Quantitative evaluation of fault propagation in a commercial cloud system
As semiconductor technology scales into the nano regime, hardware faults have been threats against computational devices. Cloud systems are incorporating more and more computing density and energy into themselves; thus, fundamental research on topics such as dependability validation is needed, in or...
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| Format: | Article |
| Language: | English |
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Wiley
2020-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720903613 |
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| _version_ | 1832547299320922112 |
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| author | Chao Wang Zhongchuan Fu |
| author_facet | Chao Wang Zhongchuan Fu |
| author_sort | Chao Wang |
| collection | DOAJ |
| description | As semiconductor technology scales into the nano regime, hardware faults have been threats against computational devices. Cloud systems are incorporating more and more computing density and energy into themselves; thus, fundamental research on topics such as dependability validation is needed, in order to verify the robustness of clouds for sensor networks. However, dependability evaluation studies have often been carried out beyond isolated physical systems, such as processors, sensors, and single boards with or without operating system hosts. These studies have been performed using inaccurate simulations instead of validating complete cloud software stacks (firmware, hypervisor, operating system hosts and workloads) as a whole. In this article, we describe the implementation of a fault injection tool, which validates the dependability of a commercial cloud software stack. Hardware faults induced by high energy density environments can be injected; the fault propagation through the cloud software stack is traced, and quantitatively evaluated. Experimental results show that the integrated fault detection mechanism of the cloud system, such as fatal trap detectors, has left a detection margin of 20% silent data corruption to narrow down. We additionally propose two detection mechanisms, which proved good performance in fault detection of cloud systems. |
| format | Article |
| id | doaj-art-8be2d0b4de9045f9a4d8b6392f5b9dea |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-8be2d0b4de9045f9a4d8b6392f5b9dea2025-02-03T06:45:23ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-03-011610.1177/1550147720903613Quantitative evaluation of fault propagation in a commercial cloud systemChao Wang0Zhongchuan Fu1Computer School, Beijing Information Science and Technology University, Beijing, ChinaComputer Department, Harbin Institute of Technology, Harbin, ChinaAs semiconductor technology scales into the nano regime, hardware faults have been threats against computational devices. Cloud systems are incorporating more and more computing density and energy into themselves; thus, fundamental research on topics such as dependability validation is needed, in order to verify the robustness of clouds for sensor networks. However, dependability evaluation studies have often been carried out beyond isolated physical systems, such as processors, sensors, and single boards with or without operating system hosts. These studies have been performed using inaccurate simulations instead of validating complete cloud software stacks (firmware, hypervisor, operating system hosts and workloads) as a whole. In this article, we describe the implementation of a fault injection tool, which validates the dependability of a commercial cloud software stack. Hardware faults induced by high energy density environments can be injected; the fault propagation through the cloud software stack is traced, and quantitatively evaluated. Experimental results show that the integrated fault detection mechanism of the cloud system, such as fatal trap detectors, has left a detection margin of 20% silent data corruption to narrow down. We additionally propose two detection mechanisms, which proved good performance in fault detection of cloud systems.https://doi.org/10.1177/1550147720903613 |
| spellingShingle | Chao Wang Zhongchuan Fu Quantitative evaluation of fault propagation in a commercial cloud system International Journal of Distributed Sensor Networks |
| title | Quantitative evaluation of fault propagation in a commercial cloud system |
| title_full | Quantitative evaluation of fault propagation in a commercial cloud system |
| title_fullStr | Quantitative evaluation of fault propagation in a commercial cloud system |
| title_full_unstemmed | Quantitative evaluation of fault propagation in a commercial cloud system |
| title_short | Quantitative evaluation of fault propagation in a commercial cloud system |
| title_sort | quantitative evaluation of fault propagation in a commercial cloud system |
| url | https://doi.org/10.1177/1550147720903613 |
| work_keys_str_mv | AT chaowang quantitativeevaluationoffaultpropagationinacommercialcloudsystem AT zhongchuanfu quantitativeevaluationoffaultpropagationinacommercialcloudsystem |