Reliable and Accurate Fault Detection with GPGPUs and LLVM
Yuichi Ozaki, Sousuke Kanamoto, Hiroaki Yamamoto, Kenichi Kourai · 2023
As the scale and complexity of cloud systems are increasing, system faults are becoming unavoidable. Therefore, they should be detected as reliably and accurately as possible. Black-box monitoring can reliably monitor a target system from a remote host, but it is often coarse-grained and cannot identify the root causes of system faults. In contrast, white-box monitoring can accurately obtain fault information inside a target system, but it is largely affected by system faults. This paper proposes GPUSentinel for more reliable white-box monitoring using general-purpose GPUs. GPUSentinel runs fault detectors in an isolated GPU, which is not easily affected by faults of a target system. For accurate detection, fault detectors in a GPU analyze main memory and directly monitor the state of the operating system. To easily develop such fault detectors, GPUSentinel provides a development environment with LLVM. We have implemented GPUSentinel and seven fault detectors and then confirmed that GPUSentinel could detect various system faults and identify the root causes.