High-Performance Persistent Storage System for BigData Analysis E-Book

Piyush Saxena

• Herausgeber: GRIN Verlag
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch
• Veröffentlichungsjahr: 2014

Beschreibung

Master's Thesis from the year 2014 in the subject Computer Science - Applied, grade: 82.00, , course: M.Tech CS&E, language: English, abstract: Hadoop and Map reduce today are facing huge amounts of data and are moving towards ubiquitous for big data storage and processing. This has made it an essential feature to evaluate and characterize the Hadoop file system and its deployment through extensive benchmarking. We have other benchmarking tools widely available with us today that are capable of analyzing the performance of the Hadoop system but they are made to either run in a single node system or are created for assessing the storage device that is attached and its basic characteristics as top speed and other hardware related details or manufacturer’s details. For this, the tool used is HiBench that is an essential part of Hadoop and is comprehensive benchmark suit that consist of a complete deposit of Hadoop applications having micro bench marks & real time applications for the purpose of benchmarking the performance of Hadoop on the available type of storage device (i.e. HDD and SSD) and machine configuration. This is helpful to optimize the performance and improve the support towards the limitations of Hadoop system. In this research work we will analyze and characterize the performance of external sorting algorithm in Hadoop (MapReduce) with SSD and HDD that are connected with various Interconnect technologies like 10GigE, IPoIB and RDBAIB. In addition, we will also demonstrate that the traditional servers and old Cloud systems can be upgraded by software and hardware up gradations to perform at par with the modern technologies to handle these loads, without spending ruthlessly on up gradations or complete changes in the system with the use of Modern storage devices and interconnect networking systems. This in turn reduces the power consumption drastically and allows smoother running of large scale servers with low latency and high throughput allowing use of the utmost power of the processors for the big data flowing in the network.