[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126842-en":3,"doc-seo-126842-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126842,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine Learning Models with Fault Tree Analysis for Explainable Failure Detection in Cloud Computing - Paper","Cloud computing availability depends on many interacting components, including software, hardware, management systems, security measures, environmental factors, and human operations. When faults occur, root cause analysis becomes difficult due to complex failure pathways. This paper integrates machine learning with fault tree analysis to improve explainable failure detection. The framework uses ML for fault tree selection and generation, predicts basic events, and derives top event probability to enhance diagnostic accuracy and interpretability.","# Machine Learning Models with Fault Tree Analysis for ExplainableFailure Detection in Cloud Computing\n\nRudolf HoffmannDa and Christoph ReichDb  \nInstitute for Data Science,Cloud Computing and IT Security,Furtwangen University,Germany  \nKeywords:Cloud Computing,Reliability,Machine Learning,AI,XAI,Transparency,Explainability,Surrogate Model,Failure Detection,Fault Tree Analysis,Root Cause Analysis.  \nAbstract:Cloud computing infrastructures availability rely on many components,like software,hardware,cloud man-agement system(CMS),security,environmental,and human operation,etc.If something goes wrong the rootcause analysis (RCA)is often complex.This paper explores the integration of Machine Learning(ML)withFault Tree Analysis(FTA)to enhance explainable failure detection in cloud computing systems.We introducea framework employing ML forFT selection and generation,and for predicting Basic Events(BEs)to enhancethe explainability of failure analysis.Our experimental validation focuses on predicting BEs and using thesepredictions to calculate the Top Event(TE)probability.The results demonstrate improved diagnostic accuracyand reliability,highlighting the potential of combining ML predictions with traditional FTA to identify rootcauses of failures in cloud computing environments and make the failure diagnostic more explainable.  \n## 1 INTRODUCTION\n\nfor a comprehensive analysis of the pathways leadingto system failures,emphasizing how combinations ofcomponent failures or specific environmental condi-tions can converge to trigger a system fault.By me-thodically breaking down the fault process from theTE to the BEs via logical gates,FTA provides a clearand detailed map of potential fault pathways,therebyfacilitating targeted interventions to increase systemreliability and prevent failures(Mani and Mahendran,2017).  \nIn the rapidly evolving domain of cloud computing,ensuring the reliability of systems has become a majorconcern among users(Mesbahi et al.,2018).As cloudservices grow more complex,the potential for faultsincreases,making it crucial to employ sophisticatedmethods for fault detection and analysis(Ng'ang'aet al.,2023).  \nOne traditional approach for understanding andmitigating system failures is Fault Tree Analysis(FTA).FTA utilizes a Fault Tree (FT),a graphical rep-resentation that describes the logical connections be-tween various faults and their root causes through theuse of logical gates.At the heart of the FT are BasicEvents(BE),which are the fundamental fault condi-tions or failures that can occur within the system com-ponents.These BEs are interconnected through logi-cal gates(such as AND,OR,NOT gates)that definehow combinations of these BEs can lead to higher-level faults or system failures,ultimately leading tothe Top Event(TE)or system failure.FTA is in-herently deductive,starting with a system failure orTE and tracing back through the network of faults toidentify root causes.This structured approach allows  \nSimultaneously,the field of Machine Learning(ML)has shown great promise in enhancing the ca-pabilities of fault detection and prediction in cloudcomputing environments(Yang and Kim,2022).ML,particularly through its subfield of Deep Learning(DL),offers powerful tools for identifying patternsand anomalies in data that may indicate impend-ing failures.However,many ML techniques,espe-cially those involving DL,suffer from a lack of trans-parency.When these models predict a TE or sys-tem failure,they often do not provide insight into theunderlying causes or the logical pathway leading tothat prediction.This “black box”nature of ML es-pecially DL models poses a significant challenge infault analysis,where understanding the root causes iscrucial for effective mitigation and prevention(Hoff-mann and Reich,2023).  \nCloud computing infrastructures availability rely  \non many components,like software,hardware,CloudManagement System(CMS),security,environmental,and human operation,etc.If something goes wrongthe Root Cause Anal","cbCaiqmM9VtuiYIp","https://ap.wps.com/l/cbCaiqmM9VtuiYIp","pdf",1172472,1,"English","en",105,"# Introduction\n## Fault Tree Analysis basics\n## Machine learning and explainability challenges\n## Proposed integration framework\n# Theoretical framework\n## ML combined with fault trees\n# Experimental validation","[{\"question\":\"What problem does the paper address in cloud computing reliability?\",\"answer\":\"It targets the difficulty of performing explainable root cause analysis when complex faults occur across many cloud components.\"},{\"question\":\"How does fault tree analysis help explain failure pathways?\",\"answer\":\"FTA starts from a top event and uses logical gates to decompose it into intermediate and basic events, mapping possible fault pathways for targeted mitigation.\"},{\"question\":\"How does the proposed approach improve explainability using machine learning?\",\"answer\":\"It combines ML with fault tree analysis by using ML to select/generate fault trees and to predict basic events, then computing top event probabilities to link predictions to fault logic.\"}]","Machine Learning Models with Fault Tree Analysis for Explainable Failure Detection in Cloud Computing - Paper | 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