Skip to main content

Sensitivity Analysis of Checkpointing Strategies for Multimemetic Algorithms on Unstable Complex Networks

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9374))

Abstract

The use of volatile decentralized computational platforms such as, e.g., peer-to-peer networks, is becoming an increasingly popular option to gain access to vast computing resources. Making an effective use of these resources requires algorithms adapted to such a changing environment, being resilient to resource volatility. We consider the use of a variant of evolutionary algorithms endowed with a classical fault-tolerance technique, namely the creation of checkpoints in a safe external storage. We analyze the sensitivity of this approach on different kind of networks (scale-free and small-world) and under different volatility scenarios. We observe that while this strategy is robust under low volatility conditions, in cases of severe volatility performance degrades sharply unless a high checkpoint frequency is used. This suggest that other fault-tolerance strategies are required in these situations.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley-Interscience, New York (2005)

    Book  Google Scholar 

  2. Alba, E., Troya, J.M.: Influence of the migration policy in parallel distributed GAs with structured and panmictic populations. Appl. Intell. 12(3), 163–181 (2000)

    Article  Google Scholar 

  3. Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47–97 (2002)

    Article  MATH  Google Scholar 

  4. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  5. Barmpoutis, D., Murray, R.M.: Networks with the smallest average distance and the largest average clustering. arXiv 1007.4031 [q-bio] (2010)

  6. Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell (2000)

    MATH  Google Scholar 

  7. Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Whitley, L.D. (ed.) Second Workshop on Foundations of Genetic Algorithms, pp. 93–108. Morgan Kaufmann, Vail (1993)

    Google Scholar 

  8. Goldberg, D.E., Deb, K., Horn, J.: Massive multimodality, deception, and genetic algorithms. In: Parallel Problem Solving from Nature - PPSN II, pp. 37–48. Elsevier, Brussels (1992)

    Google Scholar 

  9. Hidalgo, J.I., Lanchares, J., Fernández de Vega, F., Lombraña, D.: Is the Island model fault tolerant? In: Proceedings of the 9th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO 2007, pp. 2737–2744. ACM, New York (2007)

    Google Scholar 

  10. Laredo, J.J.L., Bouvry, P., González, D.L., de Vega, F.F., Arenas, M.G., Merelo, J.J., Fernandes, C.M.: Designing robust volunteer-based evolutionary algorithms. Genet. Program Evolvable Mach. 15(3), 221–244 (2014)

    Article  Google Scholar 

  11. Krasnogor, N., Blackburne, B.P., Burke, E.K., Hirst, J.D.: Multimeme algorithms for protein structure prediction. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 769–778. Springer, Heidelberg (2002)

    Google Scholar 

  12. Lombraña González, D., Jiménez Laredo, J.L., Fernández de Vega, F., Merelo Guervós, J.J.: Characterizing fault-tolerance of genetic algorithms in desktop grid systems. In: Cowling, P., Merz, P. (eds.) EvoCOP 2010. LNCS, vol. 6022, pp. 131–142. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  13. Mihaljević, M.J., Imai, H.: Security issues of cloud computing and an encryption approach. In: Despotović-Zrakić, M., Milutinović, V., Belić, A. (eds.) Handbook of Research on High Performance and Cloud Computing in Scientific Research and Education, pp. 388–408. IGI Global, Hershey (2014)

    Chapter  Google Scholar 

  14. Milojičić, D.S., Kalogeraki, V., Lukose, R., Nagaraja, K., Pruyne, J., Richard, B., Rollins, S., Xu, Z.: Peer-to-peer computing. Technical report, HPL-2002-57, Hewlett-Packard Labs (2002)

    Google Scholar 

  15. Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms, Studies in Computational Intelligence, vol. 379. Springer, Heidelberg (2012)

    Book  Google Scholar 

  16. Nogueras, R., Cotta, C.: Studying fault-tolerance in island-based evolutionary and multimemetic algorithms. J. Grid Comput. (2015). doi:10.1007/s10723-014-9315-6

  17. Nogueras, R., Cotta, C.: An analysis of migration strategies in Island-based multimemetic algorithms. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 731–740. Springer, Heidelberg (2014)

    Google Scholar 

  18. Nogueras, R., Cotta, C.: On meme self-adaptation in spatially-structured multimemetic algorithms. In: Dimov, I., Fidanova, S., Lirkov, I. (eds.) NMA 2014. LNCS, vol. 8962, pp. 70–77. Springer, Heidelberg (2015)

    Google Scholar 

  19. Nogueras, R., Cotta, C.: Studying self-balancing strategies in island-based multimemetic algorithms. J. Comput. Appl. Math. 293, 180–191 (2016). doi:10.1016/j.cam.2015.03.047

    Article  MathSciNet  Google Scholar 

  20. Ong, Y.S., Lim, M.H., Chen, X.: Memetic computation-past, present and future. IEEE Comput. Intell. Mag. 5(2), 24–31 (2010)

    Article  Google Scholar 

  21. Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 36(1), 141–152 (2006)

    Article  Google Scholar 

  22. Reichhardt, T.: It’s sink or swim as a tidal wave of data approaches. Nature 399(6736), 517–520 (1999)

    Article  Google Scholar 

  23. Sarmenta, L.F.: Bayanihan: web-based volunteer computing using java. In: Masunaga, Y., Katayama, T., Tsukamoto, M. (eds.) Worldwide Computing and Its Applications - WWCA 1998. Lecture Notes in Computer Science, vol. 1368, pp. 444–461. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  24. Schaefer, R., Byrski, A., Smołka, M.: The Island model as a Markov dynamic system. Int. J. Appl. Math. Comput. Sci. 22(4), 971–984 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  25. Skolicki, Z., Jong, K.D.: The influence of migration sizes and intervals on Island models. In: Genetic and Evolutionary Computation Conference 2005, pp. 1295–1302. ACM, New York (2005)

    Google Scholar 

  26. Smith, J.E.: Self-adaptation in evolutionary algorithms for combinatorial optimisation. In: Cotta, C., Sevaux, M., Sörensen, K. (eds.) Adaptive and Multilevel Metaheuristics, Studies in Computational Intelligence, vol. 136, pp. 31–57. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  27. Smith, J.: Self-adaptative and coevolving memetic algorithms. In: Neri, F. (ed.) Handbook of Memetic Algorithms. SCI, vol. 379, pp. 199–220. Springer, Heidelberg (2011)

    Google Scholar 

  28. Snijders, C., Matzat, U., Reips, U.D.: ‘Big Data’: big gaps of knowledge in the field of internet. Int. J. Internet Sci. 7, 1–5 (2012)

    Google Scholar 

  29. Tanese, R.: Distributed genetic algorithms. In: 3rd International Conference on Genetic Algorithms, pp. 434–439. Morgan Kaufmann Publishers Inc., San Francisco (1989)

    Google Scholar 

  30. Watson, R.A., Hornby, G.S., Pollack, J.B.: Modeling building-block interdependency. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 97–106. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the MINECO project EphemeCH (TIN2014-56494-C4-1-P), by the Junta de Andalucía project DNEMESIS (P10-TIC-6083) and by the Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos Cotta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Nogueras, R., Cotta, C. (2015). Sensitivity Analysis of Checkpointing Strategies for Multimemetic Algorithms on Unstable Complex Networks. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2015. Lecture Notes in Computer Science(), vol 9374. Springer, Cham. https://doi.org/10.1007/978-3-319-26520-9_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26520-9_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26519-3

  • Online ISBN: 978-3-319-26520-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics