[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122777-en":3,"doc-seo-122777-105":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},122777,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Centered Energy Optimization In Cloud Computing - A Review","Rapid cloud computing growth increases energy consumption, creating environmental and economic pressure that motivates energy-efficiency optimization. The review synthesizes machine-learning-based approaches for cloud computing energy efficiency, comparing learning models, ML tools, strengths and limits, datasets, evaluation metrics, and performance. Existing methods are categorized into virtual machine selection, placement, migration, and consolidation. Deep reinforcement learning, TensorFlow, and CloudSim (for dataset generation) are identified as the most widely adopted choices.","Machine Learning Centered Energy Optimization In Cloud Computing: A Review  \nNomsa Puso1, Tshiamo Sigwele2, Oba Zubair Mustapha3  \n1,2Department of Computer Science and Information Systems, BIUST University, Botswana  \n3Institute of Technology, Kwara State Polytechnic, Nigeria  \nArticle history:  \nReceived Aug 19, 2023 Revised Sep 3, 2023 Accepted Sep 22, 2023  \nKeywords:  \nEnergy Efficiency Cloud Computing Machine Learning Reinforcement Learning Virtual Machines  \nCorresponding Author:  \nThe rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing.  \nCopyright © 2023 Institute of Advanced Engineering and Science.  \nAll rights reserved.  \nTshiamo Sigwele,  \nDepartment of Computer Science and Information Systems,  \nBotswana International University of Science and Technology (BIUST) , Plot 10071, Boseja, Palapye, Botswana.  \nEmail: [sigwelet@biust.ac.bw](sigwelet@biust.ac.bw)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCloud computing is a fast-growing technology combining two significant trends: Information Technology (IT) efficiency and business agility [1], [2] . The National Institute of Standards and Technology (NIST) defines Cloud computing as a unique model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with the least service provider interactions and management efforts. These resources are networks, servers, storage, applications, and services. The cloud offers the ability to store data without restrictions and to hide a vast amount of data from other users. The users can access the required files, documents, and applications on demand. Users only pay for the services provided by the cloud vendors instead of buying the expensive Infrastructure.  \nA data centre’s power consumption is divided into three categories: cooling systems, data centre networks and servers [1] . Cooling systems takes 15% to 30% of the power, and servers consume 40% to 55%  \n[1] . The network consumes 10% to 25% of the power and it was also reported that cloud data centres alone consume approximately 7% of global electricity and are expected to rise to 13% by 2030 [3] . This sector is also responsible for an estimated 2% of global emissions, comparable to the aviation industry. The energy consumption of data centres is expected to exceed 140 billion kilowatt-hours per year [4] . The energy-saving scheduling of data centres is critical for cloud service providers and will also contribute to environmental sustainability. The major problem in the currently in cloud computing energy optimization research is that there are limited critical reviews articles for machine learning based approaches in cloud computing energy  \noptimization to direct and help researchers. There are to the best ofour knowledge no reviews that can suggest the best ML model, tools, datasets, and evaluation metrics for current and future research. This study provides a comprehensive review of en","cbCaiaZPCaZwTEl7","https://ap.wps.com/l/cbCaiaZPCaZwTEl7","pdf",1251850,1,20,"English","en",105,"# Introduction\n## Energy consumption drivers in cloud data centers\n## Contributions and article organization\n# Research Method\n## Review objective and scope\n# Related Works\n## Non-ML energy efficiency techniques\n# ML-Based Energy Efficiency Techniques\n## Comparison of learning models, tools, datasets, and metrics\n# Results and Discussions\n## Most adopted approaches and analysis\n# Future Directions\n# Conclusion","[{\"question\":\"Why is energy optimization important in cloud computing?\",\"answer\":\"Cloud computing growth increases overall energy consumption, which affects both the environment and the economy. Data centers and their subsystems contribute substantially to global electricity use and emissions.\"},{\"question\":\"How does the review categorize machine learning energy-efficiency approaches?\",\"answer\":\"It organizes existing approaches by virtual machine selection, VM placement, VM migration, and consolidation methods.\"},{\"question\":\"Which ML models and tools are most widely adopted according to the review?\",\"answer\":\"Deep reinforcement learning, TensorFlow as a platform, and CloudSim for dataset generation are highlighted as the most widely adopted choices for ML-driven energy optimization.\"}]","Machine Learning Centered Energy Optimization In Cloud Computing - A Review | PDF",1785812838,50,{"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-centered-energy-optimization-in-cloud-computing-a-review","",{"@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-centered-energy-optimization-in-cloud-computing-a-review/122777/",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","2026-08-04",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 energy optimization important in cloud computing?","Question",{"text":75,"@type":76},"Cloud computing growth increases overall energy consumption, which affects both the environment and the economy. Data centers and their subsystems contribute substantially to global electricity use and emissions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the review categorize machine learning energy-efficiency approaches?",{"text":80,"@type":76},"It organizes existing approaches by virtual machine selection, VM placement, VM migration, and consolidation methods.",{"name":82,"@type":73,"acceptedAnswer":83},"Which ML models and tools are most widely adopted according to the review?",{"text":84,"@type":76},"Deep reinforcement learning, TensorFlow as a platform, and CloudSim for dataset generation are highlighted as the most widely adopted choices for ML-driven energy optimization.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]