Run-time monitoring with adjustable overhead using dataflow-guided filtering
Run-time monitoring with adjustable overhead using dataflow-guided filtering
复制标题
使用数据流引导过滤进行可调节开销的运行时监控
DOI：
10.1109/hpca.2015.7056071
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
G. Edward Suh
中科院分区：
文献类型：
--
作者：
Daniel Lo;Tao Chen;Mohamed Ismail;G. Edward Suh
最近的研究提出了各种并行运行时监控技术，以提高计算机系统的可靠性、安全性和调试能力。然而，这些运行时监视器可能带来巨大的性能和能源开销，特别是对于支持一系列监视器的灵活系统。在本文中，我们介绍了一个硬件数据流跟踪引擎，它可以通过部分监控来调节开销。这允许在监视覆盖率和开销之间进行权衡。还可以扩展此数据流引擎，过滤掉与空元数据相关的监视操作，以减少开销。在此架构下，我们研究了应该如何为部分监视做出放弃决策，并展示了根据部分监视的目标应用程序存在有趣的策略决策。我们的实验结果表明，通过权衡覆盖率可以显著降低开销。例如，对于平均开销为2-6倍的监控技术，所建议的体系结构能够将开销减少到1.5倍，同时仍然达到14-85%的平均覆盖率。
Recent studies have proposed various parallel runtime monitoring techniques to improve the reliability, security, and debugging capabilities of computer systems. However, these run-time monitors can introduce large performance and energy overheads, especially for flexible systems that support a range of monitors. In this paper, we introduce a hardware dataflow tracking engine that enables adjustable overhead through partial monitoring. This allows a trade-off to be made between monitoring coverage and overhead. This dataflow engine can also be extended to filter out monitoring operations associated with null metadata in order to reduce overhead. Given this architecture, we investigate how the dropping decisions should be made for partial monitoring and show that there exist interesting policy decisions depending on the target application of partial monitoring. Our experimental results show that overhead can be reduced significantly by trading off coverage. For example, for monitoring techniques with average overheads of 2-6x, the proposed architecture is able to reduce overhead to 1.5x while still achieving 14-85% average coverage.