CAREER: Towards High Performance Policy Evaluation

职业：迈向高绩效政策评估

• 批准号: 0845513
• 负责人: Alex Liu
• 金额: $ 42.5万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845513&HistoricalAwards=false
• 关键词: CAREER; Towards; Performance; Policy; Evaluation

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Policy-based computing is a critical component of many large-scale configurable systems because it enables dynamic adaptability of system behavior by changing policy configurations without reprogramming the systems. Policy evaluation, the process of checking whether a request satisfies a policy, is typically the performance bottleneck of policy-based systems. Most prior research has focused on the correctness (i.e., specification, design, and analysis) of policies. While correctness is an important issue, the adoption of policy-based computing may be limited if the resulting systems are not implemented efficiently and thus perform poorly. The high-level research objective of this project is to increase the effectiveness and adoption of policy-based computing by designing high performance policy evaluation algorithms and engines that can be adapted to support various policy languages. The primary focus of this project is the fast evaluation of security policies, which are most prevalent. To speed up policy evaluation, this research uses two unique approaches: policy normalization and canonical policy representation. The idea of policy normalization is to convert a policy with a complex logical structure to an equivalent policy with a simple logical structure. The idea of canonical policy representation is to convert a given policy to its canonical form. The techniques developed in this effort will be useful beyond this project. The results will greatly benefit society by increasing the adoption of policy-based computing. To promote education and learning, this effort actively involves high school, undergraduate, graduate students, especially students from under-represented minorities.

该奖项是根据2009年的《美国回收与再投资法》（公法111-5）进行的。基于政策的计算是许多大规模可配置系统的关键组成部分，因为它可以通过更改策略配置而无需重新编程系统来实现系统行为的动态适应性。政策评估，检查请求是否满足政策的过程通常是基于策略的系统的性能瓶颈。大多数先前的研究都集中在政策的正确性（即规范，设计和分析）上。尽管正确性是一个重要的问题，但如果未能有效实施并因此效果较差，则基于策略的计算的采用可能会受到限制。该项目的高级研究目标是通过设计高性能政策评估算法和引擎来提高基于政策的计算的有效性和采用，这些算法和引擎可以适应支持各种政策语言。该项目的主要重点是对安全策略的快速评估，这是最普遍的。为了加快政策评估，本研究使用了两种独特的方法：政策归一化和规范的政策表示。政策归一化的想法是将具有复杂逻辑结构的策略转换为具有简单逻辑结构的等效策略。规范政策表示的想法是将给定的政策转换为其规范形式。在这项工作中开发的技术将在此项目之外有用。结果将通过增加基于政策的计算的采用来极大地使社会受益。为了促进教育和学习，这项努力积极涉及高中，本科生，研究生，尤其是来自代表性不足的少数群体的学生。