Lee Yen-Lin, Arizky Shinta Nuraisya, Chen Yu-Ren, Liang Deron, Wang Wei-Jen
Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan.
Institute for Information Industry, Taipei 106, Taiwan.
Sensors (Basel). 2021 Jan 13;21(2):542. doi: 10.3390/s21020542.
Modern computing platforms usually use multiple sensors to report system information. In order to achieve high availability (HA) for the platform, the sensors can be used to efficiently detect system faults that make a cloud service not live. However, a sensor may fail and disable HA protection. In this case, human intervention is needed, either to change the original fault model or to fix the sensor fault. Therefore, this study proposes an HA mechanism that can continuously provide HA to a cloud system based on dynamic fault model reconstruction. We have implemented the proposed HA mechanism on a four-layer OpenStack cloud system and tested the performance of the proposed mechanism for all possible sets of sensor faults. For each fault model, we inject possible system faults and measure the average fault detection time. The experimental result shows that the proposed mechanism can accurately detect and recover an injected system fault with disabled sensors. In addition, the system fault detection time increases as the number of sensor faults increases, until the HA mechanism is degraded to a one-system-fault model, which is the worst case as the system layer heartbeating.
现代计算平台通常使用多个传感器来报告系统信息。为了实现平台的高可用性（HA），传感器可用于有效检测导致云服务无法运行的系统故障。然而，传感器可能会发生故障并使HA保护失效。在这种情况下，需要人工干预，要么更改原始故障模型，要么修复传感器故障。因此，本研究提出了一种HA机制，该机制可以基于动态故障模型重建为云系统持续提供HA。我们已在四层OpenStack云系统上实现了所提出的HA机制，并针对所有可能的传感器故障集测试了该机制的性能。对于每个故障模型，我们注入可能的系统故障并测量平均故障检测时间。实验结果表明，所提出的机制能够准确检测并恢复因传感器故障而注入的系统故障。此外，随着传感器故障数量的增加，系统故障检测时间也会增加，直到HA机制退化为单系统故障模型，这是系统层心跳的最坏情况。
