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A key performance and user-experience challenge is cold start latency. This research presents a machine learning framework to predict and improve cold start latency, using predictive models for anticipation and duration based on historical data analysis and real-time metrics. Regression, decision trees, and neural networks are developed to learn patterns that traditional approaches cannot capture. The models forecast cold starts accurately, supporting proactive resource allocation and optimization. The framework is designed to adapt and scale within dynamic serverless environments, integrating with cloud infrastructure to enhance deployment responsiveness. By improving resource utilization efficiency, the approach helps reduce service-provider costs and fills a performance gap for practical elevation of serverless application execution.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-based-improved-cold-start-latency-prediction-framework-in-serverless-computing-msc-research-project/290701/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-based-improved-cold-start-latency-prediction-framework-in-serverless-computing-msc-research-project/290701.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the research address in serverless computing?","Question",{"text":113,"@type":114},"The research focuses on cold start latency, which adds delay when function execution environments are initialized from scratch.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the framework predict cold start latency?",{"text":118,"@type":114},"It builds machine learning models using historical data analysis plus real-time metrics, including regression, decision trees, and neural networks.",{"name":120,"@type":111,"acceptedAnswer":121},"Why is proactive prediction useful for serverless systems?",{"text":122,"@type":114},"Accurate forecasts enable strategies for resource allocation and optimization before cold starts occur, improving responsiveness and efficiency while helping reduce costs.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290701,1789642391,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Machine Learning-Based Improved Cold Start Latency Prediction Framework in Serverless Computing  \nMSc Research Project  \nCloud Computing  \nSumanth Varma Kallepalli  \nStudent ID: 22244263  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Shaguna Gupta  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nStudent Name: Sumanth Varma Kallepalli  \nStudent ID: 22244263  \nProgramme: MSc In Cloud Computing Year: 2023-2024  \nModule: MSc Research Project  \nSupervisor: 16/09/2024  \nSubmission Due  \nDate: 16/09/2024  \nProject Title: Machine Learning Based Improved Cold Start Latency Prediction Frame work in serverless computing  \nWord Count: 9647 PageCount: 27  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature: Sumanth Varma Kallepalli  \nDate: 16/09/2024  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nMachine Learning-Based Improved Cold Start Latency Prediction Framework in Serverless  \nComputing  \nSumanth Varma Kallepalli  \nX22244263  \nAbstract  \nIn the contemporary world of cloud computing, serverless computing is the game-changer paradigm that enables on-demand, scalable, and cost-effective ways to deploy applications. One such critical problem related to performance and user experience in serverless computing is cold start latency. This paper introduces a machine learning framework for the prediction and improvement of cold start latency with the objective of enhancing serverless computing efficiency. We develop predictive models for cold start anticipation and duration prediction using machine learning techniques based on historical data analysis, together with real-time metrics. Machine-learning-based regression, decision trees, and neural networks have to be developed addressing patterns and correlating traditional techniques that are incapable of doing this task.We show that the built predictive models are able to forecast cold starts accurately, thus enabling strategies for resource allocation and optimization in a proactive manner. This not only helps in making applications more responsive but also gives a fillip to efficiency in resource utilization an important factor in reining costs for the service provider. Also, we design the framework to adapt and scale to the dynamic nature of serverless environments. Through integration with cloud infrastructure, it seamlessly augments the current state-of-theart in offerings for serverless. This research fills an important gap in performance through effective means for the practical elevation of deployment and execution of serverless applications.This machine learning approach therefore represents a substantial step forward in the optimization of serverless computing for more effective and efficient cloud services.  \nKeywords: Cloud Computing, Machine Learning, Cost ","cbCaivRWXtrhf1xb","https://ap.wps.com/l/cbCaivRWXtrhf1xb","pdf",1673368,29,"English","# Abstract\n# Keywords\n# 1 Introduction\n## Background","[{\"question\":\"What problem does the research address in serverless computing?\",\"answer\":\"The research focuses on cold start latency, which adds delay when function execution environments are initialized from scratch.\"},{\"question\":\"How does the framework predict cold start latency?\",\"answer\":\"It builds machine learning models using historical data analysis plus real-time metrics, including regression, decision trees, and neural networks.\"},{\"question\":\"Why is proactive prediction useful for serverless systems?\",\"answer\":\"Accurate forecasts enable strategies for resource allocation and optimization before cold starts occur, improving responsiveness and efficiency while helping reduce costs.\"}]","Machine Learning-Based Improved Cold Start Latency Prediction Framework in Serverless Computing - MSc Research Project | PDF",73]