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.

基于云的数据存储采用数据复制与算法相结合，这些算法允许一致，同时访问数据以提供较低的响应时间，以便为其全局客户端基础提供。擦除编码（EC）是复制的概括，已知在理论上对相同的容错性更有效地储存。但是，在实践中收获EC对地理分布的数据存储的好处带来了一些新的科学挑战。该项目的总体目标是通过对基于EC一致的地理分配商店的响应时间与成本权衡的全面了解来应对这些挑战。该研究将在以下四个方案中进行：（i）开发EC技术和分布式算法以满足一致性标准，称为最终和因果关系一致性，（ii）开发有效的，EC兼容的重新配置算法，尽管有变化，但仍具有一致性在对象配置中，（ii）开发性能和成本的分析模型以及相关的优化框架，以及（iv）将开发技术集成到Apache Cassandra中。拟议的研究将在公共云的背景下进行，在公共云的背景下，DC延迟和定价信息很容易获得，允许以公平的方式比较竞争方案。拟议的研究可能会导致地理分布数据存储的成本降低托管在公共云上，这是多个应用程序的基本构建块，包括协作编辑，社交媒体，金融交易，预订系统和多玩家游戏。验证计划涉及使用基于Apache Cassandra的原型进行的实验，该原型将成为开源。该原型将作为其他研究人员和工程师插入自己的算法并比较发现的测试台。拟议的研究具有跨学科的性质，并将补充一项涉及调查文章和课程整合的教育计划。该奖项反映了NSF的法定任务，并通过基金会的知识分子和更广泛的影响来评估NSF的法定任务。审查标准。

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