CNS: Core: Small: Consistent, Geo-Distributed Data Stores on the Public Cloud Using Erasure Coding

CNS：核心：小型：使用纠删码在公共云上实现一致的地理分布式数据存储

• 批准号: 2211045
• 负责人: Viveck Cadambe
• 金额: $ 59.38万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211045&HistoricalAwards=false
• 关键词: CNS; Core; Small; Consistent; Geo

Cloud-based data stores employ data replication combined with algorithms that allow consistent, concurrent access to data to offer low response times to their global client-bases. Erasure coding (EC) is a generalization of replication known to be more storage efficient for the same fault tolerance in theory. However, reaping the benefits of EC for geo-distributed data stores in practice poses several new scientific challenges. The overarching goal of this project is to address these challenges by developing a comprehensive understanding of the response time vs. cost trade-off for EC-based consistent geo-distributed stores. The research will be conducted in the following four thrusts: (i) development of EC techniques and distributed algorithms to satisfy consistency criteria known as eventual and causal consistency, (ii) development of efficient, EC-compatible reconfiguration algorithms that provably maintain consistency despite changes in object configurations, (ii) development of analytical models of performance and cost, and associated optimization frameworks, and (iv) integration of developed techniques into Apache Cassandra. The proposed research will be conducted in the context of the public cloud, where inter-DC latencies and pricing information are readily available allowing competing schemes to be compared in a fair manner.The proposed research could lead to cost reductions for geo-distributed data stores hosted on public clouds, which are fundamental building blocks for several applications including collaborative editing, social media, financial transactions, reservation systems, and multi-player gaming. The validation plan involves experiments performed using an Apache Cassandra-based prototype that will be made open source. The prototype will serve as a testbed for other researchers and engineers to plug in their own algorithms and compare findings. The proposed research has a cross-disciplinary nature and will be supplemented with an education plan that involves development of survey articles and curricular integration.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.

基于云的数据存储将数据复制与允许一致、并发访问数据的算法相结合，从而为其全球客户群提供较低的响应时间。擦除编码(EC)是已知的复制的泛化，在理论上对于相同的容错来说存储效率更高。然而，在实践中为地理分布的数据存储获得EC的好处带来了几个新的科学挑战。该项目的总体目标是通过全面了解基于EC的一致地理分布商店的响应时间与成本之间的权衡来应对这些挑战。研究将在以下四个方面进行：(I)开发EC技术和分布式算法，以满足称为最终一致性和因果一致性的一致性标准；(Ii)开发有效的、EC兼容的重新配置算法，即使对象配置发生变化，仍可证明保持一致性；(Ii)开发性能和成本分析模型，以及相关的优化框架；以及(Iv)将开发的技术整合到Apache Cassandra中。拟议的研究将在公共云的背景下进行，其中DC之间的延迟和定价信息随时可用，从而可以公平地比较竞争方案。拟议的研究可能会降低托管在公共云上的地理分布式数据存储的成本，这些数据存储是协作编辑、社交媒体、金融交易、预订系统和多玩家游戏等应用的基本构建块。验证计划包括使用基于阿帕奇卡桑德拉的原型进行的实验，该原型将被开源。原型将作为其他研究人员和工程师的试验台，插入他们自己的算法并比较结果。这项拟议的研究具有跨学科的性质，并将辅之以一项教育计划，其中包括开发调查文章和课程整合。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Brief Announcement: CausalEC: A Causally Consistent Data Storage Algorithm based on Cross-Object Erasure Coding

简短公告：CausalEC：基于跨对象纠删码的因果一致数据存储算法

• DOI: 10.1145/3583668.3594603
• 发表时间: 2023
• 期刊: PODC '23: Proceedings of the 2023 ACM Symposium on Principles of Distributed Computing
• 作者: Cadambe, Viveck R.;Lyu, Shihang
• 通讯作者: Lyu, Shihang