CLC number: TP301.6
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-04-11
Cited: 1
Clicked: 9777
Hamid Tabatabaee, Mohammad Reza Akbarzadeh-T, Naser Pariz. Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems[J]. Journal of Zhejiang University Science C, 2014, 15(6): 423-434.
@article{title="Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems",
author="Hamid Tabatabaee, Mohammad Reza Akbarzadeh-T, Naser Pariz",
journal="Journal of Zhejiang University Science C",
volume="15",
number="6",
pages="423-434",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300204"
}
%0 Journal Article
%T Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems
%A Hamid Tabatabaee
%A Mohammad Reza Akbarzadeh-T
%A Naser Pariz
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 6
%P 423-434
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300204
TY - JOUR
T1 - Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems
A1 - Hamid Tabatabaee
A1 - Mohammad Reza Akbarzadeh-T
A1 - Naser Pariz
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 6
SP - 423
EP - 434
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300204
Abstract: An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.
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