CIF:Small:Collaborative Research:Codes for Storage with Queues for Access

CIF：小：协作研究：带有访问队列的存储代码

• 批准号: 1718658
• 负责人: Pierce Cantrell
• 金额: $ 19万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718658&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Codes

Large volumes of data, which are being collected for the purpose of knowledge extraction, have to be reliably, efficiently, and securely stored. Retrieval of large data files from storage has to be fast. Large-scale cloud data storage and distributed file systems have become the backbone of many applications such as web searching, e-commerce, and cluster computing. Cloud services are implemented on top of a distributed storage layer that acts as a middleware to the applications, and also provides the desired content to the users, whose interests range from performing data analytics to watching movies. Users of cloud systems demand that their content and services be readily available and their data be reliably stored. Although there are apparent connections and trade-offs between these two objectives, so far they have been addressed mostly separately. The proposed research will characterize the interplay between reliable data storage and fast data access, and develop methods to jointly optimize these two main objectives of cloud storage. The project team will develop the methodology for design, analysis, and performance evaluation of a broad range of techniques for distributed storage that enable high reliability, robustness, and fast data retrieval. The research team will also develop schemes for storage and distributed computing which will optimize both reliability and the latency of data access. The methodology developed in the course of this project will allow the system operators to make their content and services readily available to users and support delay-sensitive applications ranging from individual video streaming to using online collaborative tools. This research will minimize the energy requirements of data centers, which have increased massively in recent years. The project will contribute to the broader areas of coding theory, information theory, and queueing theory, and open new ways of cross-pollination.This project focuses on efficient data access in distributed file systems that employ codes for reliable and efficient storage. Users of cloud systems demand that their content and services be readily available and their data be reliably stored. Although there are apparent connections and trade-offs between these two objectives, thus far they have been addressed mostly separately. This proposal follows findings that analyze how some of today?s solutions for reliable data storage affect the speed of data download under certain access models. This preliminary research has shown that, in some scenarios, the coding schemes used for increasing storage reliability can be further exploited for fast data access, while in others, the coding schemes that seemingly increase data availability actually fail to provide efficient access to popular content (so-called ?hot data?). The proposed research aims to characterize the interplay between reliable data storage and fast data access, and develop methods to jointly optimize these two main objectives of cloud storage. The proposed research will first identify and design schemes for coded-data access and derive (bounds on and estimates of) the expected download time for these schemes. Regardless of which data access scheme is used, the expected download time will depend on realistic service models as well as the distributed system service capacity provisioning and allocation schemes, which are then addressed. The work will also focus on the connections between the proposed research and the areas of efficient distributed computing and reduction in data center energy consumption. Preliminary results indicate that these areas are closely connected and the techniques developed for one area can benefit other areas.

为了知识提取而收集的大量数据必须可靠、高效和安全地存储。从存储中检索大型数据文件必须快速。大规模云数据存储和分布式文件系统已成为Web搜索、电子商务和集群计算等许多应用的支柱。云服务在分布式存储层之上实现，该层充当应用程序的中间件，并向用户提供所需的内容，用户的兴趣范围从执行数据分析到观看电影。云系统的用户要求他们的内容和服务随时可用，并且他们的数据被可靠地存储。虽然这两个目标之间存在着明显的联系和权衡，但迄今为止，它们大多是分开处理的。这项研究将描述可靠数据存储和快速数据访问之间的相互作用，并开发方法来共同优化云存储的这两个主要目标。该项目团队将开发一系列分布式存储技术的设计、分析和性能评估方法，以实现高可靠性、鲁棒性和快速数据检索。研究团队还将开发存储和分布式计算方案，优化数据访问的可靠性和延迟。在该项目过程中开发的方法将使系统运营商能够随时向用户提供其内容和服务，并支持从个人视频流到使用在线协作工具等对延迟敏感的应用程序。 这项研究将最大限度地减少近年来大幅增加的数据中心的能源需求。该项目将有助于编码理论，信息理论和嵌入式理论的更广泛的领域，并开辟新的交叉授粉方式。该项目的重点是在分布式文件系统，采用可靠和有效的存储代码的有效数据访问。云系统的用户要求他们的内容和服务随时可用，并且他们的数据被可靠地存储。虽然这两个目标之间存在着明显的联系和权衡，但迄今为止，它们大多是分开处理的。这项建议遵循的调查结果，分析如何一些今天？的可靠数据存储解决方案会影响某些访问模型下的数据下载速度。这项初步研究表明，在某些情况下，用于提高存储可靠性的编码方案可以进一步用于快速数据访问，而在其他情况下，看似提高数据可用性的编码方案实际上无法提供对流行内容的有效访问（所谓的？热数据？）。该研究旨在描述可靠数据存储和快速数据访问之间的相互作用，并开发方法来联合优化云存储的这两个主要目标。拟议中的研究将首先确定和设计编码数据访问方案，并推导出这些方案的预期下载时间（界限和估计）。无论使用哪种数据访问方案，预期的下载时间将取决于实际的服务模型以及分布式系统服务容量提供和分配方案，然后解决这些问题。这项工作还将重点关注拟议研究与高效分布式计算和减少数据中心能耗领域之间的联系。初步结果表明，这些领域是密切相关的，为一个领域开发的技术可以使其他领域受益。

项目成果

Minimizing the alphabet size of erasure codes with restricted decoding sets

使用受限解码集最小化纠删码的字母大小

• DOI: 10.1109/isit44484.2020.9174012
• 发表时间: 2020
• 期刊: IEEE International Symposium on Information Theory (ISIT
• 作者: Gonen, Mira;Haviv, Ishay;Langberg, Michael;Sprintson, Alex
• 通讯作者: Sprintson, Alex

Single-Server Multi-Message Individually-Private Information Retrieval with Side Information

带有辅助信息的单服务器多消息单独私有信息检索

• DOI: 10.1109/isit.2019.8849283
• 发表时间: 2019
• 期刊: 2019 IEEE International Symposium on Information Theory (ISIT
• 作者: Heidarzadeh, Anoosheh;Kadhe, Swanand;El Rouayheb, Salim;Sprintson, Alex
• 通讯作者: Sprintson, Alex

Multi-Server Private Linear Transformation with Joint Privacy

具有联合隐私的多服务器私有线性变换

• DOI: 10.1109/redundancy52534.2021.9606470
• 发表时间: 2021
• 期刊: 2021 XVII International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY
• 作者: Kazemi, Fatemeh;Sprintson, Alex
• 通讯作者: Sprintson, Alex

Private Computation with Individual and Joint Privacy

具有个人和联合隐私的私密计算

• DOI: 10.1109/isit44484.2020.9174040
• 发表时间: 2020
• 期刊: 2020 IEEE International Symposium on Information Theory (ISIT
• 作者: Heidarzadeh, Anoosheh;Sprintson, Alex
• 通讯作者: Sprintson, Alex

Private Computation with Side Information: The Single-Server Case

带有辅助信息的私有计算：单服务器案例

• DOI: 10.1109/isit.2019.8849287
• 发表时间: 2019
• 期刊: 2019 IEEE International Symposium on Information Theory (ISIT
• 作者: Heidarzadeh, Anoosheh;Sprintson, Alex
• 通讯作者: Sprintson, Alex