[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121264-en":3,"doc-seo-121264-105":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},121264,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Towards Energy-Efficient and Sustainable Machine Learning Data Centers - Doctoral Thesis","Energy efficiency and sustainability of data centers grow harder as cloud services become more compute-intensive, especially for machine learning (ML) training and inference. This dissertation studies how ML-centric workloads using GPUs create new power and efficiency bottlenecks in inference and broader sustainability contexts. It proposes GPUNEST for multi-GPU inference energy characterization, ScaleServe for concurrent multi-model inference, and WattWiser for QoS-preserving GPU sharing with reduced power use. It also introduces PowerMorph and EcoCenter to support frequency regulation and quantify exogenous carbon for grid and carbon reduction.","UC Riverside  \nUC Riverside Electronic Theses and Dissertations  \nTitle  \nTowards Energy-Efficient and Sustainable Machine Learning Data Centers  \nPermalink  \n[https://escholarship.org/uc/item/7dh420g1](https://escholarship.org/uc/item/7dh420g1)  \nAuthor  \nJahanshahi, Ali  \nPublication Date  \n2024  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-ShareAlike License, available at [https://creativecommons.org/licenses/by](https://creativecommons.org/licenses/by)nc-sa/4 . 0/  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nRIVERSIDE  \nTowards Energy-Efficient and Sustainable Machine Learning Data Centers  \nA Dissertation submitted in partial satisfaction  \nof the requirements for the degree of  \nDoctor of Philosophy  \nin  \nComputer Science  \nby  \nAli Jahanshahi  \nDecember 2024  \nDissertation Committee:  \nDr. Daniel Wong, Chairperson  \nDr. Nael Abu-Ghazaleh  \nDr. Nanpeng Yu  \nDr. Elaheh Sadredini  \nCopyright by Ali Jahanshahi 2024  \nThe Dissertation of Ali Jahanshahi is approved:  \n\n|  |\n| --- |\n|  |\n|  |\n\nCommittee Chairperson  \nUniversity of California, Riverside  \nAcknowledgments  \nI express my profound appreciation to my advisor, whose guidance and support were instrumental throughout my PhD journey.  \nTo my family, whose unwavering support has been my anchor across cities and continents.  \nv  \nABSTRACT OF THE DISSERTATION  \nTowards Energy-Efficient and Sustainable Machine Learning Data Centers  \nby  \nAli Jahanshahi  \nDoctor of Philosophy, Graduate Program in Computer Science University of California, Riverside, December 2024  \nDr. Daniel Wong, Chairperson  \nEnergy efficiency and sustainability of data centers have become more challenging as cloud services evolve and become more computation-heavy, particularly with machine learning (ML) training and inference. The growing demand for ML-intensive workloads has led to the integration of more powerful hardware, such as Graphics Processing Units (GPUs), which introduces new sustainability and energy-efficiency challenges for data centers providing ML cloud services.  \nIn the first research direction, we address energy-efficiency challenges in data centers offering machine learning inference cloud solutions. Addressing these inefficiencies requires a comprehensive characterization of the system. To this end, we propose GPUNEST, a characterization framework targeting the energy efficiency of multi-GPU inference servers. To further implement energy efficiency techniques into inference servers, we developed ScaleServe, a scalable multi-GPU inference server capable of serving inference requests for multiple models concurrently. Additionally, we introduce WattWiser, an inference request scheduling policy that enables the sharing of GPUs among models during inference  \nwhile maintaining Quality of Service (QoS), leading to efficient GPU resource utilization and lower power consumption. Collectively, these solutions resulted in a significant reduction in power consumption and improved GPU resource utilization.  \nIn the second research direction, we focus on the sustainability challenges facing machine learning data centers. As these centers are predicted to consume significant amounts of electricity, they present a barrier to achieving net-zero carbon goals. We developed a framework, PowerMorph, that enables data centers to participate in frequency regulation programs. By engaging in these regulation services, data centers can help stabilize power grids that increasingly rely on intermittent renewable energy sources like wind and solar. This participation reduces dependence on fossil fuel-based power plants for grid balancing, thus lowering overall carbon emissions and promoting the integration of renewable energy. In EcoCenter, we target GPU-accelerated data centers due to higher power consumption an","cbCairDni6Do1OjA","https://ap.wps.com/l/cbCairDni6Do1OjA","pdf",5487372,1,160,"English","en",105,"# 1 Introduction\n## 1.1 Data Centers Energy Efficiency and Sustainability\n## 1.2 Research Objectives and Contributions\n# 2 Characterizing Energy Efficiency of Multi-GPU Inference Servers\n## 2.2 GPU-NEST Design\n## 2.3 Evaluation","[{\"question\":\"What problem does the dissertation address in ML data centers?\",\"answer\":\"It focuses on increasing energy-efficiency and sustainability challenges caused by ML-intensive cloud workloads, particularly GPU-heavy inference and the resulting power and carbon impacts.\"},{\"question\":\"What is GPUNEST and what is it used for?\",\"answer\":\"GPUNEST is a characterization framework that targets the energy efficiency of multi-GPU inference servers by instrumenting and studying inference behavior.\"},{\"question\":\"How does the dissertation approach sustainability beyond inference optimization?\",\"answer\":\"It develops PowerMorph to enable data centers to participate in frequency regulation programs and introduces EcoCenter to quantify exogenous carbon and evaluate carbon-reduction potential via regulation services.\"}]","Towards Energy-Efficient and Sustainable Machine Learning Data Centers - Doctoral Thesis | PDF",1785734756,403,{"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},"towards-energy-efficient-and-sustainable-machine-learning-data-centers-doctoral-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/towards-energy-efficient-and-sustainable-machine-learning-data-centers-doctoral-thesis/121264/",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 dissertation address in ML data centers?","Question",{"text":75,"@type":76},"It focuses on increasing energy-efficiency and sustainability challenges caused by ML-intensive cloud workloads, particularly GPU-heavy inference and the resulting power and carbon impacts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is GPUNEST and what is it used for?",{"text":80,"@type":76},"GPUNEST is a characterization framework that targets the energy efficiency of multi-GPU inference servers by instrumenting and studying inference behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the dissertation approach sustainability beyond inference optimization?",{"text":84,"@type":76},"It develops PowerMorph to enable data centers to participate in frequency regulation programs and introduces EcoCenter to quantify exogenous carbon and evaluate carbon-reduction potential via regulation services.","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"]