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

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
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-08-01 至 2025-07-31

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中文摘要
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英文摘要
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.
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会议论文
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
影响因子: --
作者: [Cadambe, Viveck R., Lyu, Shihang]
通讯作者: Lyu, Shihang
Collaborative Research: CIF: Small: Approximate Coded Computing - Fundamental Limits of Precision, Fault-Tolerance, and Privacy
CIF: Medium: Collaborative Research: Coded Computing for Large-Scale Machine Learning
CAREER: An Information Theoretic Perspective of Consistent Distributed Storage Systems
CRII: CIF: Towards a Systematic Interference Alignment Approach for Network Information Flow
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