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Quality-of-service aware design and management of embedded mixed-criticality systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quality-of-service aware design and management of embedded mixed-criticality systems/ by Behnaz Ranjbar, Alireza Ejlali, Akash Kumar.
Author:	Ranjbar, Behnaz.
other author:	Ejlali, Alireza.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xviii, 194 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Embedded computer systems - Design. -
Online resource:	https://doi.org/10.1007/978-3-031-38960-3
ISBN:	9783031389603

Quality-of-service aware design and management of embedded mixed-criticality systems
Ranjbar, Behnaz.

Quality-of-service aware design and management of embedded mixed-criticality systems[electronic resource] /by Behnaz Ranjbar, Alireza Ejlali, Akash Kumar. - Cham :Springer Nature Switzerland :2024. - xviii, 194 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9783031389603

Standard No.: 10.1007/978-3-031-38960-3doiSubjects--Topical Terms:

1001793
Embedded computer systems
--Design.

LC Class. No.: TK7895.E42

Dewey Class. No.: 006.22

Quality-of-service aware design and management of embedded mixed-criticality systems
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245 1 0 $a Quality-of-service aware design and management of embedded mixed-criticality systems $h [electronic resource] / $c by Behnaz Ranjbar, Alireza Ejlali, Akash Kumar.
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505 0 $a 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.
520 $a 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.
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700 1 $a Ejlali, Alireza. $3 3711344
700 1 $a Kumar, Akash. $3 1530138
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