BIGDATA: Collaborative Research: F: Holistic Optimization of Data-Driven Applications

BIGDATA：协作研究：F：数据驱动应用程序的整体优化

• 批准号: 1546543
• 负责人: Shan Lu
• 金额: $ 60万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546543&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; Holistic; Optimization

We interact with online shopping and banking websites on a daily basis. Many of these websites are powered by data-driven applications. Such application often consists of two parts: an application hosted on an application server, and a database management system (DBMS) hosted on a separate server from the application server that maintains persistent data. Unfortunately, many data-driven applications suffer from performance problems, such as taking a long time to load a page or inability to scale up to serve large number of clients simultaneously. The state of the art in discovering and fixing performance problems in data-driven applications is to examine the two parts of the application separately, and doing so misses many opportunities in discovering and fixing such problems. Unlike prior approaches, in this project we will treat the DBMS and the application in tandem. In particular, we will devise new techniques and tools to help identify performance problems, understand the cause of such problems, and fix them automatically. This project will open up new opportunities in cross-layer program compilation and optimization, with the practical goal of improving the performance of data-driven applications that will have a significant impact in many aspects of our daily lives. The findings from this project will be incorporated into undergraduate and graduate software engineering, introduction to data management, and compiler classes to be offered at the University of Chicago and the University of Washington. The outreach activities of this project will include engaging and advising students through special programs geared toward under-represented groups such as the Distributed Research Experiences for Undergraduates (DREU) organized by CRA-W (Computing Research Association -- Women) and Diversity Workshops organized by CRA-W.Specifically, the proposed research consists of three thrusts: (1) a new cross-layer program analysis framework that produces an end-to-end profile of data-driven applications by understanding the application code, the queries that the application sends to the DBMS, and how the DBMS processes such queries; (2) a program analysis and testing framework that identify performance problems in data-driven applications by leveraging the end-to-end profile created from (1); and (3) new means to optimize data-driven applications by transforming both the application code and the queries that are issued. These three thrusts will work together to improve the performance of data-driven applications and help programmers detect performance problems during development. Software developed by this project, benchmarks used for evaluation, and performance comparison with existing techniques will be released to public domain through the project website. Further information will be available at the project website (https://people.eecs.berkeley.edu/~akcheung/coopt.html).

我们每天都与网上购物和银行网站互动。这些网站中的许多都是由数据驱动的应用程序提供支持的。这样的应用程序通常由两部分组成：托管在应用程序服务器上的应用程序，以及托管在与维护持久数据的应用程序服务器不同的服务器上的数据库管理系统（DBMS）。不幸的是，许多数据驱动的应用程序都存在性能问题，例如加载页面需要很长时间，或者无法扩展以同时为大量客户端提供服务。在数据驱动的应用程序中发现和修复性能问题的最新技术是分别检查应用程序的两个部分，这样做会错过许多发现和修复此类问题的机会。与以前的方法不同，在这个项目中，我们将把DBMS和应用程序放在一起处理。特别是，我们将设计新的技术和工具来帮助识别性能问题，了解这些问题的原因，并自动修复它们。该项目将在跨层程序编译和优化方面开辟新的机会，其实际目标是提高数据驱动应用程序的性能，这些应用程序将对我们日常生活的许多方面产生重大影响。这个项目的研究结果将被纳入本科生和研究生的软件工程，介绍数据管理，并在芝加哥大学和华盛顿大学提供编译器类。该项目的外展活动将包括通过面向代表性不足群体的特别计划，如由CRA-W组织的本科生分布式研究经验（DREU），吸引学生并为学生提供咨询具体而言，拟议的研究包括三个重点：（1）新的跨层程序分析框架，其通过理解应用程序代码、应用程序发送到DBMS的查询以及DBMS如何处理这些查询来产生数据驱动应用程序的端到端简档;（2）程序分析和测试框架，其通过利用从（1）创建的端到端简档来识别数据驱动应用程序中的性能问题;以及（3）通过转换应用程序代码和发出的查询来优化数据驱动应用程序的新手段。这三个方面将共同提高数据驱动应用程序的性能，并帮助程序员在开发过程中检测性能问题。该项目开发的软件、用于评估的基准以及与现有技术的性能比较将通过项目网站向公共领域发布。更多信息可在项目网站（https：//people.eecs.berkeley.edu/coopt.html）上查阅。