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

当前Internet中的许多分布式应用都是大规模复制的，以确保无与伦比的数据健壮性和可扩展性；然而，持续的数据搅动(即源的更新)和延迟的同步会导致过时，从而降低这些系统的性能。该项目的目标是开创数据复制的随机理论，解决一般非Poisson点过程同步中的非平凡依赖问题，设计更准确的测量数据流失的采样和预测算法，解决新的多源和多副本失效优化问题，建立对合作和多跳复制的新的基本理解，并对真实源的非平稳更新过程进行建模。现在无处不在的云技术已经成为必须存储、复制和传输到各种客户端的数据的巨大消费者和生成者。该项目专注于了解此类系统中数据演化和陈旧的理论和实验特性，其结果可能会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发机制、更准确的搜索结果，并最终提高日常用户的满意度。此外，该项目融合了各种跨学科的科学领域，接触到德克萨斯农工大学的学生群体，让他们从职业生涯的早期阶段就参与到研究活动中来，培养全面发展的博士生，了解大规模网络系统的理论和实验方面的知识，让STEM领域中代表性不足的学生群体参与进来，通过德克萨斯农工大学的两个新研讨会传播信息，并与公众分享数据模型和实验结果。

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.