Predicting Software Anomalies Using Machine Learning Techniques

Alonso, Javier; Belanche, Lluís; Avresky, Dimiter R.

doi:10.1109/nca.2011.29

Cited by 54 publications

(29 citation statements)

References 20 publications

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“…We investigated the capabilities of various machine learning algorithms to predict anomalous events in Smart City. These algorithms, namely Recursive Partitioning (Decision Tree), Naive Bayes (NB), Support Vector Machines Classifiers (SVM-C), k-Nearest Neighbors (kNN), Random Forest (RF) and LDA/QDA, have been succesfully applied for predicting anomalies in complex softwares (see [9]) which are comparable to our framework. These techniques have also been studied in the analyses of traffic streams using GPS traces (see [10] and [11]).…”

Section: ) Anomaly Detection For Smart City Applicationsmentioning

confidence: 99%

Framework for smart city applications based on participatory sensing

Szabó

Farkas

Ispány

et al. 2013

2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom)

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Smart cities offer services to their inhabitants which make everyday life easier beyond providing a feedback channel to the city administration. For instance, a live timetable service for public transportation or real-time traffic jam notification can increase the efficiency of travel planning substantially. Traditionally, the implementation of these smart city services require the deployment of some costly sensing and tracking infrastructure. As an alternative, the crowd of inhabitants can be involved in data collection via their mobile devices. This emerging paradigm is called mobile crowd-sensing or participatory sensing. In this paper, we present our generic framework built upon XMPP (Extensible Messaging and Presence Protocol) for mobile participatory sensing based smart city applications. After giving a short description of this framework we show three use-case smart city application scenarios, namely a live transit feed service, a soccer intelligence agency service and a smart campus application, which are currently under development on top of our framework.

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Section: ) Anomaly Detection For Smart City Applicationsmentioning

confidence: 99%

Framework for smart city applications based on participatory sensing

Szabó

Farkas

Ispány

et al. 2013

2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom)

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“…Alonso et al [62] apply and evaluate different machine learning models with each other in order to find the best suitable for their task of predicting anomalies and failures based on software aging caused by resource exhaustion. They furthermore employ Lasso regularization in order to filter features.…”

Section: Black-box and Machine Learning Modelsmentioning

confidence: 99%

A Taxonomy of Techniques for SLO Failure Prediction in Software Systems

Grohmann

Herbst

Chalbani³

et al. 2020

Computers

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Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.

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“…Our approach differs from anomaly detection solutions using ML models or employing self-adapted monitoring [61]- [63], and it is unique in the design space of distributed debugging tools. To the best of our knowledge, this is the first approach that applies distributed debugging techniques for interpreting the fault injection experiments.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Failure Propagation Analysis in Cloud Computing Systems

Cotroneo

Simone

Liguori

et al. 2019

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)

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In order to plan for failure recovery, the designers of cloud systems need to understand how their system can potentially fail. Unfortunately, analyzing the failure behavior of such systems can be very difficult and time-consuming, due to the large volume of events, non-determinism, and reuse of third-party components. To address these issues, we propose a novel approach that joins fault injection with anomaly detection to identify the symptoms of failures. We evaluated the proposed approach in the context of the OpenStack cloud computing platform. We show that our model can significantly improve the accuracy of failure analysis in terms of false positives and negatives, with a low computational cost.

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Predicting Software Anomalies Using Machine Learning Techniques

Cited by 54 publications

References 20 publications

Framework for smart city applications based on participatory sensing

Framework for smart city applications based on participatory sensing

A Taxonomy of Techniques for SLO Failure Prediction in Software Systems

Enhancing Failure Propagation Analysis in Cloud Computing Systems

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