III: Small: Automatic Learning-based Services for Distributed Data Management Systems

III：小型：分布式数据管理系统的基于自动学习的服务

基本信息

批准号：
1815701
负责人：
Olga Papaemmanouil
金额：
$ 49.99万
依托单位：
Brandeis University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815701&HistoricalAwards=false
关键词：
III Small Automatic Learning based

项目摘要

The distributed nature of most database management systems (DBMSs) brings about new challenges to the daily tasks of database administrators. Apart from database tuning, today's administrators are often responsible for: (a) adapting data partitioning and replication schemes; (b) managing dynamic workloads to meet query prioritization and performance goals; as well as (c) provisioning computing resources on demand. Unfortunately, the complexity of these tasks often exceeds engineers' abilities to scientifically model them. To address this challenge, this project will couple existing learning-driven theory with distributed data management systems to have significant impact on the design of DBMSs. By leveraging advanced learning algorithms from machine learning and game theory, the project will allow DBMSs to move away from "hard-coded algorithmic intelligence," rigid data structures, and algorithms that are based on informal intuition. Instead, DBMSs will be able to incorporate "learning-based intelligence" that provides reasoning on numerous decisions based on pattern recognition capabilities and mathematically proven insights. Leveraging information to learn and adapt could unleash tremendous potential as databases evolve to systems capable of automatically tuning themselves. The results of this project will also reduce the human effort of database administrators as they will be offered predictive models, decision tools and insight to the interplay between data distribution, workload management, and query performance. The project will provide solutions to some of the key technical challenges that arise when tuning the performance of dynamic workloads on distributed data management systems. This research will lead to the design of new algorithms and learning-based frameworks for supporting data distribution, replication, query dispatching, query scheduling, and performance prediction without human intervention. These techniques will be automatically customized to application-level performance goals and user-defined query priorities and will allow distributed DBMSs to naturally handle dynamic workloads, changing data access patterns, and varying resource availability. By coupling unsupervised/supervised learning, deep learning and game theory to data management tasks the project will transform data management systems to "database science" tools through which system administrators will be able to explore and derive insight on the factors that affect the performance of a database and its deployed applications. As a result, the project will deliver complex predictive and correlation models between query, data, resource-related features and system performance.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.

大多数数据库管理系统 (DBMS) 的分布式特性给数据库管理员的日常任务带来了新的挑战。除了数据库调整之外，当今的管理员通常还负责： (a) 调整数据分区和复制方案； (b) 管理动态工作负载以满足查询优先级和性能目标；以及(c)按需配置计算资源。不幸的是，这些任务的复杂性往往超出了工程师对其进行科学建模的能力。为了应对这一挑战，该项目将把现有的学习驱动理论与分布式数据管理系统结合起来，对 DBMS 的设计产生重大影响。通过利用机器学习和博弈论中的先进学习算法，该项目将使 DBMS 摆脱“硬编码算法智能”、严格的数据结构和基于非正式直觉的算法。相反，DBMS 将能够整合“基于学习的智能”，根据模式识别功能和经过数学验证的见解对众多决策进行推理。随着数据库发展成为能够自动调整自身的系统，利用信息进行学习和适应可以释放巨大的潜力。该项目的结果还将减少数据库管理员的人力，因为他们将获得预测模型、决策工具以及对数据分布、工作负载管理和查询性能之间相互作用的洞察。该项目将为调整分布式数据管理系统上动态工作负载的性能时出现的一些关键技术挑战提供解决方案。这项研究将导致新算法和基于学习的框架的设计，以支持数据分发、复制、查询调度、查询调度和性能预测，而无需人工干预。这些技术将根据应用程序级性能目标和用户定义的查询优先级自动定制，并允许分布式 DBMS 自然地处理动态工作负载、不断变化的数据访问模式和变化的资源可用性。通过将无监督/监督学习、深度学习和博弈论与数据管理任务相结合，该项目将数据管理系统转变为“数据库科学”工具，系统管理员将能够通过这些工具探索并深入了解影响数据管理性能的因素。数据库及其部署的应用程序。因此，该项目将在查询、数据、资源相关功能和系统性能之间提供复杂的预测和关联模型。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查进行评估，被认为值得支持标准。