{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T20:28:07Z","timestamp":1783542487675,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CCF-2450615, CCF-2324514, CNS-2132385"],"award-info":[{"award-number":["CCF-2450615, CCF-2324514, CNS-2132385"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,22]]},"DOI":"10.1145\/3797248.3815399","type":"proceedings-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T10:46:08Z","timestamp":1781865968000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Revisiting \u201cCooler is Better\u201d: ITD-Aware Per-CPU Thermal Optimization for Sustainable Data Center Operation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6148-6887","authenticated-orcid":false,"given":"Jason","family":"Crop","sequence":"first","affiliation":[{"name":"ECE, Colorado State University, Fort Collins, CO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7693-703X","authenticated-orcid":false,"given":"Hayden","family":"Moore","sequence":"additional","affiliation":[{"name":"ECE, Colorado State University, Fort Collins, CO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0846-0066","authenticated-orcid":false,"given":"Sudeep","family":"Pasricha","sequence":"additional","affiliation":[{"name":"ECE, Colorado State University, Fort Collins, CO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"e_1_3_3_1_2_2","volume-title":"Artificial Intelligence and Data Centers Predicted to Drive Record High Energy Demand","author":"Accomando J.","year":"2025","unstructured":"J. Accomando and E. McClelland. 2025. Artificial Intelligence and Data Centers Predicted to Drive Record High Energy Demand. https:\/\/www.morganlewis.com\/blogs\/datacenterbytes\/2025\/02\/artificial-intelligence-and-data-centers-predicted-to-drive-record-high-energy-demand Accessed Feb 2026."},{"key":"e_1_3_3_1_3_2","volume-title":"Amazon EC2 Instances","author":"Services Amazon Web","year":"2026","unstructured":"Amazon Web Services. 2026. Amazon EC2 Instances. https:\/\/aws.amazon.com\/ec2"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"K. Cheong et\u00a0al. 2019. A Novel Methodology to Improve Cooling Efficiency at Data Centers. IEEE Access 7 (2019) 153799\u2013153809.","DOI":"10.1109\/ACCESS.2019.2946342"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CICC.2012.6330659"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"J. Crop and S. Pasricha. 2026. Classifying Performance Variations Across Server SKUs: Strategies for Optimizing Data Center Efficiency. IEEE Transactions on Computers (TC) (2026).","DOI":"10.1109\/TC.2026.3688914"},{"key":"e_1_3_3_1_7_2","volume-title":"International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV)","author":"De\u00a0Vogeleer K.","year":"2014","unstructured":"K. De\u00a0Vogeleer et\u00a0al. 2014. Modeling the Temperature Bias of Power Consumption for Nanometer-Scale CPUs in Application Processors. In International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV)."},{"key":"e_1_3_3_1_8_2","volume-title":"Equinix Metal Servers","author":"Metal Equinix","year":"2026","unstructured":"Equinix Metal. 2026. Equinix Metal Servers. https:\/\/deploy.equinix.com\/"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"N. Hogade and S. Pasricha. 2025. Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers. IEEE Transactions on Sustainable Computing (TSUSC) (2025).","DOI":"10.1109\/TSUSC.2024.3520969"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"N. Hogade S. Pasricha A.\u00a0A. Maciejewski H.\u00a0J. Siegel M. Oxley and E. Jonardi. 2018. Minimizing Energy Costs for Geographically Distributed Heterogeneous Data Centers. IEEE Transactions on Sustainable Computing (TSUSC) 3 4 (Oct\u2013Dec 2018).","DOI":"10.1109\/TSUSC.2018.2822674"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"N. Hogade S. Pasricha and H.\u00a0J. Siegel. 2022. Energy and Network Aware Workload Management for Geographically Distributed Data Centers. IEEE Transactions on Sustainable Computing (TSUSC) 7 2 (Apr\u2013Jun 2022).","DOI":"10.1109\/TSUSC.2021.3086087"},{"key":"e_1_3_3_1_12_2","volume-title":"Intel\u00ae Xeon\u00ae Processor Scalable Family Thermal Mechanical Specifications and Design Guide","author":"Corporation Intel","year":"2019","unstructured":"Intel Corporation. 2019. Intel\u00ae Xeon\u00ae Processor Scalable Family Thermal Mechanical Specifications and Design Guide. https:\/\/www.intel.com\/content\/dam\/www\/public\/us\/en\/documents\/guides\/xeon-scalable-thermal-guide.pdf"},{"key":"e_1_3_3_1_13_2","volume-title":"Intel\u00ae Xeon\u00ae Gold 6314U Processor","author":"Corporation Intel","year":"2021","unstructured":"Intel Corporation. 2021. Intel\u00ae Xeon\u00ae Gold 6314U Processor. https:\/\/www.intel.com\/content\/www\/us\/en\/products\/sku\/212461\/intel-xeon-gold-6314u-processor-48m-cache-2-30-ghz\/specifications.html"},{"key":"e_1_3_3_1_14_2","volume-title":"Intel\u00ae Xeon\u00ae Platinum 4th Gen Processors","author":"Corporation Intel","year":"2023","unstructured":"Intel Corporation. 