论文标题
部分可观测时空混沌系统的无模型预测
Finding and Analyzing Crash-Consistency Bugs in Persistent-Memory File Systems
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
We present a study of crash-consistency bugs in persistent-memory (PM) file systems and analyze their implications for file-system design and testing crash consistency. We develop FlyTrap, a framework to test PM file systems for crash-consistency bugs. FlyTrap discovered 18 new bugs across four PM file systems; the bugs have been confirmed by developers and many have been already fixed. The discovered bugs have serious consequences such as breaking the atomicity of rename or making the file system unmountable. We present a detailed study of the bugs we found and discuss some important lessons from these observations. For instance, one of our findings is that many of the bugs are due to logic errors, rather than errors in using flushes or fences; this has important applications for future work on testing PM file systems. Another key finding is that many bugs arise from attempts to improve efficiency by performing metadata updates in-place and that recovery code that deals with rebuilding in-DRAM state is a significant source of bugs. These observations have important implications for designing and testing PM file systems. Our code is available at https://github.com/utsaslab/flytrap .
