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Resource demand prediction supports cost-efficient allocation by anticipating future needs and provisioning accordingly. The study evaluates multiple machine learning approaches, including linear, ridge, and lasso regression, random forest and gradient boosting regression, and fully connected neural networks. Model performance is assessed using RMSE, MSE, MAE, and R-squared to determine the most accurate algorithm.",{"@graph":69,"@context":122},[70,84,105],{"@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/prediction-of-resource-allocation-in-cloud-computing-using-machine-learning-msc-research-project/290538/",{"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/prediction-of-resource-allocation-in-cloud-computing-using-machine-learning-msc-research-project/290538.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the project address in cloud computing?","Question",{"text":112,"@type":113},"It addresses the need to predict resource demand so cloud resources can be allocated efficiently and cost-effectively.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning algorithms are considered for the predictions?",{"text":117,"@type":113},"The project considers linear regression, ridge regression, lasso regression, random forest regression, gradient boosting regression, decision tree regression, and artificial neural networks, as well as fully connected neural networks.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the prediction performance evaluated?",{"text":121,"@type":113},"Evaluation uses root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), and R-squared to compare accuracy across models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290538,1789641964,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Prediction of Resource Allocation in cloud computing using Machine Learning  \nMSc Research Project  \nData Analytics  \nAruna Saravanapandian  \nStudent ID: x22182349  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Prof. Mohammed Hasanuzzaman  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nAruna Saravanapandian  \nStudent    \nName:  \nX22182349  \nStudent ID: ……………………………………………………………………………………………………………..……  \nData Analytics 2024  \nProgramme: ……………………………………………………………… Year: …………………………..  \nMSc Research project  \nModule:  \nSupervisor:  \nSubmission Due Date:  \nProject Title:  \nMohammed Hasanuzzaman  \n12/12/2024  \nPrediction of Resource allocation in cloud computing using Machine learning  \n5077 18  \nWord Count: ……………………………………… Page Count…………………………………………….……. .  \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.  \nAruna Saravanapandian  \nSignature:    \n03/12/2024  \nDate:    \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 isnot 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\nPrediction of Resource Allocation in Cloud Computing using Machine Learning  \nAruna Saravanapandian  \nX22182349  \nAbstract  \nCloud computing eliminates the use of physical devices as computational resources are made available virtually, also poses a high demand now due to the increase in necessity for the cloud resources. Cloud resources could cause high costs if they are not used wisely hence, the prediction of the resource demand will help us allocate the resources efficiently. The prediction of the resource demand is analyzed with the various machine learning algorithms like Linear regression, Ridge Regression, Lasso regression, Random Forest Regression, Gradient Boosting Regression and fully connected neural networks. The goal is determining the machine learning algorithm that helps in predicting the resource demand with greater accuracy. The evaluation metrics like root mean square error, mean squared error, mean absolute error and r squared are taken into consideration.  \n1 Introduction  \nCloud computing allows the revolutionary approach to computing by emphasizing the execution of programs over several computers like distributed computing. Resources are shared between economies leading to the rise of shared services and integrated infrastructure. Cloud computing utilizes parallel and distributed computing comprising virtual resources. Theresources are made available on demand and are cost-effective depending upon the usage. The data centres work as cloud providers and are provided by companies such as Amazon, Google, Microsoft etc. (Chaudhary and Banga, 2024) .  \nThe resource provisioning systems, when they are proactive, have a better response time thereby reducing the overall cost. This can be achieved by creating an efficient resource demand prediction model. The future CPU request, when predicted in adva","cbCaicPLprEGi5Ou","https://ap.wps.com/l/cbCaicPLprEGi5Ou","pdf",852916,18,"English","# Introduction\n## Research goal and questions\n## Objectives and study structure\n# Related Work\n## Scope of the related work section","[{\"question\":\"What problem does the project address in cloud computing?\",\"answer\":\"It addresses the need to predict resource demand so cloud resources can be allocated efficiently and cost-effectively.\"},{\"question\":\"Which machine learning algorithms are considered for the predictions?\",\"answer\":\"The project considers linear regression, ridge regression, lasso regression, random forest regression, gradient boosting regression, decision tree regression, and artificial neural networks, as well as fully connected neural networks.\"},{\"question\":\"How is the prediction performance evaluated?\",\"answer\":\"Evaluation uses root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), and R-squared to compare accuracy across models.\"}]","Prediction of Resource Allocation in Cloud Computing using Machine Learning - MSc Research Project | PDF",45]