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CAREER: An AI Empowered Architecture-Centric Framework for Systematic Software-Performance Optimization

职业：人工智能赋能的以架构为中心的系统软件性能优化框架

基本信息

批准号：
2044888
负责人：
Lu xiao
金额：
$ 48.62万
依托单位：
Stevens Institute of Technology
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044888&HistoricalAwards=false
关键词：
CAREER AI Empowered Architecture Centric

项目摘要

Software performance is a critical quality attribute measured by the timeliness, responsiveness, and resource consumption of a system at run-time. Performance issues can lead to severe consequences, including budget overrun, project delay, and market loss. In past decades, Moore’s Law greatly benefited software performance by offering exponentially more powerful hardware resources. Unfortunately, in recent years, advancement on the hardware side is reaching physical limitations. Therefore, the need for transforming performance-engineering techniques on the software side becomes more critical and urgent. This research aims to develop a framework composed of cutting-edge techniques that can transform how practitioners manage, identify, and address real-life software performance issues. This project focuses on three gaps in the current research and practice of software performance engineering. First, there is a lack of a large-scale database and comprehensive understanding of common performance issues in real-world projects. Second, despite the ubiquity of software architecture, there is inadequate understanding of how complicated architectural connections contribute to performance issues and of how fixing harmful connections leads to rewarding optimization. Finally, there is a lack of a systematic understanding of different levels of optimization tactics -- reusable resolution patterns -- for addressing real-life performance issues with different concerning scopes. This project bridges these gaps through three progressive research thrusts. Thrust 1 will construct and maintain an open database of common types of real-life performance issues, leveraging natural-language-processing techniques. Thrust 2 will contribute an architecture-centric approach that identifies architecturally connected performance-optimization opportunities, based on novel graph-embedding techniques. The proposed approach will seamlessly integrate architecture modeling and performance analysis. Thrust 3 will provide developers with actionable guidance in mitigating performance issues. This thrust will first curate multi-level performance optimization tactics from extensive empirical studies, and then build a recommender system to suggest the proper tactics to developers in performance optimization.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.

软件性能是通过系统在运行时的及时性、响应性和资源消耗来衡量的关键质量属性。绩效问题可能会导致严重的后果，包括预算超支、项目延误和市场损失。在过去的几十年里，摩尔定律通过提供成倍增长的更强大的硬件资源，极大地提高了软件性能。不幸的是，近年来，硬件方面的进步正在达到物理限制。因此，在软件方面转换性能工程技术的需要变得更加关键和紧迫。这项研究旨在开发一个由尖端技术组成的框架，该框架可以改变从业者管理、识别和解决实际软件性能问题的方式。本项目关注当前软件性能工程研究和实践中的三个空白。首先，缺乏大型数据库和对现实世界项目中常见性能问题的全面了解。其次，尽管软件体系结构无处不在，但对于复杂的体系结构连接如何导致性能问题，以及修复有害连接如何导致有益的优化，还缺乏充分的理解。最后，缺乏对不同级别的优化策略--可重用解析模式--的系统理解，以解决具有不同关注范围的实际性能问题。该项目通过三项渐进的研究努力弥合了这些差距。推力1将利用自然语言处理技术，构建和维护一个开放的数据库，其中包括现实生活中常见的性能问题类型。推力2将基于新颖的图形嵌入技术，贡献一种以架构为中心的方法，识别与架构相关的性能优化机会。提出的方法将无缝集成体系结构建模和性能分析。推力3将为开发人员提供可操作的指导，以缓解性能问题。这一努力将首先从广泛的实证研究中挑选出多级别的性能优化策略，然后构建一个推荐系统，以向开发人员建议性能优化中的适当策略。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。