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[null,null,"Autore",null,"Ranjbar Behnaz"]
[null,null,"Titolo",null,"Quality-of-Service Aware Design and Management of Embedded Mixed-Criticality Systems / / by Behnaz Ranjbar, Alireza Ejlali, Akash Kumar"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-38960-3"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (205 pages)"]
[null,null,"Disciplina",null,"006.22"]
[null,null,"Soggetti",null,"Electronic circuits"]
[{"$nulls":4},"Embedded computer systems"]
[{"$nulls":4},"Electronic circuit design"]
[{"$nulls":4},"Electronic Circuits and Systems"]
[{"$nulls":4},"Embedded Systems"]
[{"$nulls":4},"Electronics Design and Verification"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Introduction -- Preliminaries and Literature Reviews -- Bounding Time in Mixed-Criticality Systems -- Safety- and Task-Drop-Aware Mixed-Criticality Task Scheduling -- Learning-Based Drop-Aware Mixed-Criticality Task Scheduling -- Fault-Tolerance and Power-Aware Multi-Core Mixed-Criticality System Design -- QoS- and Power-Aware Run-Time Scheduler for Multi-Core Mixed-Criticality Systems -- Conclusion."]
[null,null,null,"Sommario/riassunto","This book addresses the challenges associated with efficient Mixed-Criticality (MC) system design. We focus on application analysis through execution time analysis and task scheduling analysis in order to execute more low-criticality tasks in the system, i.e., improving the Quality-of-Service (QoS), while guaranteeing the correct execution of high-criticality tasks. Further, this book addresses the challenge of enhancing QoS using parallelism in multi-processor hardware platforms. Provides an overview of the state-of-the-art in Mixed-Criticality research, related to Quality-of-Service improvement; Describes a novel theoretical approach to obtaining Worst-Case Execution Times (WCETs); Utilizes machine learning models and objective optimization techniques to improve the resource utilization and QoS. ."]