A data intensive heuristic approach to the two-stage streaming scheduling problem

Wei Liang; Chunhua Hu; Min Wu; Qun Jin

doi:10.1016/j.jcss.2017.01.005

A data intensive heuristic approach to the two-stage streaming scheduling problem

Wei Liang, Chunhua Hu^*, Min Wu, Qun Jin

^*Corresponding author for this work

School of Human Sciences

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Data intensive computing (DIC) provides a high performance computing approach to process large volume of data. In this study, a new formalization is introduced to present the two-stage DIC task execution in a stream manner. A novel heuristic algorithm is proposed for the scheduling problem due to the NP complexity. The theoretical approximation ratio bounds for the heuristic are analyzed and confirmed by the experimental evaluation. Overall, we observe that the proposed method conducts average 1.2 times makespan than the theoretic bound of the optimal solution. Besides, the proposed method outperforms the GA and FIFO scheduling schemes with overall improvements.

Original language	English
Pages (from-to)	64-79
Number of pages	16
Journal	Journal of Computer and System Sciences
Volume	89
DOIs	https://doi.org/10.1016/j.jcss.2017.01.005
Publication status	Published - 2017 Nov

Keywords

Data intensive computing
Makespan
NP-hard
Scheduling

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)
Computer Networks and Communications
Computational Theory and Mathematics
Applied Mathematics

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10.1016/j.jcss.2017.01.005

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title = "A data intensive heuristic approach to the two-stage streaming scheduling problem",

abstract = "Data intensive computing (DIC) provides a high performance computing approach to process large volume of data. In this study, a new formalization is introduced to present the two-stage DIC task execution in a stream manner. A novel heuristic algorithm is proposed for the scheduling problem due to the NP complexity. The theoretical approximation ratio bounds for the heuristic are analyzed and confirmed by the experimental evaluation. Overall, we observe that the proposed method conducts average 1.2 times makespan than the theoretic bound of the optimal solution. Besides, the proposed method outperforms the GA and FIFO scheduling schemes with overall improvements.",

keywords = "Data intensive computing, Makespan, NP-hard, Scheduling",

author = "Wei Liang and Chunhua Hu and Min Wu and Qun Jin",

note = "Funding Information: This work was jointly supported by the National Nature Science Foundation of China under Grant No. 61273232 and No. 71210003, the Program for New Century Excellent Talents in University under NCET-13-0785, the Key Laboratory of Hunan Province for Mobile Business Intelligence under Grant No. 2015TP1002, and Mobile E-business 2011 Collaborative Innovation Center of Hunan Province. Publisher Copyright: {\textcopyright} 2017 Elsevier Inc.",

year = "2017",

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doi = "10.1016/j.jcss.2017.01.005",

language = "English",

volume = "89",

pages = "64--79",

journal = "Journal of Computer and System Sciences",

issn = "0022-0000",

publisher = "Academic Press Inc.",

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T1 - A data intensive heuristic approach to the two-stage streaming scheduling problem

AU - Liang, Wei

AU - Hu, Chunhua

AU - Wu, Min

AU - Jin, Qun

N1 - Funding Information: This work was jointly supported by the National Nature Science Foundation of China under Grant No. 61273232 and No. 71210003, the Program for New Century Excellent Talents in University under NCET-13-0785, the Key Laboratory of Hunan Province for Mobile Business Intelligence under Grant No. 2015TP1002, and Mobile E-business 2011 Collaborative Innovation Center of Hunan Province. Publisher Copyright: © 2017 Elsevier Inc.

PY - 2017/11

Y1 - 2017/11

N2 - Data intensive computing (DIC) provides a high performance computing approach to process large volume of data. In this study, a new formalization is introduced to present the two-stage DIC task execution in a stream manner. A novel heuristic algorithm is proposed for the scheduling problem due to the NP complexity. The theoretical approximation ratio bounds for the heuristic are analyzed and confirmed by the experimental evaluation. Overall, we observe that the proposed method conducts average 1.2 times makespan than the theoretic bound of the optimal solution. Besides, the proposed method outperforms the GA and FIFO scheduling schemes with overall improvements.

AB - Data intensive computing (DIC) provides a high performance computing approach to process large volume of data. In this study, a new formalization is introduced to present the two-stage DIC task execution in a stream manner. A novel heuristic algorithm is proposed for the scheduling problem due to the NP complexity. The theoretical approximation ratio bounds for the heuristic are analyzed and confirmed by the experimental evaluation. Overall, we observe that the proposed method conducts average 1.2 times makespan than the theoretic bound of the optimal solution. Besides, the proposed method outperforms the GA and FIFO scheduling schemes with overall improvements.

KW - Data intensive computing

KW - Makespan

KW - NP-hard

KW - Scheduling

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DO - 10.1016/j.jcss.2017.01.005

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SP - 64

EP - 79

JO - Journal of Computer and System Sciences

JF - Journal of Computer and System Sciences

ER -

A data intensive heuristic approach to the two-stage streaming scheduling problem

Abstract

Keywords

ASJC Scopus subject areas

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