Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks

The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Year

2016

Author dc:creator

• Kambatla, Karthik Shashank

Contributors dc:contributor

• Ananth Y Grama
• Dongyan Xu
• Sonia Fahmy
• Mathias Payer
• Aniket Kate

• big data
• distributed computing
• distributed systems

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/1379

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2595