AIOPS Prediction for Hard Drive Failures Based on Stacking Ensemble Model

Wang, Haifeng; Zhang, Haili

doi:10.1109/ccwc47524.2020.9031232

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…is a monitoring system that collects indicator performance that can be used to infer the actual condition of a hard disk. S.M.A.R.T.-based active fault tolerance uses a threshold approach [15], but traditional S.M.A.R.T.-based fault detection has problems in terms of accuracy [16]. It is no longer sufficient to complete the analysis using S.M.A.R.T.…”

Section: Anomaly Detection Of Mechanical Hardmentioning

confidence: 99%

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

Shi

Ren

et al. 2022

Journal of Sensors

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The traditional Infrastructure as a Service (IaaS) cloud platform tends to realize high data availability by introducing dedicated storage devices. However, this heterogeneous architecture has high maintenance cost and might reduce the performance of virtual machines. In homogeneous IaaS cloud platform, servers in the platform would uniformly provide computing resources and storage resources, which effectively solve the above problems, although corresponding mechanisms need to be introduced to improve data availability. Efficient storage resource availability management is one of the key methods to improve data availability. As mechanical hard disk is the main way to realize data storage in IaaS cloud platform at present, timely and accurate prediction of mechanical hard disk failure and active data backup and migration before mechanical hard disk failure would effectively improve the data availability of IaaS cloud platform. In this paper, we propose an improved algorithm for early warning of mechanical hard disk failures. We first use the Relief feature selection algorithm to perform parameter selection. Then, we use the zero-sum game idea of Generative Adversarial Networks (GAN) to generate fewer category samples to achieve a balance of sample data. Finally, an improved Long Short-Term Memory (LSTM) model called Convolution-LSTM (C-LSTM) is used to complete accurate detection of hard disk failures and achieve fault warning. We evaluate several models using precision, recall, and Area Under Curve (AUC) value, and extensive experiments show that our proposed algorithm outperforms other algorithms for mechanical hard disk warning.

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Section: Anomaly Detection Of Mechanical Hardmentioning

confidence: 99%

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

Shi

Ren

et al. 2022

Journal of Sensors

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“…Finally, the performance of anomaly detection is improved by adapting to different periodic distributions. Since the performance of a single prediction model on unbalanced data sets is not adequate, Wang et al [13] uses the prediction results calculated by three algorithms (XGBoost classification, LSTM classification, and XGBoost regression) as the feature input of stacked integrated-learning models that can generate more results to obtain robust prediction results. Experimental results show that the proposed stack ensemble-learning model can accurately predict disk failures 14 to 42 days in advance.…”

Section: Methods Based On Machine Learningmentioning

confidence: 99%

A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU

et al. 2022

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Fault early warning is a challenge in the field of operation and maintenance. Considering the improvement of accuracy and real-time standards, as well as the explosive growth of operation and maintenance data, traditional manual experience and static threshold can no longer meet the production requirements. This research fully digs into the difficulties in fault early warning and provides targeted solutions in several aspects, such as difficulty in feature extraction, insufficient prediction accuracy, and difficulty in determining alarm threshold. The TCAG model proposed in this paper creatively combines the spatiotemporal characteristics and topological characteristics of specific time series data to apply to time series prediction and gives the recommended dynamic threshold interval for fault early warning according to the prediction value. A data comparison experiment of a core router of Ningxia Electric Power Co., Ltd. shows that the combination of topological data analysis (TDA) and convolutional neural network (CNN) enables the TCAG model to obtain superior feature extraction capability, and the support of the attention mechanism improves the prediction accuracy of the TCAG model compared to the benchmark models.

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“…Moreover, the tsfresh implements standard application programming interfaces of time series and machine learning libraries and is designed for both exploratory analyses as well as straightforward integration into operational data science applications [31]. In recent years, the tsfresh has been widely applied in many fields, such as predicting the smart grid stability [32], artificial intelligence for cloud storage array operations [33], and the pressure transients of pipe networks Computational Intelligence and Neuroscience in industries and water distribution systems [34]. However, the tsfresh has not been applied to the PDP-SAD of CLS.…”

Section: Deep Learning Kernel Of Liner Berthing Time Predictionmentioning

confidence: 99%

Computational Logistics for Container Terminal Handling Systems with Deep Learning

2021

Computational Intelligence and Neuroscience

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Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM-CCA) for LBT prediction is presented to practice CTO-NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO-NPFC preliminarily.

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AIOPS Prediction for Hard Drive Failures Based on Stacking Ensemble Model

Cited by 11 publications

References 8 publications

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU

Computational Logistics for Container Terminal Handling Systems with Deep Learning

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