CCF-BSF: CIF: Small: Collaborative Research: Coding and Information - Theoretic Aspects of Local Data Recovery

CCF-BSF：CIF：小型：协作研究：编码和信息 - 本地数据恢复的理论方面

• 批准号: 1618512
• 负责人: Arya Mazumdar
• 金额: $ 25万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618512&HistoricalAwards=false
• 关键词: CCF; BSF; CIF; Small; Collaborative

This project studies fundamental problems in data coding that can improve the efficiency of distributed storage systems by increasing data reliability and availability while reducing storage overhead compared to existing industry standards. The results of this research can benefit storage applications ranging from financial, scientific monitoring, and signal processing to social networks and sharing platforms. The new combinatorial, coding, and information theoretic tools developed in this project will be incorporated in course curricula in the respective institutions of the principal investigators.Data coding with locality, the focus of this project, is a rapidly developing area of coding theory that was initially motivated by applications in distributed storage, and has links to many areas of network science (e.g., index coding and network coding) as well as to computer science. This project advances the theory and practice of data coding with local recovery by investigating broad implications of the locality constraint in coding problems. These include studying new error-correcting code families and their decoding, fundamental limitations on the code parameters and capacity bounds under the requirements of local data recovery. The newly designed coding schemes developed in this project will be validated through implementation and evaluation in simulated computer environment, aiming at enhanced performance of data coding in current industry solutions.

本项目研究数据编码中的基本问题，通过提高数据可靠性和可用性来提高分布式存储系统的效率，同时减少与现有行业标准相比的存储开销。这项研究的结果可以使存储应用受益，从金融、科学监测、信号处理到社交网络和共享平台。在这个项目中开发的新的组合、编码和信息理论工具将被纳入主要研究人员各自机构的课程。具有局部性的数据编码是这个项目的重点，是编码理论的一个快速发展的领域，它最初是由分布式存储的应用程序驱动的，并且与网络科学的许多领域（例如，索引编码和网络编码）以及计算机科学有联系。本项目通过研究局域约束在编码问题中的广泛意义，推进了具有局部恢复的数据编码的理论和实践。这包括研究新的纠错码族及其译码，对码参数的基本限制以及局部数据恢复要求下的容量界限。本项目开发的新设计的编码方案将通过在模拟计算机环境中实施和评估来验证，旨在提高当前行业解决方案的数据编码性能。