Quantitatively Modeling Application Resilience with the Data Vulnerability Factor

Recent strategies to improve the observable resilience of applications require the ability to classify vulnerabilities of individual components (e.g., Data structures, instructions) of an application, and then, selectively apply protection mechanisms to its critical components. To facilitate this vulnerability classification, it is important to have accurate, quantitative techniques that can be applied uniformly and automatically across real-world applications. Traditional methods cannot effectively quantify vulnerability, because they lack a holistic view to examine system resilience, and come with prohibitive evaluation costs. In this paper, we introduce a data-driven, practical methodology to analyze these application vulnerabilities using a novel resilience metric: the data vulnerability factor (DVF). DVF integrates knowledge from both the application and target hardware into the calculation. To calculate DVF, we extend a performance modeling language to provide a structured, fast modeling solution. We evaluate our methodology on six representative computational kernels, we demonstrate the significance of DVF by quantifying the impact of algorithm optimization on vulnerability, and by quantifying the effectiveness of specific hardware protection mechanisms.