Applying Event Stream Processing to Network Online Failure Prediction

Dueñas, Juan C.; Navarro, José Manuel; Parada, Adrián González; Andión, Javier; Cuadrado, Félix

doi:10.1109/mcom.2018.1601135

Cited by 16 publications

(10 citation statements)

References 14 publications

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“…The approach applies knowledge discovery methodology and a pre-defined dictionary of faults and solutions for classifying new faults. The authors of the study [6] have presented an online failure prediction system for predicting failures on networks and systems. Several technical remarks of this system are: (1) using Random Forest technique to train prediction models;…”

Section: Related Workmentioning

confidence: 99%

An Analysis of Software Bug Reports Using Machine Learning Techniques

Tran

Nguyen

et al. 2019

SN COMPUT. SCI.

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Bug tracking systems manage bug reports for assuring the quality of software products. A bug report (alsoreferred as trouble, problem, ticket or defect) contains several features for problem management and resolution purposes. Severity and priority are two essential features of a bug report that define the effect level and fixing order of the bug. Determining these features is challenging and depends heavily on human being, e.g., software developers or system operators, especially for assessing a large number of error and warning events occurring on software products or network services. This study first proposes a comparison of machine learning techniques for assessing severity and priority for software bug reports and then chooses an approach of using optimal decision trees, or random forest, for further investigation. This approach aims at constructing multiple decision trees based on the subsets of the existing bug dataset and features, and then selecting the best decision trees to assess the severity and priority of new bugs. The approach can be applied for detecting and forecasting faults in large, complex communication networks and distributed systems today. We have presented the applicability of random forest for bug report analysis and performed several experiments on software bug datasets obtained from open source bug tracking systems. Random forest yields an average accuracy score of 0.75 that can be sufficient for assisting system operators in determining these features. We have provided some analysis of the experimental results.

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Section: Related Workmentioning

confidence: 99%

An Analysis of Software Bug Reports Using Machine Learning Techniques

Tran

Nguyen

et al. 2019

SN COMPUT. SCI.

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“…The authors used existing data in the database methodology as well as a predefined list of failures, based on their previous experience. Navarro [56] described an online failure prediction system built on Apache Spark, which took a repository of network management events and formed a random forest model. This approach uses this model to predict the occurrence of future events in real time.…”

Section: Related Workmentioning

confidence: 99%

Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision

Chakor

Azmani

2022

Applied Computer Systems

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Intelligent monitoring of a computer network provides a clear understanding of its behaviour at various times and in various situations. It also provides relief to support teams that spend most of their time troubleshooting problems caused by hardware or software failures. This type of monitoring ensures the accuracy and efficiency of the network to meet the expectations of its users. However, to ensure intelligent monitoring, it is necessary to start by automating this process, which often leads to long and costly interventions. The success of such automation implies the establishment of predictive maintenance as a prerequisite for good preventive maintenance governance. However, even when it is practiced effectively, preventive maintenance requires a great deal of time and the mobilization of several full-time resources, especially for large IT structures. This paper gives an overview of the monitoring of a computer network and explains its process and the problems encountered. It also proposes a method based on machine learning to allow for prediction and support decision making to proactively anticipate interventions.

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“…The fault detection and diagnosis are solved as a pattern classification problem. In [30], the authors described an online failure prediction system built over Apache Spark that takes a repository of network management events, trains a random forest model and uses this model to predict the appearance of future events in near real time. However, for some failures, e.g., silent failures, no event will happen in the network, making it hard for the system to detect the failure.…”

Section: B Machine Learning For Network Fault Managementmentioning

confidence: 99%

Machine Learning-Based Link Fault Identification and Localization in Complex Networks

Srinivasan

Truong-Huu

Gurusamy

2019

IEEE Internet Things J.

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With the proliferation of network devices and rapid development in information technology, networks such as Internet of Things are increasing in size and becoming more complex with heterogeneous wired and wireless links. In such networks, link faults may result in a link disconnection without immediate replacement or a link reconnection, e.g., a wireless node changes its access point. Identifying whether a link disconnection or a link reconnection has occurred and localizing the failed link become a challenging problem. An active probing approach requires a long time to probe the network by sending signaling messages on different paths, thus incurring significant communication delay and overhead. In this paper, we adopt a passive approach and develop a three-stage machine learning-based technique, namely ML-LFIL that identifies and localizes link faults by analyzing the measurements captured from the normal traffic flows, including aggregate flow rate, end-to-end delay and packet loss. ML-LFIL learns the traffic behavior in normal working conditions and different link fault scenarios. We train the learning model using support vector machine, multi-layer perceptron and random forest. We implement ML-LFIL and carry out extensive experiments using Mininet platform. Performance studies show that ML-LFIL achieves high accuracy while requiring much lower fault localization time compared to the active probing approach.

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Applying Event Stream Processing to Network Online Failure Prediction

Cited by 16 publications

References 14 publications

An Analysis of Software Bug Reports Using Machine Learning Techniques

An Analysis of Software Bug Reports Using Machine Learning Techniques

Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision

Machine Learning-Based Link Fault Identification and Localization in Complex Networks

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