CAREER: Reliability in Large-Scale Storage
Description: With the advent of large scale distributed storage systems, cloud computing and commercial data storage applications, there is a renewed interest in the coding and information theory in reliabile issues data storage. In large networked databases, faster updates and quick repair requirements must be integrated with reliable data storage protocols. These requirements bring new dimensions and parameters to the traditional optimization problem of information theory.
In this project we propose, for the first time, a model of large-scale storage that accounts for the topology of storage networks. Previously, storage topology was never a concern of the code designers. Further, we study the update efficiency and local repair (fast recovery) properties of error-correcting codes suitable for storage. For all of these, we will analyze the fundamental limits of systems, as well as propose explicit (fast algorithmic) constructions of codes. Several tools from graph and network theory, combinatorics and optimization theory will be leveraged to find performance limits and devise coding algorithms.
By cutting redundancy and allowing faster processing, the codes developed from this project will directly save energy consumption in data centers. Existing and new collaborations of the principal investigator will facilitate industry cooperation and increase the transition to practice of results generated from this project. Elements of this endeavor will be integrated with the courses taught by the principal investigator. The findings will be disseminated through publications in peer-reviewed venues and made available in the form of technical reports for public access on the investigator's webpage. Finally, motivated by practical applications and extending across various disciplines, this project is representative of an intriguing area of engineering science, and will attract a diverse student base including undergraduate students.
Funder: National Science Foundation
Amount: $113,700
Funding type: grant
Funder scheme: Continuing Grant
Start date: 2021-04-01
End date: 2023-03-31
Investigator: Arya Mazumdar
Institution: University of California-San Diego
Funded outputs count: 9
Funder award ID: 2127929
OpenAlex ID: G1754370423