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.

该项目的目标是开发和测试用于调度可并行化数据库查询流的算法。挑战在于查询的异构性很强。它们的并行化程度不同，它们的并行化程度也会随着时间的推移而变化。它们本身的工作量也不同。由于资源有限，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。该项目开发模型来优化现代数据库中这些可并行化查询的调度。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，以教授未来的研究人员建模。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。大多数当前系统以先到先得的顺序提供查询，这一策略可能会导致过多的排队时间。此外，这种简单的调度策略没有考虑到不同类型查询的不同并行化级别和服务需求。利用随机模型和排队论，该项目开发了新的调度策略，以最大限度地利用计算和内存等系统资源，以极大地减少查询延迟。该项目的目标是在固定的硬件集和云中进行调度，云中的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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。