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CLC number: TP301.6

On-line Access: 2014-06-06

Received: 2013-08-01

Revision Accepted: 2014-01-17

Crosschecked: 2014-04-11

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.6 P.423-434


Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems

Author(s):  Hamid Tabatabaee, Mohammad Reza Akbarzadeh-T, Naser Pariz

Affiliation(s):  Department of Computer Engineering, Islamic Azad University, Quchan Branch, Quchan, Iran; more

Corresponding email(s):   hamid.tabatabaee@Iauq.ac.ir

Key Words:  Dynamic task scheduling, Fuzzy logic, Genetic algorithms, Unstructured environment, Linear switching state spaceAn erratum to this article can be found at doi:10.1631/jzus.C13e0204

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.

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%A Hamid Tabatabaee
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%A Naser Pariz
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300204

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
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DOI - 10.1631/jzus.C1300204

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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