SmartInjector: Exploiting intelligent fault injection for SDC rate analysis
SmartInjector: Exploiting intelligent fault injection for SDC rate analysis
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SmartInjector：利用智能故障注入进行 SDC 速率分析
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
QingPing Tan
中科院分区：
文献类型：
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作者：
Jianli Li;QingPing Tan
最近，研究人员表明，利用基于症状的解决方案提供了一种实现低成本容错的有前途的方法。然而，由于缺乏处理静默数据损坏（SDC）的能力，这些解决方案无法提供足够的可靠性。为了有效地解决 SDC，有效地识别它们非常重要。本文提出了一种智能故障注入框架 SmartInjector，它可以通过负担得起的故障模拟工作来准确分析应用程序中指令的 SDC 速率。 SmartInjector基于程序分析，首先剪枝那些无需故障模拟就可以知道结果的故障或被认为与其他故障等效的故障。 SmartInjector还采用故障结果预测技术来减少单次故障模拟的时间。计算资源需求的减少使得 SmartInjector 能够执行详细的故障注入实验，以分析应用程序中每条指令的 SDC 速率。验证结果表明，SmartInjector所需的故障模拟时间仅为模拟所有可能故障所需时间的0.15%左右，而SDC率的分析精度平均达到94%。与现有的故障注入框架不同，SmartInjector 是第一个利用故障结果预测来减少单次模拟时间的框架。
Recently, researchers have shown that exploiting symptom-based solutions provides a promising way to achieve low-cost fault tolerance. However, these solutions cannot provide enough reliability due to lack of ability to handle silent data corruptions (SDCs). To address SDCs efficiently, it is important to identify them effectively. This paper presents an intelligent fault injection framework, SmartInjector, which can analyze the SDC rates of the instructions in an application accurately with affordable fault simulation efforts. Based on program analysis, SmartInjector firstly prunes the faults whose outcome can be known without fault simulation or the faults which are considered to be equivalent to other faults. SmartInjector also employs the fault outcome prediction technique to reduce the time for a single fault simulation. The decreased requirement of computational resources allows SmartInjector to perform detailed fault injection experiments to analyze SDC rate for each instruction in an application. Validation results show that the fault simulation time needed by SmartInjector is only about 0.15% of the time required for simulating all possible faults, while the analysis accuracy of SDC rate reaches to 94% on average. Unlike existing fault injection frameworks, SmartInjector is the first to exploit fault outcome prediction to reduce the single simulation time.