[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127065-en":3,"doc-seo-127065-105":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},127065,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning for Fault Prediction in Clouds - A Thesis","Cloud computing adoption expands data-center scale and complexity, increasing the likelihood of faults that can reduce performance, availability, and reliability while driving maintenance costs and revenue loss. Fault prediction is therefore critical, and machine learning is widely used for its pattern-recognition capabilities. However, accurate models for dynamic clouds are difficult to maintain due to concept drift, feature drift, and sensitivity to data-related parameters. This thesis addresses fault-prediction challenges and application performance degradation under such drifts using drift-adaptation and multi-objective optimization approaches.","Machine Learning for Fault Prediction in Clouds  \nBehshid Shayesteh  \nA Thesis  \nin the  \nConcordia Institute  \nfor  \nInformation Systems Engineering  \nPresented in Partial Fulfillment of the Requirements For the Degree of  \nDoctor of Philosophy (Information Systems Engineering) at Concordia University  \nMontral, Qubec, Canada  \nJuly 2024  \n© Behshid Shayesteh, 2024  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Behshid Shayesteh  \nEntitled: Machine Learning for Fault Prediction in Clouds  \nand submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy (Information Systems Engineering)  \ncomplies with the regulations of this University and meets the accepted standards with respect to originality and quality.  \nSigned by the Final Examining Committee:  \n  Chair  \nDr. Ferhat Khendek  \n  External Examiner Dr. Alberto Leon-Garcia  \nDr. Anjali Agarwal  External to Program  \n  Examiner  \nDr. Jamal Bentahar  \n  Examiner  \nDr. ChadiAssi  \nDr. Roch Glitho  Supervisor  \nApproved by  Dr. Jun Yan   \nChair of Department or Graduate Program Director July 18, 2024  \nDate of Defence    \nDr. Mourad Debbabi, Dean  \nGina Cody School of Engineering and Computer Science  \nAbstract  \nMachine Learning for Fault Prediction in Clouds  \nBehshid Shayesteh, Ph.D.  \nConcordia University, 2024  \nThe vast adoption of cloud computing has increased the size and complexity of data centers, increasing possibility of faults. Fault can negatively impact the performance, availability, and reliability of cloud services, leading to significant maintenance cost and revenue loss for cloud service providers. Therefore, fault prediction in clouds is a critical task. Machine Learning (ML) is increasingly used for this purpose due to their pattern recognition capabilities. While predicting faults in clouds using ML enables a proactive approach to prevent faults, building accurate prediction models that can maintain their performance in dynamic clouds is challenging. One problem is concept drift, where changes in data distribution can degrade model performance. Similarly, feature drift, which is changes in feature relevancy, can also degrade the model performance. Additionally, models accuracy is influenced by data-related parameters, necessitating selection of these parameters to achieve a high model performance. Existing ML-based fault prediction solutions do not focus on adaptability to dynamic conditions like concept or feature drift. Additionally, selecting data-related parameters to balance model performance and resource consumption is not addressed in current literature.  \nThis thesis mainly focuses on addressing the challenges of employing ML models for predicting faults and predicting application performance degradation caused by faults in cloud environments. We first propose a concept drift adaptation algorithm for fault prediction in clouds using Reinforcement Learning (RL) . This algorithm considers the cloud operator’s requirements, and uses RL to select the most appropriate drift adaptation method as well as data size for adaptation that fulfills  \nthe requirements. Second, we propose a feature drift adaptation solution for adapting the model to feature drifts while predicting application performance degradation in clouds. This solution consists of a feature drift detector that monitors the performance of the prediction model as well as the feature importance, and a feature drift adaptor that measures the drift severity to adapt the prediction model. Finally, we propose a multi-objective optimization algorithm to select the training data size, data sampling interval, input window, and prediction horizon for training an ML model that predicts application performance degradation in clouds.  \nAcknowledgments  \nFirst and foremost, I express my sincere gratitude to my Ph.D. supervisor, Prof. Roch Glitho. I appreciate all his contributions of time, ideas, and resources to ma","cbCaimyptpZQp1Qh","https://ap.wps.com/l/cbCaimyptpZQp1Qh","pdf",4471121,1,124,"English","en",105,"# Abstract\n# Acknowledgments\n# Doctoral Thesis Certification","[{\"question\":\"Why is fault prediction in cloud environments critical?\",\"answer\":\"Faults can degrade performance, availability, and reliability of cloud services, leading to higher maintenance costs and revenue loss. As cloud data centers grow in size and complexity, the fault risk increases.\"},{\"question\":\"What makes maintaining machine learning fault prediction models difficult in clouds?\",\"answer\":\"Dynamic cloud conditions introduce concept drift and feature drift, which can degrade prediction accuracy. Model performance is also influenced by data-related parameters that must be selected carefully.\"},{\"question\":\"How does the thesis adapt to concept drift and feature drift?\",\"answer\":\"It proposes a reinforcement-learning-based concept drift adaptation algorithm to choose suitable adaptation methods and data size. It also introduces a feature drift detector and adaptor to adjust the prediction model based on drift severity while predicting application performance degradation.\"}]","Machine Learning for Fault Prediction in Clouds - A Thesis | PDF",1785936631,312,{"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},"machine-learning-for-fault-prediction-in-clouds-a-thesis","",{"@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/machine-learning-for-fault-prediction-in-clouds-a-thesis/127065/",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","2026-08-05",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},"Why is fault prediction in cloud environments critical?","Question",{"text":75,"@type":76},"Faults can degrade performance, availability, and reliability of cloud services, leading to higher maintenance costs and revenue loss. As cloud data centers grow in size and complexity, the fault risk increases.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes maintaining machine learning fault prediction models difficult in clouds?",{"text":80,"@type":76},"Dynamic cloud conditions introduce concept drift and feature drift, which can degrade prediction accuracy. Model performance is also influenced by data-related parameters that must be selected carefully.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis adapt to concept drift and feature drift?",{"text":84,"@type":76},"It proposes a reinforcement-learning-based concept drift adaptation algorithm to choose suitable adaptation methods and data size. It also introduces a feature drift detector and adaptor to adjust the prediction model based on drift severity while predicting application performance degradation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]