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

当前Internet中的许多分布式应用程序都被大规模复制，以确保无与伦比的数据健壮性和可伸缩性；然而，持续的数据混乱（即源的更新）和延迟的同步会导致这些系统的过时，从而降低性能。该项目的目标是开创一种数据复制的随机理论，该理论可以解决一般非泊松点过程同步中的非平凡依赖问题，设计更精确的采样和预测算法来测量数据流失率，解决新的多源和多副本过时优化问题，建立对合作和多跳复制的新的基本理解，并模拟真实源的非平稳更新过程。现在无处不在的云技术已经成为一个巨大的数据消费者和生成器，必须存储、复制和传输到各种客户端。该项目侧重于理解这些系统中数据演变和过时的理论和实验特性，其结果可能会通过创造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，接触到德州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研究活动，培养在大规模网络系统的理论和实验方面都有知识的全面发展的博士生，吸引STEM领域代表性不足的学生群体，通过德州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.