paper, we describe the GFS and HDFS models in detail and relate these two distributed file systems on the basis of their properties and characteristic behavior in different environments. We also discuss several techniques and modifications to prevent and mitigate the limitations of these file systems.