A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors

Chu Ge Wu; Ling Wang; Jing Jing Wang

doi:10.1109/COASE.2017.8256173

A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors

Chu Ge Wu, Ling Wang, Jing Jing Wang

Tsinghua University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The development of cloud computing drives the research on parallel processing. One of the important problems in parallel processing is to minimize the makespan of the tasks with precedence constraints on multiprocessors scheduling. In this paper, the property of the t-level (top-level) is analyzed, and a t-level (top level) driven search is proposed to enhance the exploitation ability of the efficient estimation of distributed algorithm (eEDA), which was developed for solving the precedence constrained scheduling problem. Numerical tests and comparisons are carried out. The results demonstrate that the t-level driven search is able to improve the optimization capacity of the eEDA under heterogeneous multiprocessor situation. Moreover, it is also shown that the eEDA with the t-level driven search on homogeneous computing systems is effective.

Original language	English
Title of host publication	2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
Publisher	IEEE Computer Society
Pages	630-635
Number of pages	6
ISBN (Electronic)	9781509067800
DOIs	https://doi.org/10.1109/COASE.2017.8256173
Publication status	Published - 1 Jul 2017
Externally published	Yes
Event	13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China Duration: 20 Aug 2017 → 23 Aug 2017

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2017-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	13th IEEE Conference on Automation Science and Engineering, CASE 2017
Country/Territory	China
City	Xi'an
Period	20/08/17 → 23/08/17

Keywords

estimation of distribution algorithm
parallel computing system
t-level
task graph allocation

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10.1109/COASE.2017.8256173

Cite this

Wu, C. G., Wang, L., & Wang, J. J. (2017). A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors. In 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017 (pp. 630-635). (IEEE International Conference on Automation Science and Engineering; Vol. 2017-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2017.8256173

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title = "A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors",

abstract = "The development of cloud computing drives the research on parallel processing. One of the important problems in parallel processing is to minimize the makespan of the tasks with precedence constraints on multiprocessors scheduling. In this paper, the property of the t-level (top-level) is analyzed, and a t-level (top level) driven search is proposed to enhance the exploitation ability of the efficient estimation of distributed algorithm (eEDA), which was developed for solving the precedence constrained scheduling problem. Numerical tests and comparisons are carried out. The results demonstrate that the t-level driven search is able to improve the optimization capacity of the eEDA under heterogeneous multiprocessor situation. Moreover, it is also shown that the eEDA with the t-level driven search on homogeneous computing systems is effective.",

keywords = "estimation of distribution algorithm, parallel computing system, t-level, task graph allocation",

author = "Wu, \{Chu Ge\} and Ling Wang and Wang, \{Jing Jing\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 13th IEEE Conference on Automation Science and Engineering, CASE 2017 ; Conference date: 20-08-2017 Through 23-08-2017",

year = "2017",

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doi = "10.1109/COASE.2017.8256173",

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Wu, CG, Wang, L & Wang, JJ 2017, A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors. in 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017. IEEE International Conference on Automation Science and Engineering, vol. 2017-August, IEEE Computer Society, pp. 630-635, 13th IEEE Conference on Automation Science and Engineering, CASE 2017, Xi'an, China, 20/08/17. https://doi.org/10.1109/COASE.2017.8256173

A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors. / Wu, Chu Ge; Wang, Ling; Wang, Jing Jing.
2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017. IEEE Computer Society, 2017. p. 630-635 (IEEE International Conference on Automation Science and Engineering; Vol. 2017-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors

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AU - Wang, Ling

AU - Wang, Jing Jing

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N2 - The development of cloud computing drives the research on parallel processing. One of the important problems in parallel processing is to minimize the makespan of the tasks with precedence constraints on multiprocessors scheduling. In this paper, the property of the t-level (top-level) is analyzed, and a t-level (top level) driven search is proposed to enhance the exploitation ability of the efficient estimation of distributed algorithm (eEDA), which was developed for solving the precedence constrained scheduling problem. Numerical tests and comparisons are carried out. The results demonstrate that the t-level driven search is able to improve the optimization capacity of the eEDA under heterogeneous multiprocessor situation. Moreover, it is also shown that the eEDA with the t-level driven search on homogeneous computing systems is effective.

AB - The development of cloud computing drives the research on parallel processing. One of the important problems in parallel processing is to minimize the makespan of the tasks with precedence constraints on multiprocessors scheduling. In this paper, the property of the t-level (top-level) is analyzed, and a t-level (top level) driven search is proposed to enhance the exploitation ability of the efficient estimation of distributed algorithm (eEDA), which was developed for solving the precedence constrained scheduling problem. Numerical tests and comparisons are carried out. The results demonstrate that the t-level driven search is able to improve the optimization capacity of the eEDA under heterogeneous multiprocessor situation. Moreover, it is also shown that the eEDA with the t-level driven search on homogeneous computing systems is effective.

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KW - parallel computing system

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A t-level driven search for estimation of distribution algorithm in solving task graph allocation to multiprocessors

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