{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T13:47:21Z","timestamp":1782481641069,"version":"3.54.5"},"reference-count":29,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Guangdong Provincial Natural Science Foundation Project","award":["2025A1515010113"],"award-info":[{"award-number":["2025A1515010113"]}]},{"name":"Innovative Development Joint Fund of Natural Science Foundation in Shandong Province","award":["ZR2024LZH012"],"award-info":[{"award-number":["ZR2024LZH012"]}]},{"name":"Major Key Project of PCL, China","award":["PCL2025A08 and PCL2025A11"],"award-info":[{"award-number":["PCL2025A08 and PCL2025A11"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Archit. Code Optim."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    The rapid expansion of cloud computing has made precise performance evaluation a critical necessity. However, conventional cloud benchmarks often face significant limitations in simulation environments\u2014necessary for scalable and cost-effective testing\u2014due to the complexity of technology stacks and substantial runtime overheads. Proxy benchmarking has thus emerged as a practical alternative. Existing methods primarily focus on the global similarity of micro-architectural metrics between proxy benchmarks and real workloads but neglect their temporal similarity, leading to inaccurate performance evaluations, flawed cache behavior simulations, and misguided architectural optimization decisions. To address this, we present\n                    <jats:bold>PaTGen<\/jats:bold>\n                    , a phase-aware method for generating proxy benchmarks that accurately reflect both global and temporal similarity. By partitioning workloads into phases and formulating proxy generation as nonlinear optimization problems, PaTGen further refines intra-phase execution patterns via the delay-based temporal similarity optimization (DTSO) technique. Evaluations on 15 real-world workloads show PaTGen achieves over 97% global similarity in key metrics while significantly outperforming state-of-the-art methods in temporal similarity. Ablation studies confirm the efficacy of phase division and DTSO. Further experiments confirm its scalability and generalizability across architectures. Moreover, the effectiveness observed in downstream tasks provides empirical evidence that preserving temporal similarity is a fundamental requirement for proxy benchmarks to faithfully capture real workload behavior.\n                  <\/jats:p>","DOI":"10.1145\/3816437","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T10:01:54Z","timestamp":1780135314000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PaTGen: Temporal Similarity-Driven Proxy Benchmark Generation Method for Cloud Workloads"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4467-5743","authenticated-orcid":false,"given":"Haolang","family":"Yin","sequence":"first","affiliation":[{"name":"South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6876-1795","authenticated-orcid":false,"given":"Weiwei","family":"Lin","sequence":"additional","affiliation":[{"name":"South China University of Technology","place":["Guangzhou, China"]},{"name":"Pengcheng Laboratory","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1503-572X","authenticated-orcid":false,"given":"Huikang","family":"Huang","sequence":"additional","affiliation":[{"name":"South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6668-7648","authenticated-orcid":false,"given":"Xiaoxuan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4975-668X","authenticated-orcid":false,"given":"Haocheng","family":"Zhong","sequence":"additional","affiliation":[{"name":"South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-4048","authenticated-orcid":false,"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, State University of New York at New Paltz","place":["New Paltz, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Alibaba Cluster Trace Programs","year":"2018","unstructured":"Alibaba. 2018. 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