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Hybrid Evolutionary Workflow Scheduling Algorithm for Dynamic Heterogeneous Distributed Computational Environment

  • Conference paper
International Joint Conference SOCO’14-CISIS’14-ICEUTE’14

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 299))

Abstract

The optimal workflow scheduling is one of the most important issues in heterogeneous distributed computational environment. Existing heuristic and evolutionary scheduling algorithms have their advantages and disadvantages. In this work we propose a hybrid algorithm based on Heterogeneous Earliest Finish Time heuristic and genetic algorithm that combines best characteristics of both approaches. We also experimentally show its efficiency for variable workload in dynamically changing heterogeneous computational environment.

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References

  1. Yu, J., Buyya, R.: A taxonomy of workflow management systems for grid computing. Journal of Grid Computing 3(3-4), 171–200 (2005)

    Article  Google Scholar 

  2. Arabnejad, H.: List Based Task Scheduling Algorithms on Heterogeneous Systems-An overview (2013)

    Google Scholar 

  3. Topcuoglu, H., Hariri, S., Wu, M.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13(3), 260–274 (2002)

    Article  Google Scholar 

  4. Blythe, J., Jain, S., Deelman, E., Gil, A., Vahi, K.: Task scheduling strategies for workflow-based applications in grids. In: Proceedings of the 5th IEEE International Symposium on Cluster Computing and the Grid (CCGRID 2005), UK (May 2005)

    Google Scholar 

  5. Jakob, W., Strack, S., Quinte, A., Bengel, G., Stucky, K.U., Süß, W.: Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing. Algorithms 6(2), 245–277 (2013)

    Article  Google Scholar 

  6. Singh, L., Singh, S.: A Survey of Workflow Scheduling Algorithms and Research Issues. International Journal of Computer Applications 74(15) (2013)

    Google Scholar 

  7. Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, USA (1989)

    MATH  Google Scholar 

  8. Sinnen, O.: Task scheduling for parallel systems, p. 108. Wiley-Interscience (2007)

    Google Scholar 

  9. Casanova, H., Legrand, A., Zagorodnov, D., Berman, F.: Heuristics for scheduling parameter sweep applications in grid environments. In: Proceedings of the 9th Heterogeneous Computing Workshop (HCW 2000), pp. 349–363. IEEE (2000)

    Google Scholar 

  10. Rahman, M., Hassan, R., Ranjan, R., Buyya, R.: Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurrency and Computation: Practice and Experience 25(13), 1816–1842 (2013)

    Article  Google Scholar 

  11. Xhafa, F., Alba, E., Dorronsoro, B., Duran, B., Abraham, A.: Efficient Batch Job Scheduling in Grids Using Cellular Memetic Algorithms. In: Xhafa, F., Abraham, A. (eds.) Metaheuristics for Scheduling in Distributed Computing Environments. SCI, vol. 146, pp. 273–299. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Liu, X., Chen, J., Wu, Z., Ni, Z., Yuan, D., Yang, Y.: Handling Recoverable Temporal Violations in Scientific Workflow Systems: A Workflow Rescheduling Based Strategy. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (2010)

    Google Scholar 

  13. https://confluence.pegasus.isi.edu/display/pegasus/MontageBenchmark

  14. Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.H., Vahi, K.: Characterization of scientific workflows. In: Third Workshop on Workflows in Support of Large-Scale Science, WORKS 2008, pp. 1–10. IEEE (November 2008)

    Google Scholar 

  15. Corchado, E., Wozniak, M., Abraham, A., de Carvalho, A.C.P.L.F., Snásel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)

    Article  Google Scholar 

  16. Calvo-Rolle, J.L., Corchado, E.: A Bio-inspired knowledge system for improving combined cycle plant control tuning. Neurocomputing 126, 95–105 (2014)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. ITMO University, Saint-Petersburg, Russian Federation, Russia

    Denis Nasonov, Nikolay Butakov, Marina Balakhontseva & Konstantin Knyazkov

  2. Netherlands Institute for Advanced Study in the Humanities and Social Sciences, Wassenaar, The Netherlands

    Alexander V. Boukhanovsky

Authors
  1. Denis Nasonov
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  2. Nikolay Butakov
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  3. Marina Balakhontseva
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  4. Konstantin Knyazkov
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  5. Alexander V. Boukhanovsky
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Corresponding author

Correspondence to Denis Nasonov .

Editor information

Editors and Affiliations

  1. DeustoTech Computing, University of Deusto, Bilbao, Spain

    José Gaviria de la Puerta

  2. DeustoTech Computing, University of Deusto, Bilbao, Spain

    Iván García Ferreira

  3. DeustoTech Computing, University of Deusto, Bilbao, Spain

    Pablo Garcia Bringas

  4. German Workforce ADL Partnership Laboratory, Waltershausen, Germany

    Fanny Klett

  5. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Washington, USA

    Ajith Abraham

  6. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, Brazil

    André C.P.L.F. de Carvalho

  7. Department of Civil Engineering Escuela Politénica Superior, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. Department of Civil Engineering Escuela Politénica Superior, University of Burgos, Burgos, Spain

    Bruno Baruque

  9. Departamento de Enxeñeria Industrial, Escuela Universitaria Politécnica, University of Salamanca, Salamanca, Spain and University of Coruña, La Coruña, Spain

    Héctor Quintián

  10. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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© 2014 Springer International Publishing Switzerland

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Nasonov, D., Butakov, N., Balakhontseva, M., Knyazkov, K., Boukhanovsky, A.V. (2014). Hybrid Evolutionary Workflow Scheduling Algorithm for Dynamic Heterogeneous Distributed Computational Environment. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-07995-0_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07994-3

  • Online ISBN: 978-3-319-07995-0

  • eBook Packages: EngineeringEngineering (R0)

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