{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:40:49Z","timestamp":1783698049611,"version":"3.55.0"},"reference-count":22,"publisher":"Association for Computing Machinery (ACM)","issue":"5s","license":[{"start":{"date-parts":[[2017,9,27]],"date-time":"2017-09-27T00:00:00Z","timestamp":1506470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2017,10,31]]},"abstract":"<jats:p>Recent embedded systems are becoming integrated systems with components of different criticality. To tackle this, mixed-criticality systems aim to provide different levels of timing assurance to components of different criticality levels while achieving efficient resource utilization. Many approaches have been proposed to execute more lower-criticality tasks without affecting the timeliness of higher-criticality tasks. Those previous approaches however have at least one of the two limitations; i) they penalize all lower-criticality tasks at once upon a certain situation, or ii) they make the decision how to penalize lower-criticality tasks at design time. As a consequence, they under-utilize resources by imposing an excessive penalty on low-criticality tasks. Unlike those existing studies, we present a novel framework, called MC-ADAPT, that aims to minimally penalize lower-criticality tasks by fully reflecting the dynamically changing system behavior into adaptive decision making. Towards this, we propose a new scheduling algorithm and develop its runtime schedulability analysis capable of capturing the dynamic system state. Our proposed algorithm adaptively determines which task to drop based on the runtime analysis. To determine the quality of task dropping solution, we propose the speedup factor for task dropping while the conventional use of the speedup factor only evaluates MC scheduling algorithms in terms of the worst-case schedulability. We apply the speedup factor for a newly-defined task dropping problem that evaluates task dropping solution under different runtime scheduling scenarios. We derive that MC-ADAPT has a speedup factor of 1.619 for task drop. This implies that MC-ADAPT can behave the same as the optimal scheduling algorithm with optimal task dropping strategy does under any runtime scenario if the system is sped up by a factor of 1.619.<\/jats:p>","DOI":"10.1145\/3126498","type":"journal-article","created":{"date-parts":[[2017,9,27]],"date-time":"2017-09-27T12:33:53Z","timestamp":1506515633000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["MC-ADAPT"],"prefix":"10.1145","volume":"16","author":[{"given":"Jaewoo","family":"Lee","sequence":"first","affiliation":[{"name":"University of Pennsylvania, Philadelphia, PA, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoon Sung","family":"Chwa","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, MI, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linh T. X.","family":"Phan","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia, PA, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Insik","family":"Shin","sequence":"additional","affiliation":[{"name":"KAIST, Daejeon, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Insup","family":"Lee","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia, PA, US"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,9,27]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"AUTOSAR. 2005. AUTomotive open system ARchitecture. www.autosar.org. (2005). AUTOSAR. 2005. AUTomotive open system ARchitecture. www.autosar.org. (2005)."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECRTS.2012.42"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2011.12"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01995675"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECRTS.2015.30"},{"key":"e_1_2_1_6_1","volume-title":"Workshop of Mixed Criticality Systems (WMC).","author":"Burns A.","unstructured":"A. Burns and S. Baruah . 2013. Towards a more practical model for mixed criticality systems . In Workshop of Mixed Criticality Systems (WMC). A. Burns and S. Baruah. 2013. Towards a more practical model for mixed criticality systems. In Workshop of Mixed Criticality Systems (WMC)."},{"key":"e_1_2_1_7_1","unstructured":"Alan Burns and Robert Davis. 2016. Mixed criticality systems -- A review. http:\/\/www-users.cs.york.ac.uk\/burns\/review.pdf. (2016). the seventh edition. Alan Burns and Robert Davis. 2016. Mixed criticality systems -- A review. http:\/\/www-users.cs.york.ac.uk\/burns\/review.pdf. (2016). the seventh edition."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2013.16"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECRTS.2012.24"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2834848.2834850"},{"key":"e_1_2_1_11_1","volume-title":"Real Time System Symposium (RTSS). 47--56","author":"Gu X.","unstructured":"X. Gu and A. Easwaran . 2016. Dynamic budget management with service guarantees for mixed-criticality systems . In Real Time System Symposium (RTSS). 47--56 . X. Gu and A. Easwaran. 2016. Dynamic budget management with service guarantees for mixed-criticality systems. In Real Time System Symposium (RTSS). 47--56."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECRTS.2015.9"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2011.10"},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","unstructured":"P. Huang P. Kumar N. Stoimenov and L. Thiele. 2013. Interference constraint graph - A new specification for mixed-criticality systems. In Emerging Technologies and Factory Automation (ETFA). 1--8. P. Huang P. Kumar N. Stoimenov and L. Thiele. 2013. Interference constraint graph - A new specification for mixed-criticality systems. In Emerging Technologies and Factory Automation (ETFA). 1--8.","DOI":"10.1109\/ETFA.2013.6647967"},{"key":"e_1_2_1_15_1","volume-title":"Workshop of Mixed Criticality Systems (WMC).","author":"Jan Mathieu","year":"2013","unstructured":"Mathieu Jan , Lilia Zaourar , and Maurice Pitel . 2013 . Maximizing the execution rate of low-criticality tasks in mixed criticality systems . In Workshop of Mixed Criticality Systems (WMC). Mathieu Jan, Lilia Zaourar, and Maurice Pitel. 2013. Maximizing the execution rate of low-criticality tasks in mixed criticality systems. In Workshop of Mixed Criticality Systems (WMC)."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/347476.347479"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2016.013"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/NAECON.1992.220669"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECRTS.2015.10"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECRTS.2012.39"},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Hang Su and Dakai Zhu. 2013. An elastic mixed-criticality task model and its scheduling algorithm. In Design Automation and Test in Europe (DATE). 147--152. Hang Su and Dakai Zhu. 2013. An elastic mixed-criticality task model and its scheduling algorithm. In Design Automation and Test in Europe (DATE). 147--152.","DOI":"10.7873\/DATE.2013.043"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2007.35"}],"container-title":["ACM Transactions on Embedded Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3126498","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3126498","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T22:34:08Z","timestamp":1750890848000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3126498"}},"subtitle":["Adaptive Task Dropping in Mixed-Criticality Scheduling"],"short-title":[],"issued":{"date-parts":[[2017,9,27]]},"references-count":22,"journal-issue":{"issue":"5s","published-print":{"date-parts":[[2017,10,31]]}},"alternative-id":["10.1145\/3126498"],"URL":"https:\/\/doi.org\/10.1145\/3126498","relation":{},"ISSN":["1539-9087","1558-3465"],"issn-type":[{"value":"1539-9087","type":"print"},{"value":"1558-3465","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,27]]},"assertion":[{"value":"2017-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-09-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}