Skip to main content
SpringerLink
Log in
Book cover

International Conference on Image Processing and Communications

IP&C 2017: Image Processing and Communications Challenges 9 pp 133–140Cite as

Acceleration of Genome Sequencing with Intelligent Cloud Brokers

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 681))

Abstract

Workflows from DNA sequencing applications have an extensive number of jobs which are reliant and that require parallel execution if high levels of performance are desired. In this work, a novel workflow broker based on expert systems is presented to accelerate workflows for DNA sequencing in cloud computing datacenters. The broker is based on the adaptation of Fuzzy Rule-Based Systems (FRBSs), which are inspired by Fuzzy Logic (FL) and rule-based systems, and as shown by simulation results, it is able to accelerate the processing of genome sequencing more efficiently than a wide range of scheduling strategies.

This is a preview of subscription content, log in via an institution.

References

  1. Chen, W., Deelman, E.: Workflowsim: a toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-Science (e-Science), pp. 1–8, October 2012

    Google Scholar 

  2. Cordon, O., Herrera, F., Villar, P.: Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Trans. Fuzzy Syst. 9(4), 667–674 (2001)

    Article  MATH  Google Scholar 

  3. Cotes-Ruiz, I.T., Prado, R.P., Munoz-Exposito, J.E., Garcia-Galan, S., Ruiz-Reyes, N.: Dynamic voltage frequency scaling simulator for real workflows energy-aware management in green cloud computing. PLoS ONE (2017). http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169803

  4. Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (2003)

    Google Scholar 

  5. Juve, G., Deelman, E., Vahi, K., Mehta, G., Berriman, B., Berman, B.P., Maechling, P.: Scientific workflow applications on Amazon EC2. In: 2009 5th IEEE International Conference on E-Science Workshops, pp. 59–66, December 2009

    Google Scholar 

  6. Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Fut. Gener. Comput. Syst. 29(3), 682–692 (2013). Special Section: Recent Developments in High Performance Computing and Security

    Article  Google Scholar 

  7. Prado, R.P., García-Galán, S., Muñoz Expósito, J.E., Yuste, A.J.: Knowledge acquisition in fuzzy rule based systems with particle swarm optimization. IEEE Trans. Fuzzy Syst. 18(6) (2010)

    Google Scholar 

  8. Prado, R.P., Garcia-Galan, S., Munoz-Exposito, J.E., Yuste, A.J., Bruque, S.: Genetic fuzzy rule-based meta-scheduler for grid computing. In: Fourth International Workshop on Genetic and Evolutionary Fuzzy Systems, GEFS 2010, pp. 51–56, March 2010

    Google Scholar 

  9. Prado, R.P., Garcia-Galan, S., Yuste, A., Munoz-Exposito, J.: Genetic fuzzy rule-based scheduling system for grid computing in virtual organizations. Soft Comput. - A Fusion of Foundations, Methodologies and Applications 15, 1255–1271 (2011)

    Google Scholar 

  10. Prado, R.P., García-Galán, S., Yuste, A.J., Muñoz Expósito, J.E.: A fuzzy rule-based meta-scheduler with evolutionary learning for grid computing. Eng. Appl. Artif. Intell. 23(7), 1072–1082 (2010)

    Article  Google Scholar 

  11. Prathibha, S.: Monitoring the performance analysis of executing workflow applications with different resource types in a cloud environment. In: 1st International Symposium on Big Data and Cloud Computing Challenges, ISBCC-2014, 27–28 March 2013. VIT University, Chennai (2013)

    Google Scholar 

  12. Team, P.: Scientific workflow Epigenomics. https://pegasus.isi.edu/application-showcase/epigenomics/

Download references

Acknowledgments

This work was financially supported by Research Projects TEC2015-67387-C4-2 and TEC2012-38142- C04-03.

Author information

Authors and Affiliations

  1. Telecommunication Engineering Department, University of Jaén, Linares, Spain

    Rocío Pérez de Prado, Sebastian García-Galán & Jose Enrique Muñoz-Expósito

  2. Institute of Telecommunications and Computer Science, UTP University of Science and Technology in Bydgoszcz, Bydgoszcz, Poland

    Adam Marchewka

Authors
  1. Rocío Pérez de Prado
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastian García-Galán
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jose Enrique Muñoz-Expósito
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adam Marchewka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rocío Pérez de Prado .

Editor information

Editors and Affiliations

  1. University of Science and Technology, UTP, Bydgoszcz, Poland

    Michał Choraś

  2. University of Science and Technology, UTP, Bydgoszcz, Poland

    Ryszard S. Choraś

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

de Prado, R.P., García-Galán, S., Muñoz-Expósito, J.E., Marchewka, A. (2018). Acceleration of Genome Sequencing with Intelligent Cloud Brokers. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications Challenges 9. IP&C 2017. Advances in Intelligent Systems and Computing, vol 681. Springer, Cham. https://doi.org/10.1007/978-3-319-68720-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68720-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68719-3

  • Online ISBN: 978-3-319-68720-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation