Understanding and dealing with hard faults in persistent memory systems
Understanding and dealing with hard faults in persistent memory systems
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DOI：
10.1145/3447786.3456252
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Brian Choi;R. Burns;Peng Huang
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文献类型：
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作者：
Brian Choi;R. Burns;Peng Huang
持久存储器（PM）设备的出现使得系统能够以低成本主动地持久存储信息，包括传统上在易失性存储器中的程序状态。然而，这种趋势带来了可靠性的挑战，其中，在传统系统中，在重启后消失的多个类别的软故障变成PM系统中的硬（复发）故障。在本文中，我们首先描述了这个新兴的问题与实证研究的28个现实世界的错误。我们分析了它们如何导致PM系统中的硬故障。然后，我们提出了阿尔萨斯，一个工具，有效地恢复PM系统的硬故障。阿尔萨斯通过细粒度版本控制检查PM状态，并使用错误指令的程序切片将有问题的PM状态恢复为良好版本。我们评估阿尔萨斯12个现实世界的硬故障，从五个大型PM系统。与最先进的检查点回滚解决方案相比，Arthas成功地恢复了所有情况下的系统，同时平均丢弃的数据减少了10倍。
The advent of Persistent Memory (PM) devices enables systems to actively persist information at low costs, including program state traditionally in volatile memory. However, this trend poses a reliability challenge in which multiple classes of soft faults that go away after restart in traditional systems turn into hard (recurring) faults in PM systems. In this paper, we first characterize this rising problem with an empirical study of 28 real-world bugs. We analyze how they cause hard faults in PM systems. We then propose Arthas, a tool to effectively recover PM systems from hard faults. Arthas checkpoints PM states via fine-grained versioning and uses program slicing of fault instructions to revert problematic PM states to good versions. We evaluate Arthas on 12 real-world hard faults from five large PM systems. Arthas successfully recovers the systems for all cases while discarding 10× less data on average compared to state-of-the-art checkpoint-rollback solutions.