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Evaluation of Storage Systems for Big Data Analytics


Abstract Recent trends in big data storage systems show a shift from disk centric models to memory centric models. The primary challenges faced by these systems are speed, scalability, and fault tolerance. It is interesting to investigate the performance of these two models with respect to some big data applications. This thesis studies the performance of Ceph (a disk centric model) and Alluxio (a memory centric model) and evaluates whether a hybrid model provides any performance benefits with respect to big data applications. To this end, an application TechTalk is created that uses Ceph to store data and Alluxio to perform data analytics. The functionalities of the application include offline lecture storage, live recording of classes, content ana... (more)
Created Date 2017
Contributor NAGENDRA, SHILPA (Author) / Huang, Dijiang (Advisor) / Zhao, Ming (Committee member) / Maciejewski, Ross (Committee member) / Chung, Chun-Jen (Committee member) / Arizona State University (Publisher)
Subject Computer science / Alluxio / Big Data Analytics / Ceph / Disk Centric / Hybrid / Memory Centric
Type Masters Thesis
Extent 150 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis