Result: Event-driven dynamic workload sscaling for uniprocessor real-time embedded systems : Operating systems and adaptive applications (OSAA) track

Title:
Event-driven dynamic workload sscaling for uniprocessor real-time embedded systems : Operating systems and adaptive applications (OSAA) track
Authors:
CHANG, Li-Pin, CHEN, Ya-Shu
Source:
Journal of information science and engineering. 23(5):1349-1365
Publisher Information:
Taipei: Institute of Information Science, Academia sinica, 2007.
Publication Year:
2007
Physical Description:
print, 21 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse fonctionnelle, Functional analysis, Análisis funcional, Calculateur embarqué, Boarded computer, Calculador embarque, Charge travail, Workload, Carga trabajo, Clé sécurité, Security key, Llave seguridad, Effet dimensionnel, Size effect, Efecto dimensional, Estimation mouvement, Motion estimation, Estimación movimiento, Gestion événement, Event management, Gestión aconticimiento, Loi échelle, Scaling law, Ley escala, Monitorage, Monitoring, Monitoreo, Méthode adaptative, Adaptive method, Método adaptativo, Phénomène transitoire, Transients, Fenómeno transitorio, Poursuite cible, Target tracking, Précision élevée, High precision, Precisión elevada, Réponse transitoire, Transient response, Respuesta transitoria, Surcharge, Overload, Sobrecarga, Surveillance, Vigilancia, Système réactif, Reactive system, Sistema reactivo, Système temps réel, Real time system, Sistema tiempo real, Temps réel, Real time, Tiempo real, Traitement image, Image processing, Procesamiento imagen, adaptive applications, embedded systems, overload management, real-time surveillance, real-time systems
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science National Chiao Tung University, Hsinchu, 300, Tawain, Province of China
Department of Electrical Engineering National Taiwan University of Science and Technology, Taipei, 106, Tawain, Province of China
ISSN:
1016-2364
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19097835
Rights:
Copyright 2008 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19097835
Database:
PASCAL Archive

Further Information

Many embedded systems are designed to take timely reactions to the occurrences of particular scenarios. Such systems could sometimes experience transient overloads because of workload bursts or hardware malfunctions. Thus a mechanism to focus limited resources on the processing of urgent events is a key to retain system validity under stressing workloads. In this paper, we propose a new approach for workload scaling in uniprocessor real-time embedded systems. The idea is to view the system as a black box, and workload scaling for overload management can be done via very intuitive primitives, i.e., how hardware events are selectively fed into the system. Such a new approach removes the need for the adjustments of task periods and task phasing, which is important for many workload-scaling techniques. The proposed approach is implemented in a real-time surveillance system. Experimental results show that the system still delivers good accuracy and high responsiveness for visual-object tracking under the presence of overloads.