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When tasks execute concurrently on different cores, their execution times can vary substantially with their allocated resources. Moreover, the instruction rate of a task during a job execution varies with time, and this variation pattern differs across tasks. Therefore, to improve performance it is crucial to incorporate the relationship between the resource budget allocated to each task and its time-varying instruction rate in task modeling, resource allocation, and scheduling algorithm design. Yet, no prior work has considered the fine-grained dynamic resource allocation and scheduling problems jointly while also providing hard real-time guarantees.<\/jats:p>\n          <jats:p\/>\n          <jats:p>\n            In this article, we introduce a resource-dependent multi-phase timing model that captures the time-varying instruction rates of a task under different resource allocations and that enables worst-case analysis under dynamic allocation. We present a method for constructing estimates of such a model based on task execution profiles, which can be obtained through measurements. We then present\n            <jats:sc>Rasco<\/jats:sc>\n            , a co-design technique for multicore resource allocation and scheduling of real-time DAG applications with end-to-end deadlines.\n            <jats:sc>Rasco<\/jats:sc>\n            leverages the resource-dependent multi-phase model of each task to simultaneously allocate resources at a fine granularity\n            <jats:italic toggle=\"yes\">and<\/jats:italic>\n            assign task deadlines. This approach maximizes execution progress under resource constraints while providing hard real-time schedulability guarantees. Our evaluation shows that\n            <jats:sc>Rasco<\/jats:sc>\n            substantially enhances schedulability and reduces end-to-end latency compared to the state of the art.\n          <\/jats:p>","DOI":"10.1145\/3761814","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T11:28:08Z","timestamp":1755602888000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Rasco: Resource Allocation and Scheduling Co-design for DAG Applications on Multicore"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7462-667X","authenticated-orcid":false,"given":"Abigail","family":"Eisenklam","sequence":"first","affiliation":[{"name":"Computer and Information Science, University of Pennsylvania","place":["Philadelphia, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2937-6215","authenticated-orcid":false,"given":"Robert","family":"Gifford","sequence":"additional","affiliation":[{"name":"Computer and Information Science, University of Pennsylvania","place":["Philadelphia, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1369-2495","authenticated-orcid":false,"given":"Georgiy A","family":"Bondar","sequence":"additional","affiliation":[{"name":"Applied Mathematics, University of California Santa Cruz","place":["Santa Cruz, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8125-1054","authenticated-orcid":false,"given":"Yifan","family":"Cai","sequence":"additional","affiliation":[{"name":"Computer and Information Science, University of Pennsylvania","place":["Philadelphia, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3864-1217","authenticated-orcid":false,"given":"Tushar","family":"Sial","sequence":"additional","affiliation":[{"name":"Aerospace Engineering, Iowa State University","place":["Ames, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7511","authenticated-orcid":false,"given":"Linh Thi Xuan","family":"Phan","sequence":"additional","affiliation":[{"name":"Computer and Information Science, University of Pennsylvania","place":["Philadelphia, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1509-5853","authenticated-orcid":false,"given":"Abhishek","family":"Halder","sequence":"additional","affiliation":[{"name":"Aerospace Engineering, Iowa State University","place":["Ames, United States"]},{"name":"Applied Mathematics, University of California Santa Cruz","place":["Ames, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/SIES.2015.7185039"},{"issue":"162","key":"e_1_3_2_3_2","first-page":"1","article-title":"A kernel multiple change-point algorithm via model selection","volume":"20","author":"Arlot Sylvain","year":"2019","unstructured":"Sylvain Arlot, Alain Celisse, and Zaid Harchaoui. 2019. A kernel multiple change-point algorithm via model selection. Journal of Machine Learning Research 20, 162 (2019), 1\u201356.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ECRTS.2014.22"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/1454115.1454128"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2006.27"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/RTAS48715.2020.000-3"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/RTAS58335.2023.00021"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","unstructured":"Xiaotian Dai. 2022. dag-gen-rnd: A randomized multi-DAG task generator for scheduling and allocation research. (March2022). DOI:10.5281\/zenodo.6334205. Accessed: November 7 2024.","DOI":"10.5281\/zenodo.6334205"},{"key":"e_1_3_2_10_2","volume-title":"WATERS","author":"Emberson P.","year":"2010","unstructured":"P. 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