{"title":"动态软件控制器,以提高混合关键系统的资源利用率","authors":"A. Kritikakou, T. Marty, Matthieu Roy","doi":"10.1145/3110222","DOIUrl":null,"url":null,"abstract":"In real-time mixed-critical systems, Worst-Case Execution Time (WCET) analysis is required to guarantee that timing constraints are respected—at least for high-criticality tasks. However, the WCET is pessimistic compared to the real execution time, especially for multicore platforms. As WCET computation considers the worst-case scenario, it means that whenever a high-criticality task accesses a shared resource in multicore platforms, it is considered that all cores use the same resource concurrently. This pessimism in WCET computation leads to a dramatic underutilization of the platform resources, or even failing to meet the timing constraints. In order to increase resource utilization while guaranteeing real-time guarantees for high-criticality tasks, previous works proposed a runtime control system to monitor and decide when the interferences from low-criticality tasks cannot be further tolerated. However, in the initial approaches, the points where the controller is executed were statically predefined. In this work, we propose a dynamic runtime control which adapts its observations to online temporal properties, further increasing the dynamism of the approach, and mitigating the unnecessary overhead implied by existing static approaches. Our dynamic adaptive approach allows one to control the ongoing execution of tasks based on runtime information, and further increases the gains in terms of resource utilization compared with static approaches.","PeriodicalId":7063,"journal":{"name":"ACM Trans. Design Autom. Electr. Syst.","volume":"62 11 1","pages":"13:1-13:26"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"DYNASCORE: DYNAmic Software COntroller to Increase REsource Utilization in Mixed-Critical Systems\",\"authors\":\"A. Kritikakou, T. Marty, Matthieu Roy\",\"doi\":\"10.1145/3110222\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In real-time mixed-critical systems, Worst-Case Execution Time (WCET) analysis is required to guarantee that timing constraints are respected—at least for high-criticality tasks. However, the WCET is pessimistic compared to the real execution time, especially for multicore platforms. As WCET computation considers the worst-case scenario, it means that whenever a high-criticality task accesses a shared resource in multicore platforms, it is considered that all cores use the same resource concurrently. This pessimism in WCET computation leads to a dramatic underutilization of the platform resources, or even failing to meet the timing constraints. In order to increase resource utilization while guaranteeing real-time guarantees for high-criticality tasks, previous works proposed a runtime control system to monitor and decide when the interferences from low-criticality tasks cannot be further tolerated. However, in the initial approaches, the points where the controller is executed were statically predefined. In this work, we propose a dynamic runtime control which adapts its observations to online temporal properties, further increasing the dynamism of the approach, and mitigating the unnecessary overhead implied by existing static approaches. Our dynamic adaptive approach allows one to control the ongoing execution of tasks based on runtime information, and further increases the gains in terms of resource utilization compared with static approaches.\",\"PeriodicalId\":7063,\"journal\":{\"name\":\"ACM Trans. Design Autom. Electr. Syst.\",\"volume\":\"62 11 1\",\"pages\":\"13:1-13:26\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Trans. Design Autom. Electr. Syst.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3110222\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Trans. Design Autom. Electr. Syst.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3110222","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DYNASCORE: DYNAmic Software COntroller to Increase REsource Utilization in Mixed-Critical Systems
In real-time mixed-critical systems, Worst-Case Execution Time (WCET) analysis is required to guarantee that timing constraints are respected—at least for high-criticality tasks. However, the WCET is pessimistic compared to the real execution time, especially for multicore platforms. As WCET computation considers the worst-case scenario, it means that whenever a high-criticality task accesses a shared resource in multicore platforms, it is considered that all cores use the same resource concurrently. This pessimism in WCET computation leads to a dramatic underutilization of the platform resources, or even failing to meet the timing constraints. In order to increase resource utilization while guaranteeing real-time guarantees for high-criticality tasks, previous works proposed a runtime control system to monitor and decide when the interferences from low-criticality tasks cannot be further tolerated. However, in the initial approaches, the points where the controller is executed were statically predefined. In this work, we propose a dynamic runtime control which adapts its observations to online temporal properties, further increasing the dynamism of the approach, and mitigating the unnecessary overhead implied by existing static approaches. Our dynamic adaptive approach allows one to control the ongoing execution of tasks based on runtime information, and further increases the gains in terms of resource utilization compared with static approaches.