Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Safety and Crime Assistance System for a Fast Track Response on Mobile Devices in Bhubaneswar Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Adaptive Dynamic Genetic Algorithm Based Node Scheduling for Time-Triggered Systems

verfasst von : B. Abdul Rahim, K. Soundara Rajan

Erschienen in: Computational Intelligence in Data Mining

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Nowadays, there has been tremendous increase in the use of reliable systems for safety critical applications such as avionics and automotives. As the systems become reliable, fault-tolerant design is must often involving strict timings. The implementation of such systems with traditional event-triggered approach is inappropriate; consequently, time-triggered approach is taking control. The time-triggered architectures are hard real-time embedded systems. Similarly, the scheduling process has to be redefined for optimality in resource allocation. The schedulability of tasks in such systems is analysed with meta-heuristic approach; genetic algorithm for optimization of processing nodes. Further, an adaptive approach has been arrived called adaptive dynamic genetic algorithm which allocates tasks to available nodes in a better optimized way for multiprocessor architecture.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Firefly Algorithm for Feature Selection in Sentiment Analysis
Nächstes Kapitel Deformation Monitoring of Volcanic Eruption Using DInSAR Method
Literatur
1.
C Scheidler, et.al, Time Triggered Architectures, EMMSEC’97, Advances in information Technologies: The business challenge, IOS Press, (1997), 758-765.
2.
Mike Falla (edt), Advances in safety critical Systems-Results and achievements from the DTI/EPSRC R&D Program (1997)1–2.
3.
Martin L Shooman, Reliability of Computer Systems and Networks. John Wiley (2000) 14–15.
4.
H kopetz, Real Time Systems, Kluwer Academic Publishers, Boston, (1997).
5.
M Sparchmann, Modeling a controller for a time triggered protocol, PhD Thesis, Vienna, University of Technology, (1997).
6.
A Albert, Comparison of event triggered and time triggered concepts with regard to distributed control systems, in Embedded World, (2004).
7.
N Navet, et.al, Trends in automotive communication systems, Proceedings of IEEE, Vol.93(6), (2005) 1204–1224.
8.
H kopetz, Event triggered versus Time triggered real time systems, Technical report 8/91, Insitut fur Technische Informatik TU Vienna, Austria (1991).
9.
L Sha, R Rajkumar, J P Lehoczky, Priority Inheritance Protocols: An Approach to real time synchronization, IEEE Transactions on Computers, Vol 39 (9), (1990) 1175–1185.
10.
S Poledna, Tolerating Sensor timing faults in highly responsive hard real time systems, IEEE Transactions on Computers, Vol 44 (2), (1995).
11.
D Ullman, NP-Complete scheduling problems, journal of computer systems & science, Vol.10(3), (1975) 384–393.
12.
J A Stankovic, et.al, implications of classical scheduling results for real time systems, Technical report UM-CS-94–089, computer science dept. University of Massachusetts, (1994).
13.
N Audsley, et.al, Fixed priority preemptive scheduling: An Historical perspective, Real time systems, Vol.8(3), (1995).
14.
F Balarin, et.al, Scheduling for embedded real time systems, IEEE Design and Test of Computers, Jan-Mar, (1998).
15.
J Xu and D L Parnas, On satisfying timing constraints in hard real time systems, IEEE Transactions on Software Engineering, Vol 19 (1), (1993).
16.
M Schwehm and T Walter, Mapping and Scheduling by Genetic Algorithms, Conference on Algorithms and Hardware for parallel processing, (1994) 832–841.
17.
K Tindell, A Burns and A J Wellings, Allocating Hard real time tasks (An NP-Hard problem made easy), Journal of Real time systems, Vol. 4(2), (1992) 145–165.
18.
Johnson D.S. & McGeoch L.A., “The Traveling Salesman Problem: A Case Study in Local Optimization”, in: E.H.L. Aarts, J.K. Lenstra (Eds.), Local Search in Combinatorial Optimization, Wiley, New York, (1997), 215–310.
19.
Probir Roy, Md. Mejbah Ul Alam and Nishita Das, “Heuristic Based Task Scheduling In Multiprocessor Systems With Genetic Algorithm By Choosing The Eligible Processor”, International Journal of Distributed and Parallel Systems (IJDPS) Vol.3(4), (2012).
20.
Besan Al Salibi, M B Jelodar and Ibrahim Venkat, “A Comparative study between the nearest neighbor and genetic algorithms: A revisit to the TSP”, IJCSEE, Vol 1(1), (2013) 34–38.
21.
H kopetz and G Gruenstiedl, TTP- A Protocol for Fault Tolerant Real Time Systems, IEEE Computer, Vol 24(1), (1994), 14–23.
22.
H kopetz, et.al, A Synchronization strategy for a TTP/C Controller, SAE paper 960120, SAE press Warrendale, (1996), 19–27.
23.
SAE: Class C Application Requirements – J2056/1, SAE Handbook, SAE Press Waarendale, (1994), 23.366–23.372.
24.
H kopetz and R Nossal, The Cluster Compiler – A Tool for the Design of Time Triggered RTS, ACM SIGPLAN Workshop, (1995).
25.
C Scheidler, L J Schafers and O K Fuhrmann, Software Engineering for parallel systems: The TRAPPER Approach, HICCS-28, IEEE CS Press, (1995), 349–358.
26.
G R Harik, F G Lobo and D E Goldberg, The compact Genetic Algorithm, IEEE Transactions on Evolutionary Computation, Vol 3(4), (1999) 287–297.
Metadaten
Titel
Adaptive Dynamic Genetic Algorithm Based Node Scheduling for Time-Triggered Systems
verfasst von
B. Abdul Rahim
K. Soundara Rajan
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Computational Intelligence in Data Mining

Print ISBN: 978-981-10-3873-0

Electronic ISBN: 978-981-10-3874-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-3874-7_67

Premium Partner

    Bildnachweise