NSF-BSF:CIF:Small:Reliable Data Storage on Sampling Channels

NSF-BSF:CIF:Small：采样通道上的可靠数据存储

• 批准号: 2330309
• 负责人: Lara Dolecek
• 金额: $ 60万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330309&HistoricalAwards=false
• 关键词: NSF; BSF; CIF; Small; Reliable

Explosive data growth and insatiable demand for data processing and computing have created an urgent need to develop sophisticated information systems able to timely handle advanced data processing requests. Emerging solutions increasingly employ more complex, hyper-scaled, decentralized data storage systems and sub-systems, ranging from the multi-cloud to block-chain assisted networks. Common approaches to combatting errors and failures in data storage systems add redundancy to blocks of user data before they are stored. Existing mathematical solutions that describe these operations intrinsically assume that the stored data block can be accessed in its entirety and in a centralized manner. To meet the requirements of emerging systems that have both new types of data tasks and a decentralized data organization, there is now an identified need to develop new mathematical models and abstractions that will provide relevant theoretical foundations and practical design principles for decentralized data storage systems. The project addresses this need by introducing the concept of a sampling channel, which serves to mathematically capture the relationship between the user data and its representation that is available for processing. The key feature of the sampling concept, motivated by the properties of modern data systems, is that it assumes neither centralized access nor full-block availability. The project will then develop mathematical tools and techniques that are suitable for sampling channels of different types. In addition to technical and scientific contributions, this project also has a potential to reduce resource consumption and increase robustness to adversarial participants in future information systems. This project will develop a new mathematical framework centered around sampling channels, with the focus on their applications to decentralized data storage systems. Moving beyond the basic and well-studied case of uncontrolled sampling, emerging distributed systems and applications motivate the usage of controlled sampling, which is classified into two levels depending on whether one has the exact control on the sampling or only through its distribution. Channel coding is a scientific discipline aimed at maximizing the user-data robustness and minimizing system redundancy through principled mathematical frameworks. In this context, the investigators will focus their attention on the theory and practice of efficiently decodable codes with sparse graphical representations. Such codes offer sufficient flexibility to be utilized for the sampling categories and system tasks that are needed in emerging systems. Equipped with initial compelling findings that point to code-design principles that depart from the methods proven successful in conventional settings, the investigators will rigorously analyze existing code families, and offer techniques on how to adapt them to the studied setting, as well as develop new code families. The project will involve theoretically establishing relevant code properties, performance guarantees and trade-offs, and practical evaluations and comparisons.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

爆炸性的数据增长和对数据处理和计算的无限需求已经迫切需要开发能够及时处理高级数据处理请求的复杂信息系统。新兴解决方案越来越多地采用更复杂，超级缩放，分散的数据存储系统和子系统，从多云到块链辅助网络不等。在数据存储系统中打击错误和故障的常见方法为用户数据存储之前为块的块增加了冗余。现有描述这些操作的现有数学解决方案本质地认为可以以整体和集中式的方式访问存储的数据块。为了满足具有新型数据任务和分散数据组织的新兴系统的要求，现在已经确定需要开发新的数学模型和摘要，这些模型和摘要将为分散数据存储系统提供相关的理论基础和实用设计原理。该项目通过介绍采样通道的概念来满足这一需求，该概念可在数学上捕获可用于处理的用户数据与其表示形式之间的关系。采样概念的关键特征是由现代数据系统的属性促进的，它既不假定集中式访问也不是全块可用性。然后，该项目将开发适用于不同类型的采样通道的数学工具和技术。除技术和科学贡献外，该项目还具有减少资源消耗并增加对对抗性参与者的鲁棒性的潜力。该项目将开发一个以采样渠道为中心的新数学框架，重点关注其应用于分散的数据存储系统。超越了不受控制的采样，新兴的分布式系统和应用程序的发展，激励了受控抽样的使用，该采样的使用情况，该级别分为两个级别，具体取决于对采样的确切控制还是仅通过其分布进行控制。渠道编码是一门科学学科，旨在通过原则上的数学框架来最大化用户数据鲁棒性和最大程度地减少系统冗余。在这种情况下，调查人员将把注意力集中在有效解码代码的理论和实践上，具有稀疏的图形表示。这些代码提供了足够的灵活性，可用于新兴系统中所需的采样类别和系统任务。配备了最初引人注目的发现，这些发现指出了代码设计原则，这些原则偏离了在常规环境中被证明是成功的方法，研究人员将严格分析现有代码家庭，并提供有关如何使其适应研究环境以及开发新代码家族的技术。该项目将涉及从理论上建立相关的代码属性，绩效保证和权衡以及实际评估和比较。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。