[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124047-en":3,"doc-seo-124047-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124047,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhancing Infrastructure Observability - Machine Learning for Proactive Monitoring and Anomaly Detection","This study addresses proactive anomaly detection and efficient resource management in infrastructure observability by integrating machine learning models into observability platforms for more precise real-time monitoring. Using a microservices architecture, the proposed system enables fast and anticipatory detection of anomalies, overcoming limitations of traditional monitoring that often reacts only after issues escalate. Predictive models based on Random Forest, Gradient Boosting, and Support Vector Machine forecast key metrics such as CPU usage and memory allocation. Experiments show strong performance, with GradientBoostingRegressor reaching R²=0.86 for request-rate prediction and RandomForestRegressor reducing mean squared error by 2.06% for memory predictions versus traditional baselines.","Journal of Internet Services and Applications, 2024, 15:1, doi: 10.5753/jisa.2024.4509  \n􀈸 This work is licensed under a Creative Commons Attribution 4 .0 International License.  \nEnhancing Infrastructure Observability: Machine Learning for Proactive Monitoring and Anomaly Detection  \nDarlan Noetzold 􀁂 􀀎 [ Instituto Federal de Educação, Ciência e Tecnologia Sul-Rio-Grandense | dar[lan.noetzold@gmail.com](lan.noetzold@gmail.com) ]  \nAnubis G. D. M. Rossetto 􀁂 [ Instituto Federal de Educação, Ciência e Tecnologia Sul-Rio[Grandense](Grandense | anubisrossetto@ifsul.edu.br)[ |](Grandense | anubisrossetto@ifsul.edu.br)[ anubisrossetto@ifsul.edu.br](Grandense | anubisrossetto@ifsul.edu.br) ]  \nValderi R. Q. Leithardt 􀁂 􀀎 [ Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisboa, Portugal) | [valderi.leithardt@iscte-iul.pt](valderi.leithardt@iscte-iul.pt) ]  \nHumberto J. de M. Costa 􀁂 [ Instituto Federal de Educação, Ciência e Tecnologia Sul-Rio[Grandense](Grandense | humberto.costa@osorio.ifrs.edu.br)[ |](Grandense | humberto.costa@osorio.ifrs.edu.br)[ humberto.costa@osorio.ifrs.edu.br](Grandense | humberto.costa@osorio.ifrs.edu.br) ]  \n􀀎 Federal Institute of Education, Science and Technology Sul-rio-grandense, Passo Fundo, Rio Grande do Sul, RS, 99064-440, Brazil.  \nInstituto Universitário de Lisboa (ISCTE-IUL), Av. das Forças Armadas, 1649-026, Lisboa, Portugal.  \nReceived: 23 May 2024 • Accepted: 21 August 2024 • Published: 28 October 2024  \nAbstract This study addresses the critical challenge of proactive anomaly detection and efficient resource management in infrastructure observability. Introducing an innovative approach to infrastructure monitoring, this work integrates machine learning models into observability platforms to enhance real-time monitoring precision. Employing a microservices architecture, the proposed system facilitates swift and proactive anomaly detection, addressing the limitations of traditional monitoring methods that often fail to predict potential issues before they escalate. The core of this system lies in its predictive models that utilize Random Forest, Gradient Boosting, and Support Vector Machine algorithms to forecast crucial metric behaviors, such as CPU usage and memory allocation. The empirical results underscore the system’s efficacy, with the GradientBoostingRegressor model achieving an R² score of 0.86 for predicting request rates, and the RandomForestRegressor model significantly reducing the Mean Squared Error by 2.06% for memory usage predictions compared to traditional monitoring methods. These findings not only demonstrate the potential of machine learning in enhancing observability but also pave the way for more resilient and adaptive infrastructure management.  \nKeywords: Machine Learning, Infrastructure Monitoring, Anomaly Detection, Proactive Maintenance  \n1 Introduction  \nIn the era of digital transformation, software systems have become fundamentally complex [dos Santos et al., 2021], driven by distributed architectures such as microservices, which bring with them significant challenges for monitoring and ensuring quality of service [Borré et al., 2023] . Observability, unlike traditional monitoring, is conceptualized as the ability to infer the internal state of a system from its external data, such as logs, metrics, and traces, making it easier to diagnose problems and understand the behavior of the system in production.  \nGiven the complexity of modern systems and the vast amount of data generated [Surek et al., 2023], it is crucial not only to collect but also to analyze and interpret this data efficiently. In this context, predictive approaches, supported by machine learning techniques [Yamasaki et al., 2024], present as a natural evolution of observability practices, enabling the proactive identification of potential failures and performance problems before they negatively impact end users or business operations [Corso et al., 2023] .  \nThe main contributions of this pap","cbCaijof3brkUeGm","https://ap.wps.com/l/cbCaijof3brkUeGm","pdf",829692,1,15,"English","en",105,"# Introduction\n## Observability and monitoring in complex systems\n## Predictive approaches for proactive anomaly detection\n# Contributions\n# Proposed approach and methodology\n# Evaluation metrics and results\n## Comparison with traditional monitoring","[{\"question\":\"What problem does the study target in infrastructure observability?\",\"answer\":\"It targets proactive anomaly detection and efficient resource management, aiming to predict issues before they negatively affect users or business operations.\"},{\"question\":\"How is machine learning integrated into the observability approach?\",\"answer\":\"The approach embeds predictive models into observability platforms within a microservices architecture, forecasting metric behaviors to support early anomaly detection.\"},{\"question\":\"Which machine learning algorithms and metrics are used for prediction?\",\"answer\":\"It uses Random Forest, Gradient Boosting, and Support Vector Machine models to predict crucial metrics such as CPU usage, memory allocation, and request rates.\"}]","Enhancing Infrastructure Observability - 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