Hora: Architecture-aware online failure prediction
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TLDR
This paper proposes a hierarchical online failure prediction approach, called Hora, which combines component failure predictors with architectural knowledge and the failure propagation is modeled using Bayesian networks which incorporate both prediction results and component dependencies extracted from the architectural models.About:
This article is published in Journal of Systems and Software.The article was published on 2017-03-02 and is currently open access. It has received 55 citations till now. The article focuses on the topics: Software system & Component (UML).read more
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Proceedings ArticleDOI
Latent error prediction and fault localization for microservice applications by learning from system trace logs
TL;DR: The results indicate that MEPFL can achieve high accuracy in intraapplication prediction of latent errors, faulty microservices, and fault types, and outperforms a state-of-the-art approach of failure diagnosis for distributed systems.
Proceedings ArticleDOI
Task Failure Prediction in Cloud Data Centers Using Deep Learning
TL;DR: A failure prediction algorithm based on multi-layer Bidirectional Long Short Term Memory (Bi-LSTM) to identify task and job failures in the cloud and shows that the algorithm outperforms other state-of-art prediction methods with 93% accuracy and 87% for task failure and job failure respectively.
Architecture-Based Reliability Prediction with the Palladio Component Model.
TL;DR: This paper introduces a reliability modeling and prediction technique that considers the relevant architectural factors of software systems by explicitly modeling the system usage profile and execution environment and automatically deriving component usage profiles and offers a UML-like modeling notation whose models are automatically transformed into a formal analytical model.
Proceedings ArticleDOI
Desh: deep learning for system health prediction of lead times to failure in HPC
TL;DR: This work aims to predict node failures that occur in supercomputing systems via long short-term memory (LSTM) networks that exploit recurrent neural networks (RNNs), and identifies failure indicators with enhanced training and classification for generic applicability to logs from operating systems and software components without the need to modify them.
References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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An introduction to ROC analysis
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
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Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
TL;DR: A nonparametric approach to the analysis of areas under correlated ROC curves is presented, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Xavier Robin,Natacha Turck,Alexandre Hainard,Natalia Tiberti,Frédérique Lisacek,Jean-Charles Sanchez,Markus Müller +6 more
TL;DR: pROC as mentioned in this paper is a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.