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Abstract
Software aging is a phenomenon referring to the performance degradation of a long-running software system. This phenomenon is an accumulative process during execution, which will gradually lead the system from a normal state to a failure-prone state. It is a crucial challenge for system reliability to predict the Aging-Related Failures (ARFs) accurately. In this paper, permutation entropy (PE) is modified to Multidimensional Multi-scale Permutation Entropy (MMPE) as a novel aging indicator to detect performance anomalies, since MMPE is sensitive to dynamic state changes. An experiment is set on the distributed database system Voldemort, and MMPE is calculated based on the collected performance metrics during execution. Finally, based on MMPE, a failure prediction model using the machine learning method to reveal the anomalies is presented, which can predict failures with high accuracy.
Highlights
In modern society, the complexity of software is continuously increasing, which brings convenience and increases challenges to maintain software reliability
The prediction results obtained by the Aging-Related Failures (ARFs) prediction approach based on Multidimensional Multi-scale Permutation Entropy (MMPE) and original performance time series are compared to verify that MMSE can effectively improve the accuracy of predicting failure
An approach for predicting ARFs is proposed based on the dynamical anomaly detection method
Summary
The complexity of software is continuously increasing, which brings convenience and increases challenges to maintain software reliability. Developing cloud software based on distributed systems is a typical example and has attracted a lot of attention. It has two characteristics of high complexity and being long-running. Due to the accumulation of errors and garbage, it usually suffers from performance degradation or an increase in failure rate. This phenomenon is called software aging [1]. Software aging has caused tremendous damage to many complex long-running systems such as web servers [2], operating systems [3], and even safety-critical software [4]. Memory-related types of aging are the most concerned in the existing research [6]
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