[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121363-en":3,"doc-seo-121363-105":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},121363,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Using Machine Learning for Accelerating the Detection Time of Unreliable Failure Detectors","This study evaluates machine learning-based failure detectors for identifying faulty nodes in distributed systems, using metrics such as detection time and average mistake rate. Random Forest and Support Vector Machine failure detectors show the strongest overall performance. Despite this, machine learning approaches generate higher error rates, especially when detection time increases. To improve the trade-off, three ensemble methods are proposed, including one combining top machine learning and analytical failure detectors. A precision distribution module enhances accuracy but reduces completeness, addressed via an analytical fallback whose effectiveness depends on threshold relationships.","Using Machine Learning for Accelerating the Detection Time of Unreliable Failure Detectors  \nMaster’s thesis in Computer science and engineering  \nCHENGBO REN  \nDepartment of Computer Science and Engineering CHALMERS UNIVERSITY OF TECHNOLOGY UNIVERSITY OF GOTHENBURG  \nGothenburg, Sweden 2024  \nMaster’s thesis 2024  \nUsing Machine Learning for Accelerating the Detection Time of Unreliable Failure Detectors  \nCHENGBO REN  \nDepartment of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2024  \nUsing Machine Learning for Accelerating the Detection Time of Unreliable Failure Detectors  \nCHENGBO REN  \n© CHENGBO REN, 2024 .  \nSupervisor: Elad Michael Schiller, Department of Computer Science and Engineering  \nAdvisor: Hasan Mahmood, Nexer Group  \nExaminer: Romaric Duvignau, Department of Computer Science and Engineering  \nMaster’s Thesis 2024  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg  \nTelephone +46 31 772 1000  \nTypeset in LATEX  \nGothenburg, Sweden 2024  \nUsing Machine Learning for Accelerating the Detection Time of Unreliable Failure Detectors  \nCHENGBO REN  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg  \nAbstract  \nThis study evaluates Machine Learning-based Failure Detectors for detecting faulty nodes in distributed systems, focusing on metrics like Detection Time and Average Mistake Rate. Random Forest and Support Vector Machine Failure Detectors emerge as top performers. However, Machine Learning-based Failure Detectors exhibit higher error rates, especially with longer detection times. To address this, three ensemble methods are proposed, with one integrating top Machine Learningbased Failure Detectors and analytical Failure Detectors for better balance. The Precision Distribution module improves accuracy but struggles with completeness. One analytical Failure Detector is used as a fallback to address completeness concerns, although its effectiveness depends on the relationship between the analytical Failure Detector and Precision Distribution module thresholds. Additionally, the Li-Marin Long Short-Term Memory Failure Detector shows reduced detection time but with increased computational overhead, posing challenges in fine-tuning overestimation levels.  \nKeywords: Failure Detector, Machine Learning, Distributed Systems, Ensemble Methods.  \nAcknowledgements  \nOn finishing the last word of my thesis report, I see a heart filled with gratitude beating in my chest.  \nI first need to thank my supervisor, Elad Michael Schiller, who guided me through the whole project with great patience and wisdom on not only academic research but also how to be a strong and honest man. His passion and devotion to his work have set a standard for me to reach regardless of my career choice in the future. Iam so grateful to have him as my supervisor.  \nI extend my sincere appreciation to my company advisor, Hasan Mahmood. Hasan’s practical insights, expert advice, and willingness to share his knowledge have played a pivotal role in enriching the relevance and applicability of my research within the corporate context. His support and encouragement have provided me with invaluable perspectives and guidance, enabling me to navigate the complexities of integrating academic theory with real-world practices effectively.  \nTo my beloved wife and daughters, I owe an immeasurable debt of gratitude for their unwavering love, encouragement, and support throughout my thesis journey. Their constant belief in my abilities, unwavering patience during challenging times, and endless encouragement have been the cornerstone of my academic achievements. Whether it was offering words of wisdom, lending a listening ear, or providing practical assistance, my family’s steadfast support has been the driving force behind my perseverance and success. I am prof","cbCaiaiumlrV7NXI","https://ap.wps.com/l/cbCaiaiumlrV7NXI","pdf",2081310,1,70,"English","en",105,"# Introduction\n## Background\n## Problem Statement\n## Motivation and Related Work\n## Contribution of This Work\n## Disposition\n# Methods\n## Crash Failures\n## Failure Detectors\n## Unreliable Failure Detectors\n## Eventually Perfect Failure Detectors\n## Heartbeat Failure Detector\n## Analytical Failure Detectors\n## Machine Learning\n## Binary Classifiers\n## Ensemble Methods\n## Data Points Generation\n## Parameter Module\n## Timed and Time-free Windows\n## Generating Data Points\n## Precision-Distribution Module\n## Data Leakage","[{\"question\":\"Which failure detector models perform best in detection accuracy and detection time?\",\"answer\":\"Random Forest and Support Vector Machine failure detectors emerge as the top performers based on the study’s metrics, especially for detection time and average mistake rate.\"},{\"question\":\"What main problem remains with machine learning-based failure detectors?\",\"answer\":\"Machine learning-based detectors show higher error rates when detection times become longer, creating an accuracy–latency imbalance.\"},{\"question\":\"How does the proposed ensemble approach address the accuracy–completeness trade-off?\",\"answer\":\"Three ensemble methods are proposed, including an integration of top machine learning and analytical failure detectors. A precision distribution module improves accuracy, while an analytical fallback is used to mitigate completeness issues, depending on threshold relationships.\"}]","Using Machine Learning for Accelerating the Detection Time of Unreliable Failure Detectors | PDF",1785735255,176,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"using-machine-learning-for-accelerating-the-detection-time-of-unreliable-failure-detectors","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-machine-learning-for-accelerating-the-detection-time-of-unreliable-failure-detectors/121363/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which failure detector models perform best in detection accuracy and detection time?","Question",{"text":75,"@type":76},"Random Forest and Support Vector Machine failure detectors emerge as the top performers based on the study’s metrics, especially for detection time and average mistake rate.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What main problem remains with machine learning-based failure detectors?",{"text":80,"@type":76},"Machine learning-based detectors show higher error rates when detection times become longer, creating an accuracy–latency imbalance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed ensemble approach address the accuracy–completeness trade-off?",{"text":84,"@type":76},"Three ensemble methods are proposed, including an integration of top machine learning and analytical failure detectors. 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