CSSAP: Software Aging Prediction for Cloud Services Based on ARIMA-LSTM Hybrid Model

Liu, Jing; Tan, Xueyong; Wang, Yan

doi:10.1109/icws.2019.00055

Cited by 27 publications

(21 citation statements)

References 14 publications

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“…Due to their performance being close to the software aging threshold, the corresponding failure reports in the data set are regarded as software aging failures for the ARFs prediction. A similar approach is verified in the references [ 40 ].…”

Section: Experimental Verificationsupporting

confidence: 56%

A Dynamic Anomaly Detection Approach Based on Permutation Entropy for Predicting Aging-Related Failures

Wang

Kong

et al. 2020

Entropy

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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.

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Section: Experimental Verificationsupporting

confidence: 56%

A Dynamic Anomaly Detection Approach Based on Permutation Entropy for Predicting Aging-Related Failures

Wang

Kong

et al. 2020

Entropy

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show abstract

“…Adamu et al 34 rely on ML‐based prediction models to detect hardware failures in real‐time cloud environments. Liu et al 35 propose a composite model‐based approach to detect aging of cloud resources, in the context of anomalies. A regression‐based transaction model, which reflects the resource consumption model of the application has been used by Cherkasova et al, 36 which notably does not require to explicitly probe the system as we do.…”

Section: Related Workmentioning

confidence: 99%

Autonomic rejuvenation of cloud applications as a countermeasure to software anomalies

Sanzo

Avresky²,

Pellegrini

2020

Softw Pract Exp

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Failures in computer systems can be often tracked down to software anomalies of various kinds. In many scenarios, it might be difficult, unfeasible, or unprofitable to carry out extensive debugging activity to spot the cause of anomalies and remove them. In other cases, taking corrective actions may led to undesirable service downtime. In this article, we propose an alternative approach to cope with the problem of software anomalies in cloud-based applications, and we present the design of a distributed autonomic framework that implements our approach. It exploits the elastic capabilities of cloud infrastructures, and relies on machine learning models, proactive rejuvenation techniques, and a new load balancing approach. By putting together all these elements, we show that it is possible to improve both availability and performance of applications deployed to heterogeneous cloud regions and subject to frequent failures. Overall, our study demonstrates the viability of our approach, thus opening the way towards its adoption, and encouraging further studies and practical experiences to evaluate and improve it.

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“…It will be useful to predict the number of software faults over the course of development. Liu et al [7] proposed a new hybrid aging prediction model that incorporates well the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models for more appropriate fitting of the linear pattern as well as mining the nonlinear relationship in the time series of computing resource usage data for cloud services. Their model was not, however, perfect, since it assumed that the linear and nonlinear components of the sequence are merely additive.…”

Section: Literature Reviewmentioning

confidence: 99%

A fuzzy neural network for web service selection aimed at dynamic software rejuvenation

Kalantari¹,

Ebrahimnejad²,

Motameni³

2020

Turk J Elec Eng & Comp Sci

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Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the requirements of receivers and the facilities of servers. One of the challenges while assigning web services is how to select appropriate server to reduce faults. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the neural fuzzy system. While considering a threshold for the rejuvenation of each web service, we completed the training based on the features of the service providers as well as the requirements of the receivers. The results of simulations revealed that our strategy can decrease the failure rate in comparison with state-of-the-art strategies and improve system availability in web services.

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CSSAP: Software Aging Prediction for Cloud Services Based on ARIMA-LSTM Hybrid Model

Cited by 27 publications

References 14 publications

A Dynamic Anomaly Detection Approach Based on Permutation Entropy for Predicting Aging-Related Failures

A Dynamic Anomaly Detection Approach Based on Permutation Entropy for Predicting Aging-Related Failures

Autonomic rejuvenation of cloud applications as a countermeasure to software anomalies

A fuzzy neural network for web service selection aimed at dynamic software rejuvenation

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