2023. Intel\u00ae Xeon\u00ae Platinum 4th Gen Processors. https:\/\/www.intel.com\/content\/www\/us\/en\/ark\/products\/series\/228622\/4th-gen-intel-xeon-scalable-processors.html"},{"key":"e_1_3_3_1_15_2","volume-title":"Intel\u00ae Xeon\u00ae 6 processors","author":"Corporation Intel","year":"2024","unstructured":"Intel Corporation. 2024. Intel\u00ae Xeon\u00ae 6 processors. https:\/\/www.intel.com\/content\/www\/us\/en\/ark\/products\/series\/240357\/intel-xeon-6-processors.html"},{"key":"e_1_3_3_1_16_2","volume-title":"Model-Specific Registers (MSRs)","author":"Corporation Intel","year":"2026","unstructured":"Intel Corporation. 2026. Model-Specific Registers (MSRs). https:\/\/www.intel.com\/content\/www\/us\/en\/developer\/articles\/technical\/intel-sdm.html"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/2851553.2851567"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3716368.3735301"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"H. Niaz et\u00a0al. 2026. Concurrent and Orthogonal Software Power Meters for Accurate Runtime Energy Profiling of Parallel Hybrid Programs on Heterogeneous Hybrid Servers. IEEE Transactions on Parallel and Distributed Systems (TPDS) 37 2 (2026) 322\u2013339.","DOI":"10.1109\/TPDS.2025.3637511"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"M. Oxley E. Jonardi S. Pasricha H.\u00a0J. Siegel T. Maciejewski P.\u00a0J. Burns and G. Koenig. 2018. Rate-based Thermal Power and Co-location Aware Resource Management for Heterogeneous Data Centers. Journal of Parallel and Distributed Computing (JPDC) 112 (Feb 2018) 126\u2013139.","DOI":"10.1016\/j.jpdc.2017.04.015"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.111"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"S. Qi D. Milojicic C. Bash and S. Pasricha. 2026. SHIELD-EB: Sustainable Hybrid Evolutionary-Boosting Framework for Carbon Wastewater and Cost-Aware Datacenter Management. IEEE Access (2026).","DOI":"10.1109\/ACCESS.2026.3674066"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/IGSC64514.2024.00015"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"M. Sagi et\u00a0al. 2020. A Lightweight Nonlinear Methodology to Accurately Model Multicore Processor Power. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) 39 11 (2020) 3152\u20133164.","DOI":"10.1109\/TCAD.2020.3013062"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/DATE.2012.6176453"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"crossref","unstructured":"H.-L. Shih et\u00a0al. 2025. Research on energy optimization for liquid-cooled server cooling systems based on swarm intelligence algorithms and LSTM. Mechanics 41 (2025).","DOI":"10.1093\/jom\/ufaf051"},{"key":"e_1_3_3_1_27_2","volume-title":"511.povray_r SPEC CPU\u00ae2017 Benchmark Description","year":"2017","unstructured":"SPEC.org. 2017. 511.povray_r SPEC CPU\u00ae2017 Benchmark Description. https:\/\/www.spec.org\/cpu2017\/Docs\/benchmarks\/511.povray_r.html"},{"key":"e_1_3_3_1_28_2","unstructured":"S. Van\u00a0den Steen et\u00a0al. 2016. Analytical Processor Performance and Power Modeling Using Micro-Architecture Independent Characteristics. IEEE Trans. Comput. 65 12 (2016) 3537\u20133551."},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"crossref","unstructured":"D. Van\u00a0Le et\u00a0al. 2024. Impacts of Increasing Temperature and Relative Humidity in Air-Cooled Tropical Data Centers. IEEE Transactions on Sustainable Computing (TSUSC) 9 5 (2024) 790\u2013802.","DOI":"10.1109\/TSUSC.2024.3379550"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA59077.2024.00041"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"crossref","unstructured":"Y. Zhang et\u00a0al. 2023. The Global Energy Impact of Raising the Space Temperature for High-Density Data Centers. Cell Reports Physical Science 4 (2023).","DOI":"10.1016\/j.xcrp.2023.101624"},{"key":"e_1_3_3_1_32_2","volume-title":"Proceedings of the 49th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO)","author":"Zu Y.","year":"2016","unstructured":"Y. Zu et\u00a0al. 2016. Ti-states: Processor power management in the temperature inversion region. In Proceedings of the 49th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO) (Taipei, Taiwan)."}],"event":{"name":"IGSC '26: International Green and Sustainable Computing Conference","location":"Canandaigua USA","acronym":"IGSC 2026","sponsor":["SIGDA ACM Special Interest Group on Design Automation"]},"container-title":["Proceedings of the 16th ACM International Green and Sustainable Computing Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3797248.3815399","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3797248.3815399","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3797248.3815399","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T19:45:11Z","timestamp":1783539911000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3797248.3815399"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":31,"alternative-id":["10.1145\/3797248.3815399","10.1145\/3797248"],"URL":"https:\/\/doi.org\/10.1145\/3797248.3815399","relation":{},"subject":[],"published":{"date-parts":[[2026,6,22]]},"assertion":[{"value":"2026-06-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}