ARF-Predictor: Effective Prediction of Aging-Related Failure Using Entropy
ARF-Predictor: Effective Prediction of Aging-Related Failure Using Entropy
复制标题
ARF-Predictor：利用熵有效预测老化相关故障
DOI：
10.1109/tdsc.2016.2604381
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发表时间：
2018-07-01
影响因子：
7.3
通讯作者：
Lyu, Michael Rung-Tsong
中科院分区：
文献类型：
--
作者：
Chen, Pengfei;Qi, Yong;Lyu, Michael Rung-Tsong
即使是设计良好的软件系统也会由于内部（例如，软件错误）或外部（例如，资源耗尽）损伤。这些慢性问题在造成严重影响之前通常在软件监控系统的雷达下飞行（例如，系统故障）。因此，如何及时预测由这些问题引起的故障的发生是一个具有挑战性的问题。遗憾的是，现有方法的有效性远不能令人满意，因为它们采用的老化指标不足。为了准确预测软件老化引起的失效，即老化相关失效（ARF），提出了一种新的基于熵的软件老化指标，即多维多尺度熵（MMSE），它利用运行时性能指标的复杂性来表征软件老化。据我们所知，这是第一次利用熵来预测ARF。基于MMSE，我们实现了三个故障预测方法封装在一个概念验证原型ARF预测。在视频点播（VoD）系统中的实验评估，并在现实世界的生产系统，蚁视，表明ARF-Predictor可以预测ARF具有非常高的准确性和低的提前故障时间（ATTF）。与以前的方法相比，ARF-Predictor将预测精度提高了约5倍，并将ATTF降低了3个数量级。此外，ARF-Predictor是轻量级的，足以满足实时性的要求。
Even well-designed software systems suffer from chronic performance degradation, also known as "software aging", due to internal (e.g., software bugs) or external (e.g., resource exhaustion) impairments. These chronic problems often fly under the radar of software monitoring systems before causing severe impacts (e.g., system failures). Therefore, it is a challenging issue how to timely predict the occurrence of failures caused by these problems. Unfortunately, the effectiveness of prior approaches are far from satisfactory due to the insufficiency of aging indicators adopted by them. To accurately predict failures caused by software aging which are named as Aging-Related Failure (ARFs), this paper presents a novel entropy-based aging indicator, namely Multidimensional Multi-scale Entropy (MMSE) which leverages the complexity embedded in runtime performance metrics to indicate software aging. To the best of our knowledge, this is the first time to leverage entropy to predict ARFs. Based upon MMSE, we implement three failure prediction approaches encapsulated in a proof-of-concept prototype named ARF-Predictor. The experimental evaluations in a Video on Demand (VoD) system, and in a real-world production system, AntVision, show that ARF-Predictor can predict ARFs with a very high accuracy and a low Ahead-Time-To-Failure (ATTF). Compared to previous approaches, ARF-Predictor improves the prediction accuracy by about 5 times and reduces ATTF even by 3 orders of magnitude. In addition, ARF-Predictor is light-weight enough to satisfy the real-time requirement.