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）的分布式性质为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绩效目标；以及（c）根据需求进行计算资源。不幸的是，这些任务的复杂性通常超出了工程师对它们进行科学建模的能力。为了应对这一挑战，该项目将将现有的学习驱动理论与分布式数据管理系统相结合，以对DBMS的设计产生重大影响。通过利用机器学习和游戏理论的高级学习算法，该项目将允许DBMS脱离“硬编码算法智能”，“刚性数据结构”以及基于非正式直觉的算法。取而代之的是，DBMS将能够合并“基于学习的智能”，这些智能基于模式识别能力和数学证明的见解提供了许多决策的推理。利用信息来学习和适应可能会释放出巨大的潜力，因为数据库发展为能够自动调整自己的系统。该项目的结果还将减少数据库管理员的人力努力，因为他们将获得预测模型，决策工具和洞察力，以实现数据分配，工作负载管理和查询性能之间的相互作用。该项目将为调整分布式数据管理系统的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。这项研究将导致设计新算法和基于学习的框架，以支持数据分发，复制，查询调度，查询计划和绩效预测，而无需人工干预。这些技术将自动定制为应用程序级的性能目标和用户定义的查询优先级，并将允许分布式的DBMS自然处理动态工作负载，更改数据访问模式以及不同的资源可用性。通过将无监督/监督的学习，深度学习和游戏理论结合到数据管理任务，该项目将将数据管理系统转换为“数据库科学”工具，通过该工具，系统管理员将能够探索并洞悉影响数据库及其部署应用程序的性能的因素。结果，该项目将在查询，数据，与资源相关的功能和系统性能之间提供复杂的预测和相关模型。该奖项反映了NSF的法定任务，并使用基金会的智力优点和更广泛的影响评估审查标准，认为值得通过评估来获得支持。