Result: Preemptive scheduling in overloaded systems

Title:
Preemptive scheduling in overloaded systems
Source:
Automata, languages and programming (Malaga, 8-13 July 2002)Lecture notes in computer science. :800-811
Publisher Information:
Berlin: Springer, 2002.
Publication Year:
2002
Physical Description:
print, 9 ref
Original Material:
INIST-CNRS
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science, University of California, Riverside, CA 92521, United States
School of Computer Science, The Interdisciplinary Center, P.O.B. 167, 46150 Herzliya, Israel
Department of Computer Science, California State University, Northridge, CA 91330, United States
Mathematical Institute, AS fdCR, Žitná 25, 11567 Praha, Czech Republic
Institut für Informatik, Albert-Ludwigs-Universität, Georges-Köhler-Allee, 79110 Freiburg, Germany
Facultad de Ciencias, Universidad Autonoma del Estado de Morelos, 62251 Cuernavaca, Morelos, Mexico
ISSN:
0302-9743
Rights:
Copyright 2003 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.14614258
Database:
PASCAL Archive

Further Information

The following scheduling problem is studied: We are given a set of tasks with release times, deadlines, and profit rates. The objective is to determine a 1-processor preemptive schedule of the given tasks that maximizes the overall profit. In the standard model, each completed task brings profit, while non-completed tasks do not. In the metered model, a task brings profit proportional to the execution time even if not completed. For the metered task model, we present an efficient offline algorithm and improve both the lower and upper bounds on the competitive ratio of online algorithms. Furthermore, we prove three lower bound results concerning resource augmentation in both models.