{"id":{"repo_id":"texas-state","oai_identifier":"oai:digital.library.txst.edu:10877/24848"},"canonical_url":"https://search.dev.ndltd.org/etd/texas-state/oai:digital.library.txst.edu:10877/24848","repository":{"repo_id":"texas-state","name":"Texas State University","base_url":"https://digital.library.txst.edu/server/oai/request"},"display":{"title":"Optimizing Multi-Dimensional Efficiency of Foundation Models: From Fine-Tuning to Inference","abstract":"With the rapid advancement of foundation AI models and the widespread adoption of AI applications, the operational energy demand and environmental impacts of AI, have become increasingly significant. Evaluating AI systems should extend beyond accuracy and wall-clock performance to treat sustainability as a first-class objective. However, optimizing sustainability of AI systems is challenging due to the inherently multi-dimensional nature of efficiency (e.g., runtime, memory footprint, and energy) and the fact that operational emissions depend on both energy consumption and time- and location-varying carbon intensity under real-world constraints. This dissertation proposes an end-to-end methodology for sustainable AI that integrates measurement, multi-dimensional evaluation, and decision-making from fine-tuning to deployment scheduling. First, it introduces a multi-dimensional efficiency evaluation framework spanning runtime, GPU memory footprint, and energy consumption, demonstrating why single-metric optimization can be misleading. Second, it develops the first resource-aware decision frameworks that translate multi-dimensional efficiency measurement into actionable configuration choices for sustainable LLM fine-tuning under heterogeneous GPU environments. Third, it presents the first carbon-aware scheduling simulator that evaluates and guides carbon efficient and sustainable decisions under realistic capacity and latency constraints. Together, these contributions provide a systematic foundation for measuring, evaluating, and improving the sustainability of AI workloads in heterogeneous cloud environments.","abstract_html":"With the rapid advancement of foundation AI models and the widespread adoption of AI applications, the operational energy demand and environmental impacts of AI, have become increasingly significant. Evaluating AI systems should extend beyond accuracy and wall-clock performance to treat sustainability as a first-class objective. However, optimizing sustainability of AI systems is challenging due to the inherently multi-dimensional nature of efficiency (e.g., runtime, memory footprint, and energy) and the fact that operational emissions depend on both energy consumption and time- and location-varying carbon intensity under real-world constraints. This dissertation proposes an end-to-end methodology for sustainable AI that integrates measurement, multi-dimensional evaluation, and decision-making from fine-tuning to deployment scheduling. First, it introduces a multi-dimensional efficiency evaluation framework spanning runtime, GPU memory footprint, and energy consumption, demonstrating why single-metric optimization can be misleading. Second, it develops the first resource-aware decision frameworks that translate multi-dimensional efficiency measurement into actionable configuration choices for sustainable LLM fine-tuning under heterogeneous GPU environments. Third, it presents the first carbon-aware scheduling simulator that evaluates and guides carbon efficient and sustainable decisions under realistic capacity and latency constraints. Together, these contributions provide a systematic foundation for measuring, evaluating, and improving the sustainability of AI workloads in heterogeneous cloud environments.","abstract_has_math":false,"creators":["Chen, Dayuan"],"institution":"Texas State University","degree_name":"Doctor of Philosophy","degree_level":"Doctoral","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Zong, Ziliang"],"committee_chairs":[],"committee_members":["Islam, Tanzima","Metsis, Vangelis","Chen, Heping"],"year":2026,"date_issued":"2026-05","date_published":"2026-05","updated_at":"2026-07-27T21:22:45Z","subjects":["cloud computing","sustainability","artificial intelligence","fine-tuning","optimization","carbon awareness","workload shifting"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10877/24848","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zong, Ziliang"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Islam, Tanzima","Metsis, Vangelis","Chen, Heping"]},{"key":"dc:creator","label":"Author","values":["Chen, Dayuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-05-13T19:31:02Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-05"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Texas State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["cloud computing","sustainability","artificial intelligence","fine-tuning","optimization","carbon awareness","workload shifting"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10877/24848"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["With the rapid advancement of foundation AI models and the widespread adoption of AI applications, the operational energy demand and environmental impacts of AI, have become increasingly significant. 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Second, it develops the first resource-aware decision frameworks that translate multi-dimensional efficiency measurement into actionable configuration choices for sustainable LLM fine-tuning under heterogeneous GPU environments. Third, it presents the first carbon-aware scheduling simulator that evaluates and guides carbon efficient and sustainable decisions under realistic capacity and latency constraints. Together, these contributions provide a systematic foundation for measuring, evaluating, and improving the sustainability of AI workloads in heterogeneous cloud environments."]},{"key":"dc:format","label":"Dc Format","values":["Text"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["1 file (.pdf)"]},{"key":"dc:title","label":"Title","values":["Optimizing Multi-Dimensional Efficiency of Foundation Models: From Fine-Tuning to Inference"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zong, Ziliang"],"dc:contributor.committeemember":["Islam, Tanzima","Metsis, Vangelis","Chen, Heping"],"dc:creator":["Chen, Dayuan"],"dc:date.accessioned":["2026-05-13T19:31:02Z"],"dc:date.issued":["2026-05"],"dc:description.abstract":["With the rapid advancement of foundation AI models and the widespread adoption of AI applications, the operational energy demand and environmental impacts of AI, have become increasingly significant. 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