[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119450-en":3,"doc-seo-119450-105":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},119450,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Thermal and Energy Prediction for Energy-efficient Data Centers using Machine Learning","Operational costs in data centers rise significantly as computing demand and required resource provisioning grow. Reliability and scalability depend on effective energy management and resource distribution, making energy-efficient workload management a core approach in cloud environments. Thermal and energy modeling supports workload decisions, and machine learning has become widely used for thermal prediction. This thesis studies machine learning methods for thermal prediction, collects CPU utilization, temperature, and energy data, and applies regression and ML models to forecast CPU temperature. It also evaluates cooling energy costs across workload scheduling algorithms using GreenCloud simulations.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nThermal and Energy Prediction for Energy-efficient Data Centers using Machine Learning  \nA thesis submitted in partial fulfillment of the requirements For the degree of Master of Science in  \nComputer Science  \nBy  \nSai Pilla Vishnu  \nDecember 2022  \nThe graduate project of Sai Pilla Vishnu is approved:  \nProfessor David Freedman  \nDr. Taehyung Wang  \nDr. Xunfei Jiang, Chair  \nDate  \nDate  \nDate  \nCalifornia State University Northridge  \nTable of Contents  \nSignature Page ii  \nList of Figures iv  \nList of Tables v Abstract vi Chapter 1. Introduction 1  \nChapter 2. Related Works 3  \n2.1 Machine learning for thermal and energy prediction .............. 3  \n2.2 Energy efficient workload scheduling . . . . . . . . . . . . . . . . . . . . . . 4  \nChapter 3. Preliminary Work 6  \n3.1 Energy Consumption of Scheduling Algorithms ................. 6  \n3.2 Thermal Profiling on Cluster Servers ...................... 9  \nChapter 4. Design 10  \n4.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10  \n4.2 ML algorithms for thermal prediction ...................... 10  \n4.3 Workload management strategies on GreenCloud simulator .......... 12  \nChapter 5. Experiments 13  \n5.1 Testbed Cluster Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13  \n5.2 Experiment Setup ................................. 13  \nChapter 6. Modeling Results 18  \n6.1 Linear Regression Models ............................ 18  \n6.2 XGBoost Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18  \n6.3 XGBoost Model on Dataset 3 . . . . . . . . . . . . . . . . . . . . . . . . . . 22  \n6.4 CatBoost Model on Dataset 3 . . . . . . . . . . . . . . . . . . . . . . . . . . 23  \n6.5 LightGradient Model on Dataset 3 ....................... 24  \n6.6 Comparison of the Models............................. 25  \nChapter 7. Conclusion 26  \nBibliography 27  \nList of Figures  \n3.1 Green Cloud Architecture used in simulation . . . . . . . . . . . . . . . . . . . . 6  \n3.2 Comparison of Energy Consumption on Three-tier Architecture using Various Scheduling Algorithms ................................ 7  \n3.3 Comparison of Energy Consumption on Three-tier-high-speed Architecture using Various Scheduling Algorithms........................ 8  \n3.4 Temperature of the CPU cores running 2 threads of Whetstone ................................... 9  \n5.1 Multi-Core Temperature and Power Consumption .......................................................... 14  \n5.2 Single Core Temperature and Power Consumption ......................................................... 15  \n5.3 Multi-Core Different Utilization......................................................................................... 17  \n6.1 Comparison of average CPU temperature with linear regression models ..................... 18  \n6.2 Average CPU core temperature during heat-up phase.................................................... 19  \n6.3 Average CPU core temperature during cool-down phase................................................20  \n6.4 Comparison of chip 0’s average temperature .....................................................................21  \n6.5 Comparison of chip 1’s average temperature .....................................................................21  \n6.6 Comparison of chip 0’s average temperature with XGBoost ..........................................21  \n6.7 Comparison of chip 1’s average temperature with XGBoost ..........................................22  \n6.8 Scatter plot analysis on Data set 3 with XGBoost ..........................................................22  \n6.9 Scatter plot analysis on Data set 3 with Catboost ...........................................................23  \n6.10 Scatter plot analysis on Data set 3 with Light-Gradient .................................................24  \nList of Tables  \n5.1 Cluster System Configuration ......................................","cbCaigruteRSpIsi","https://ap.wps.com/l/cbCaigruteRSpIsi","pdf",1444657,1,36,"English","en",105,"# Chapter 1. Introduction\n# Chapter 2. Related Works\n## 2.1 Machine learning for thermal and energy prediction\n## 2.2 Energy efficient workload scheduling\n# Chapter 3. Preliminary Work\n## 3.1 Energy Consumption of Scheduling Algorithms\n## 3.2 Thermal Profiling on Cluster Servers\n# Chapter 4. Design\n## 4.1 Data Collection\n## 4.2 ML algorithms for thermal prediction\n## 4.3 Workload management strategies on GreenCloud simulator\n# Chapter 5. Experiments\n## 5.1 Testbed Cluster Server\n## 5.2 Experiment Setup\n# Chapter 6. Modeling Results\n## 6.1 Linear Regression Models\n## 6.2 XGBoost Model\n## 6.3 XGBoost Model on Dataset 3\n## 6.4 CatBoost Model on Dataset 3\n## 6.5 LightGradient Model on Dataset 3\n## 6.6 Comparison of the Models\n# Chapter 7. Conclusion\n# Bibliography","[{\"question\":\"What problem does the thesis address in energy-efficient data centers?\",\"answer\":\"It addresses how energy and thermal behavior affect data center operations and workload decisions, aiming to predict CPU temperature and evaluate cooling energy impact.\"},{\"question\":\"Which data are collected to build the thermal and energy prediction models?\",\"answer\":\"The study collects CPU utilization, CPU temperature, and energy data, then uses them as inputs for regression and machine learning models.\"},{\"question\":\"How are different workload scheduling algorithms evaluated?\",\"answer\":\"Electric energy cost and cooling energy cost are assessed by applying the thermal and energy models to workload scheduling algorithms executed in the GreenCloud simulator.\"}]","Thermal and Energy Prediction for Energy-efficient Data Centers using Machine Learning | PDF",1785724339,91,{"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},"thermal-and-energy-prediction-for-energy-efficient-data-centers-using-machine-learning","",{"@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/thermal-and-energy-prediction-for-energy-efficient-data-centers-using-machine-learning/119450/",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","2026-08-03",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},"What problem does the thesis address in energy-efficient data centers?","Question",{"text":75,"@type":76},"It addresses how energy and thermal behavior affect data center operations and workload decisions, aiming to predict CPU temperature and evaluate cooling energy impact.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data are collected to build the thermal and energy prediction models?",{"text":80,"@type":76},"The study collects CPU utilization, CPU temperature, and energy data, then uses them as inputs for regression and machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"How are different workload scheduling algorithms evaluated?",{"text":84,"@type":76},"Electric energy cost and cooling energy cost are assessed by applying the thermal and energy models to workload scheduling algorithms executed in the GreenCloud simulator.","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"]