CSR: Small: Yesterday's News: Theory of Staleness under Data Churn

CSR：小：昨天的新闻：数据搅动下的陈旧理论

• 批准号: 1319984
• 负责人: Dmitri Loguinov
• 金额: $ 47.34万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319984&HistoricalAwards=false
• 关键词: CSR; Small; Yesterday; News; Theory

Many distributed applications in the current Internet are massively replicated to ensure unsurpassed data robustness and scalability; however, constant data churn (i.e., update of the source) and delayed synchronization lead to staleness and thus lower performance in these systems. The goal of this project is to pioneer a stochastic theory of data replication that can tackle non-trivial dependency issues in synchronization of general non-Poisson point processes, design more accurate sampling and prediction algorithms for measuring data churn, solve novel multi-source and multi-replica staleness-optimization problems, establish new fundamental understanding of cooperative and multi-hop replication, and model non-stationary update processes of real sources. The now omnipresent cloud technology has become a vast consumer and generator of data that must be stored, replicated, and streamed to a variety of clients. This project focuses on understanding theoretical and experimental properties of data evolution and staleness in such systems, whose outcomes are likely to impact Internet computing through creation of insight that leads to better content-distribution mechanisms, more accurate search results, and ultimately higher satisfaction among everyday users. Furthermore, this project blends a variety of inter-disciplinary scientific areas, reaches out to the student population at Texas A&M to engage them in research activities from early stages of their careers, trains well-rounded PhD students knowledgeable in both theoretical and experimental aspects of large-scale networked systems, engages under-represented student groups in STEM fields, disseminates information through two new seminars at Texas A&M, and shares data models and experimental results with the public.

当前Internet中的许多分布式应用程序都经过大量复制，以确保无与伦比的数据鲁棒性和可扩展性；但是，恒定数据流失（即源的更新）和延迟同步导致稳定性，从而在这些系统中的性能较低。 The goal of this project is to pioneer a stochastic theory of data replication that can tackle non-trivial dependency issues in synchronization of general non-Poisson point processes, design more accurate sampling and prediction algorithms for measuring data churn, solve novel multi-source and multi-replica staleness-optimization problems, establish new fundamental understanding of cooperative and multi-hop replication, and model实际来源的非平稳更新过程。现在无所不在的云技术已成为庞大的消费者和数据的生成器，必须存储，复制并流向各种客户。该项目的重点是理解此类系统数据演变和陈旧性的理论和实验性能，这些系统的结果可能通过创建洞察力来影响互联网计算，从而导致更好的内容分布机制，更准确的搜索结果，并最终在日常用户中获得更高的满意度。 Furthermore, this project blends a variety of inter-disciplinary scientific areas, reaches out to the student population at Texas A&M to engage them in research activities from early stages of their careers, trains well-rounded PhD students knowledgeable in both theoretical and experimental aspects of large-scale networked systems, engages under-represented student groups in STEM fields, disseminates information through two new seminars at Texas A&M, and与公众共享数据模型和实验结果。