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首页> 外文期刊>IEEE transactions on dependable and secure computing >Impact of Failure Prediction on Availability: Modeling and Comparative Analysis of Predictive and Reactive Methods
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Impact of Failure Prediction on Availability: Modeling and Comparative Analysis of Predictive and Reactive Methods

机译：故障预测对可用性的影响：预测和反应方法的建模与比较分析

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摘要

Predicting failures and acting proactively have a potential to improve availability as a correct prediction and a successful mitigation may bring a reward resulting in decrease of downtime and availability improvement. But, conversely, each incorrect prediction may introduce additional downtime (penalty). Therefore, depending on the quality of prediction and the system parameters, predictive fault-tolerance methods may improve or may degrade availability in comparison to the reactive ones. We first derive taxonomies of fault-tolerant techniques and policies to differentiate between reactive and proactive policies that are further classified as systematic and predictive. To evaluate whether a predictive policy improves availability or not, we derive an analytical model for availability quantification. We use Markov chains to extend steady-state availability equation to include: precision and recall, penalty and reward, mitigation success probability and potential failure rate increase due to the prediction load. We also derive an A-measure to optimize failure prediction for increasing availability. In our conclusion, precision and recall have comparable impact on availability as changing MTTF and MTTR. To validate the model we also simulate and analyze availability of a virtualized server with exponential distribution of failure and repair rates.
机译：预测失败和行动主动有可能改善可用性作为正确的预测，并且成功的缓解可能会带来奖励导致停机时间和可用性改进的减少。但是，相反，每个错误的预测可能会引入额外的停机时间（惩罚）。因此，取决于预测的质量和系统参数，预测容错方法可以改善或可能与反应性相比降低可用性。我们首先派生容错技术和政策的分类，以区分反应和主动政策，进一步归类为系统和预测性。为了评估预测政策是否提高了可用性，我们得出了可用性量化的分析模型。我们使用Markov链扩展稳态可用性方程，包括：精确和回忆，罚款和奖励，减缓成功概率和由于预测负载而增加的潜在故障率增加。我们还导出了一种测量来优化用于提高可用性的故障预测。在我们的结论中，精确和召回对可用性的可比性产生了相当的影响，改变了MTTF和MTTR。为了验证模型，我们还模拟和分析了虚拟化服务器的可用性，具有失败和维修速率的指数分布。

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