CSR: Small: Statistical Memory Monitoring in Hardware for Security and Performance

CSR：小型：硬件中的统计内存监控以确保安全性和性能

• 批准号: 1117726
• 负责人: Vijay Pai
• 金额: $ 42万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-15 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117726&HistoricalAwards=false
• 关键词: CSR; Small; Statistical; Memory; Monitoring

This project applies statistical sampling techniques to detect deviation from normal or desired behavior for a variety of applications, ranging from security problems such as phase-change memory (PCM) wear leveling and intrusion detection to performance problems such as multicore communication, data migration, and locality optimizations. Existing performance monitoring and measurement techniques are insufficient for our applications for a variety of reasons. Many depend upon the OS which (1) may be compromised and hence be unsuitable for security-related monitoring, and (2) tracks data at page granularity whereas memory hierarchy performance often needs monitoring at the block granularity. To avoid these limitations, this project designs a monitoring architecture for statistically sampling memory access patterns. Brute-force monitoring would require large, frequently-searched hardware structures that increase complexity and power, whereas sampling enables much smaller structures that are searched at low rates, incurring far less overhead. In general, sampling loses accuracy or requires a large number of samples (and large hardware structures) if the monitored behavior exhibits high standard deviation. Our key insight is that we can bound the standard deviation of the behavior within the region of interest to the application, thereby allowing accurate and low-overhead sampling. Our key intellectual merit is to show that statistical sampling and performance monitoring can unify the aims of disparate applications and enable monitoring with high accuracy and low overhead. The broader impacts include paving the way for robust statistics-based performance monitoring in future computer systems and for research and education efforts that combine statistics and computer architecture.

该项目应用统计采样技术来检测各种应用程序的正常或期望行为的偏差，范围从相变存储器（PCM）磨损均衡和入侵检测等安全问题到多核通信，数据迁移和局部优化等性能问题。现有的性能监控和测量技术是不足以为我们的应用程序的各种原因。许多依赖于操作系统，操作系统（1）可能会受到损害，因此不适合安全相关的监控，以及（2）以页面粒度跟踪数据，而存储器层次结构性能通常需要以块粒度进行监控。 为了避免这些限制，这个项目设计了一个监控架构，用于对内存访问模式进行统计采样。暴力监控需要大型的、频繁搜索的硬件结构，这会增加复杂性和功耗，而采样可以实现以低速率搜索的小得多的结构，从而产生少得多的开销。一般来说，如果被监控的行为表现出高标准偏差，则采样会失去准确性或需要大量样本（以及大型硬件结构）。我们的关键见解是，我们可以将感兴趣区域内的行为的标准差绑定到应用程序，从而实现准确和低开销的采样。我们的主要智力价值是表明，统计采样和性能监控可以统一不同的应用程序的目标，并实现高精度和低开销的监控。更广泛的影响包括为未来计算机系统中基于统计的强大性能监控以及将联合收割机统计数据和计算机体系结构结合起来的研究和教育工作铺平道路。