Collaborative Research: III: Small: High-Performance Scheduling for Modern Database Systems

协作研究：III：小型：现代数据库系统的高性能调度

• 批准号: 2322974
• 负责人: Benjamin Berg
• 金额: $ 27.5万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322974&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Small; Performance

The goal of this project is to develop and test algorithms for scheduling a stream of parallelizable database queries. The challenge is that queries are very heterogeneous. They differ in how parallelizable they are, and their level of parallelizability can also change over time. They also differ in their inherent amount of work. Given limited resources, it is not obvious how to allocate these resources across the different queries over time. This project develops models to optimize the scheduling of these parallelizable queries in modern databases. In addition to developing new modeling tools, this project includes the development of new computer science courses to teach modeling to future researchers.This project aims to improve query scheduling in modern databases via a stochastic modeling approach. Most current systems serve queries in a First-Come-First-Served order, a policy that can lead to excessive queueing times. Furthermore, this simple scheduling policy does not account for the differing levels of parallelizability and service requirements of different types of queries. Using stochastic models and queueing theory, the project develops new scheduling policies that maximize the utilization of system resources such as compute and memory in order to greatly reduce query latencies. The project targets scheduling both on a fixed set of hardware and in the cloud where resources can be scaled dynamically to meet user demand.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.

这个项目的目标是开发和测试调度可并行数据库查询流的算法。 挑战在于查询是非常异构的。 它们的可并行性不同，它们的可并行性水平也会随着时间的推移而变化。 它们的内在工作量也不同。 由于资源有限，如何随着时间的推移在不同的查询之间分配这些资源并不明显。 这个项目开发模型来优化这些并行查询在现代数据库中的调度。 除了开发新的建模工具外，该项目还包括开发新的计算机科学课程，以向未来的研究人员教授建模。该项目旨在通过随机建模方法改进现代数据库中的查询调度。 大多数当前的系统以先到先得的顺序提供查询，这种策略可能导致过多的查询时间。 此外，这种简单的调度策略没有考虑不同类型的查询的不同级别的并行化和服务要求。 利用随机模型和调度理论，该项目开发了新的调度策略，最大限度地利用系统资源，如计算和内存，以大大减少查询延迟。 该项目的目标是在一组固定的硬件上和云中进行调度，其中资源可以动态扩展以满足用户需求。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。