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: 2322973
• 负责人: Mor Harchol-Balter
• 金额: $ 32.5万
• 依托单位: Carnegie-Mellon University
• 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=2322973&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.

该项目的目的是开发和测试算法，以计划可行的数据库查询流。 挑战是查询非常异构。 它们在可行的程度上有所不同，并且它们的并行性水平也会随着时间而变化。 他们的固有工作数量也有所不同。 鉴于资源有限，随着时间的推移，如何在不同的查询中分配这些资源并不明显。 该项目开发了模型，以优化现代数据库中这些可行的查询的调度。 除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讲授建模。该项目旨在通过随机建模方法来改善现代数据库中的查询时间表。 大多数当前系统以先到先得的顺序提供查询，该策略可能导致过度排队时间。 此外，这种简单的调度策略不能说明不同类型查询的并行性和服务要求的不同级别。 使用随机模型和排队理论，该项目制定了新的调度策略，以最大程度地利用系统资源（例如计算和内存），以大大减少查询潜伏期。 该项目的目标是安排在固定的硬件和云上，在云中可以动态扩展资源以满足用户需求。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估值得支持。