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Schedule length and reliability-oriented multi-objective scheduling for distributed computing

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Abstract

Maximizing system reliability and minimizing schedule length are the two major objectives in scheduling a distributed computing system. These two objectives have been considered separately by most researchers, although more realistically they should be considered simultaneously. This paper addresses the problem by taking a multi-objective approach in scheduling. A Tabu search algorithm is proposed and two lateral interference schemes are used to distribute the Pareto optimal solutions along the Pareto front uniformly. Randomly generated directed acyclic graphs and a real application task graph are used to study the performance of the proposed algorithms. Experimental results show that for this problem lateral interference has no influence on the non-dominated solution number, but does benefit the uniform distribution of non-dominated solutions, irrespective of the computation method used to determine distances between the solutions.

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Acknowledgments

Guoquan Liu acknowledges support from the start-up grant from Xi’an Jiaotong-Liverpool University. Yifeng Zeng acknowledges support from NSFC (#61375070).

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Authors and Affiliations

  1. International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Guoquan Liu

  2. Department of Automation, Xiamen University, Xiamen, China

    Yifeng Zeng

  3. School of Computing, Teesside University, Middlesbrough, UK

    Yifeng Zeng

  4. The York Management School, University of York, York, United Kingdom

    Dong Li

  5. College of Computer Science, Sichuan University, Chengdu, China

    Yingke Chen

Authors
  1. Guoquan Liu
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  2. Yifeng Zeng
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  3. Dong Li
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  4. Yingke Chen
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Corresponding author

Correspondence to Yifeng Zeng.

Additional information

Communicated by V. Loia.

Appendix

Appendix

We provide extra results on examining the parameters in Algorithm 1.

1.1 Numbers for intensification and diversification strategies

We follow the graph setting in Table 2, and test the impact of the number for intensification and diversification strategies in Table 3.

Given the settings of (10, 300) and (40, 700) for the numbers of intensification and diversification strategies, we show the comparison of three schemes in Tables 6 and 7 respectively. The results verify better performance using either SA or SD.

Table 6 Comparison of three schemes for the setting of (10, 300) for the the number of intensification and diversification strategies respectively
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Table 7 Comparison of three schemes for the setting of (40, 700) for the the number of intensification and diversification strategies respectively
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Table 8 Comparison of three schemes based on UD for Gaussian Elimination given \(m\) = 70 and \(n\) = 20
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Table 9 Comparison of three schemes based on UD for Gaussian Elimination given \(m\) = 100 and \(n\) = 20
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1.2 Large task graph of the Gaussian elimination

We further test the Tabu search algorithm in a large real-world problem where the matrix size (\(m\)) is increased to 70 and 100. The results of comparing three schemes are shown in Tables 8 and 9 respectively. The results follow the same performance of using different schemes as that for \(m\) = 50.

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Liu, G., Zeng, Y., Li, D. et al. Schedule length and reliability-oriented multi-objective scheduling for distributed computing. Soft Comput 19, 1727–1737 (2015). https://doi.org/10.1007/s00500-014-1360-3

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  • DOI: https://doi.org/10.1007/s00500-014-1360-3

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