III: Small: Increase the Throughput of Non-Relational Databases through Theoretical Modeling and Optimization

III：小：通过理论建模和优化提高非关系数据库的吞吐量

• 批准号: 1619463
• 负责人: Neal Young
• 金额: $ 50万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619463&HistoricalAwards=false
• 关键词: III; Small; Increase; Throughput; Non

The explosive growth of data is driving the rapid evolution of massive data-storage systems. These systems are widely used, not only in large-scale Internet services, but also in scientific projects in diverse areas such as astronomy, geography, and genetics. This project will increase the efficiency of these data-storage systems, which will allow processing more data at lower cost. There is the potential for a large societal impact as science and engineering research is made more cost-effective.More specifically, this project will work on improving non-relational databases with log-structured merge-tree storage architectures. One main focus will be on improving a key component of such systems, namely, compaction policies. Compaction policies are not yet well understood, but are crucial for system performance. To date, compaction policies have been designed by trial and error, guided mainly by empirical experience. The project will develop analytical models for compaction, validate and refine the models with empirical testing, design improved policies that are optimal according to the models, and deploy these policies in live systems. Further, the developed theoretical models will be leveraged to optimize non-relational database systems in handling high volumes of dynamic continuous queries, which arrive and expire rapidly.

数据的爆炸式增长推动了海量数据存储系统的快速发展。这些系统被广泛使用，不仅在大型互联网服务中，而且在天文学、地理学和遗传学等不同领域的科学项目中。该项目将提高这些数据存储系统的效率，从而可以以更低的成本处理更多的数据。随着科学和工程研究变得更具成本效益，这有可能产生巨大的社会影响。更具体地说，这个项目将致力于用日志结构的合并树存储架构改进非关系数据库。一个主要的重点将是改进这种系统的一个关键组成部分，即压缩政策。压缩策略还没有被很好地理解，但对系统性能至关重要。迄今为止，紧缩政策主要是在经验经验的指导下，通过反复试验来设计的。该项目将开发用于压缩的分析模型，通过实证测试验证和完善模型，根据模型设计优化的策略，并在实际系统中部署这些策略。此外，将利用开发的理论模型来优化非关系数据库系统，以处理大量快速到达和过期的动态连续查询。