Skip to main content
SpringerLink
Log in

Optimization of Location Allocation of Web Services Using a Modified Non-dominated Sorting Genetic Algorithm

  • Conference paper
  • First Online:
Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

In recent years, Web services technology is becoming increasingly popular because of the convenience, low cost and capacity to be composed into high-level business processes. The service location-allocation problem for a Web service provider is critical and urgent, because some factors such as network latency can make serious effect on the quality of service (QoS). This paper presents a multi-objective optimization algorithm based on NSGA-II to solve the service location-allocation problem. A stimulated experiment is conducted using the WS-DREAM dataset. The results are compared with a single objective genetic algorithm (GA). It shows NSGA-II based algorithm can provide a set of best solutions that outperforms genetic algorithm.

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

Access this chapter

Log in via an institution
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

Institutional subscriptions

References

  1. Aboolian, R., Sun, Y., Koehler, G.J.: A locationallocation problem for a web services provider in a competitive market. Eur. J. Oper. Res. 194(1), 64–77 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  2. Caramia, M.: Multi-objective optimization. In: Multi-objective Management in Freight Logistics, pp. 11–36. Springer, London (2008)

    Google Scholar 

  3. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  4. Deb, K., Mohan, M., Mishra, S.: Evaluating the epsilon-domination based multi-objective evolutionary algorithm for a quick computation of pareto-optimal solutions. Evol. Comput. 13(4), 501–525 (2005)

    Article  Google Scholar 

  5. Deb, K., Sundar, J., Rao N, U.B., Chaudhuri, S.: Reference point based multi-objective optimization using evolutionary algorithms. In: International Journal of Computational Intelligence Research, pp. 635–642 (2006)

    Google Scholar 

  6. Desai, S., Bahadure, S., Kazi, F., Singh, N.: Article: Multi-objective constrained optimization using discrete mechanics and NSGA-II approach. Int. J. Comput. Appl. 57(20), 14–20 (2012). (full text available)

    Google Scholar 

  7. Ehrgott, M.: A discussion of scalarization techniques for multiple objective integer programming. Ann. Oper. Res. 147(1), 343–360 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  8. He, K., Fisher, A., Wang, L., Gember, A., Akella, A., Ristenpart, T.: Next stop, the cloud: Understanding modern web service deployment in ec2 and azure. In: Proceedings of the 2013 Conference on Internet Measurement Conference, IMC 2013, pp. 177–190. ACM (2013)

    Google Scholar 

  9. Huang, H., Ma, H., Zhang, M.: An enhanced genetic algorithm for web service location-allocation. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds.) DEXA 2014, Part II. LNCS, vol. 8645, pp. 223–230. Springer, Heidelberg (2014)

    Google Scholar 

  10. Huang, V.L., Suganthan, P.N., Liang, J.J.: Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems: Research articles. Int. J. Intell. Syst. 21(2), 209–226 (2006)

    Article  MATH  Google Scholar 

  11. Hwang, J., Park, S., Kong, I.Y.: An integer programming-based local search for large-scale maximal covering problems. Int. J. Comput. Sci. Eng. 3, 837–843 (2011)

    Google Scholar 

  12. Ishibuchi, H., Nojima, Y., Doi, T.: Comparison between single-objective and multi-objective genetic algorithms: Performance comparison and performance measures. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1143–1150 (2006)

    Google Scholar 

  13. Jamin, S., Jin, C., Kurc, A., Raz, D., Shavitt, Y.: Constrained mirror placement on the internet. In: INFOCOM 2001, Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, Proceedings, vol. 1, pp. 31–40. IEEE (2001)

    Google Scholar 

  14. Johansson, J.M.: On the impact of network latency on distributed systems design. Inf. Technol. Manag. 1(3), 183–194 (2000)

    Article  Google Scholar 

  15. Kanagarajan, D., Karthikeyan, R., Palanikumar, K., Davim, J.: Optimization of electrical discharge machining characteristics of wc/co composites using non-dominated sorting genetic algorithm (NSGA-II). Int. J. Adv. Manufact. Technol. 36(11–12), 1124–1132 (2008)

    Article  Google Scholar 

  16. Kemps-Snijders, M., Brouwer, M., Kunst, J.P., Visser, T.: Dynamic web service deployment in a cloud environment (2012)

    Google Scholar 

  17. Man, K.F., Tang, K.S., Kwong, S.: Genetic algorithms: concepts and applications. IEEE Trans. Ind. Electron. 43(5), 519–534 (1996)

    Article  Google Scholar 

  18. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  19. Morandat, F., Hill, B., Osvald, L., Vitek, J.: Evaluating the design of the R language. In: Noble, J. (ed.) ECOOP 2012. LNCS, vol. 7313, pp. 104–131. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  20. Ran, S.: A model for web services discovery with QoS. SIGecom Exch. 4(1), 1–10 (2003)

    Article  Google Scholar 

  21. Sun, Y., Koehler, G.J.: A location model for a web service intermediary. Decis. Support Syst. 42(1), 221–236 (2006)

    Article  Google Scholar 

  22. Vanrompay, Y., Rigole, P., Berbers, Y.: Genetic algorithm-based optimization of service composition and deployment. In: Proceedings of the 3rd International Workshop on Services Integration in Pervasive Environments, SIPE 2008, pp. 13–18. ACM (2008)

    Google Scholar 

  23. Xie, H., Zhang, M., Andreae, P., Johnson, M.: An analysis of multi-sampled issue and no-replacement tournament selection. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 1323–1330. ACM (2008)

    Google Scholar 

  24. Xue, B., Zhang, M., Browne, W.N.: Multi-objective particle swarm optimisation (pso) for feature selection. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, GECCO 2012, pp. 81–88. ACM (2012)

    Google Scholar 

  25. Xue, B., Zhang, M., Browne, W.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656–1671 (2013)

    Article  Google Scholar 

  26. Zhang, Y., Zheng, Z., Lyu, M.: Exploring latent features for memory-based QoS prediction in cloud computing. In: 2011 30th IEEE Symposium on Reliable Distributed Systems (SRDS), pp. 1–10 (2011)

    Google Scholar 

  27. Zheng, Z., Zhang, Y., Lyu, M.: Distributed QoS evaluation for real-world web services. In: 2010 IEEE International Conference on Web Services (ICWS), pp. 83–90 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Boxiong Tan, Hui Ma & Mengjie Zhang

Authors
  1. Boxiong Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mengjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Boxiong Tan .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Tan, B., Ma, H., Zhang, M. (2016). Optimization of Location Allocation of Web Services Using a Modified Non-dominated Sorting Genetic Algorithm. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28270-1_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation