A Real-Time Scheduler Using Neural Networks for Scheduling Independent and Nonpreemptable Tasks with Deadlines and Resource Requirements
Summary :
This paper describes a neural network scheduler for scheduling independent and nonpreemptable tasks with deadlines and resource requirements in critical real-time applications, in which a schedule is to be obtained within a short time span. The proposed neural network scheduler is an integrate model of two Hopfield-Tank neural network medels. To cope with deadlines, a heuristic policy which is modified from the earliest deadling policy is embodied into the proposed model. Computer simulations show that the proposed neural network scheduler has a promising performance, with regard to the probability of generating a feasible schedule, compared with a scheduler that executes a conventional algorithm performing the earliest deadline policy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E76-D No.8 pp.947-955
- Publication Date
- 1993/08/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Bio-Cybernetics
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Ruck THAWONMAS, Norio SHIRATORI, Shoichi NOGUCHI, "A Real-Time Scheduler Using Neural Networks for Scheduling Independent and Nonpreemptable Tasks with Deadlines and Resource Requirements" in IEICE TRANSACTIONS on Information,
vol. E76-D, no. 8, pp. 947-955, August 1993, doi: .
Abstract: This paper describes a neural network scheduler for scheduling independent and nonpreemptable tasks with deadlines and resource requirements in critical real-time applications, in which a schedule is to be obtained within a short time span. The proposed neural network scheduler is an integrate model of two Hopfield-Tank neural network medels. To cope with deadlines, a heuristic policy which is modified from the earliest deadling policy is embodied into the proposed model. Computer simulations show that the proposed neural network scheduler has a promising performance, with regard to the probability of generating a feasible schedule, compared with a scheduler that executes a conventional algorithm performing the earliest deadline policy.
URL: https://global.ieice.org/en_transactions/information/10.1587/e76-d_8_947/_p
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@ARTICLE{e76-d_8_947,
author={Ruck THAWONMAS, Norio SHIRATORI, Shoichi NOGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={A Real-Time Scheduler Using Neural Networks for Scheduling Independent and Nonpreemptable Tasks with Deadlines and Resource Requirements},
year={1993},
volume={E76-D},
number={8},
pages={947-955},
abstract={This paper describes a neural network scheduler for scheduling independent and nonpreemptable tasks with deadlines and resource requirements in critical real-time applications, in which a schedule is to be obtained within a short time span. The proposed neural network scheduler is an integrate model of two Hopfield-Tank neural network medels. To cope with deadlines, a heuristic policy which is modified from the earliest deadling policy is embodied into the proposed model. Computer simulations show that the proposed neural network scheduler has a promising performance, with regard to the probability of generating a feasible schedule, compared with a scheduler that executes a conventional algorithm performing the earliest deadline policy.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - A Real-Time Scheduler Using Neural Networks for Scheduling Independent and Nonpreemptable Tasks with Deadlines and Resource Requirements
T2 - IEICE TRANSACTIONS on Information
SP - 947
EP - 955
AU - Ruck THAWONMAS
AU - Norio SHIRATORI
AU - Shoichi NOGUCHI
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 1993
AB - This paper describes a neural network scheduler for scheduling independent and nonpreemptable tasks with deadlines and resource requirements in critical real-time applications, in which a schedule is to be obtained within a short time span. The proposed neural network scheduler is an integrate model of two Hopfield-Tank neural network medels. To cope with deadlines, a heuristic policy which is modified from the earliest deadling policy is embodied into the proposed model. Computer simulations show that the proposed neural network scheduler has a promising performance, with regard to the probability of generating a feasible schedule, compared with a scheduler that executes a conventional algorithm performing the earliest deadline policy.
ER -
English
