KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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A New Multi-objective Evolutionary Algorithm for Inter-Cloud Service Composition

  • Liu, Li (School of Automation and Electrical Engineering University of Science and Technology Beijing) ;
  • Gu, Shuxian (School of Automation and Electrical Engineering University of Science and Technology Beijing) ;
  • Fu, Dongmei (School of Automation and Electrical Engineering University of Science and Technology Beijing) ;
  • Zhang, Miao (School of Information and Electronics, Beijing Institute of Technology) ;
  • Buyya, Rajkumar (The University of Melbourne)
  • Received : 2017.06.09
  • Accepted : 2017.09.15
  • Published : 2018.01.31
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Abstract

Service composition in the Inter-Cloud raises new challenges that are caused by the different Quality of Service (QoS) requirements of the users, which are served by different geo-distributed Cloud providers. This paper aims to explore how to select and compose such services while considering how to reach high efficiency on cost and response time, low network latency, and high reliability across multiple Cloud providers. A new hybrid multi-objective evolutionary algorithm to perform the above task called LS-NSGA-II-DE is proposed, in which the differential evolution (DE) algorithm uses the adaptive mutation operator and crossover operator to replace the those of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to get the better convergence and diversity. At the same time, a Local Search (LS) method is performed for the Non-dominated solution set F{1} in each generation to improve the distribution of the F{1}. The simulation results show that our proposed algorithm performs well in terms of the solution distribution and convergence, and in addition, the optimality ability and scalability are better compared with those of the other algorithms.

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References

  1. C. Qu, R. N. Calheiros, R. Buyya, "A reliable and cost-efficient auto-scaling system for web applications using heterogeneous spot instances," Journal of Network & Computer Applications, vol.65, pp.167-180, 2016.
  2. N. Grozev, R. Buyya., "Inter-Cloud architectures and application brokering: taxonomy and survey," Software: Practice and Experience, vol. 44, pp. 369-390, March 2014. https://doi.org/10.1002/spe.2168
  3. J. Amin., S. Elankovan and O. Zalinda, "Cloud computing service composition: A systematic literature review," Expert Systems with Applications: An International Journal, vol.41, no.8, pp.3809-3824, 2014. https://doi.org/10.1016/j.eswa.2013.12.017
  4. K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist multi objective genetic algorithm: NSGA-II," IEEE Trans. Evol. Compute., vol. 6, no. 2, pp. 182-197, Apr. 2002. https://doi.org/10.1109/4235.996017
  5. R. Storn, K. Price, "Differential evolutional-A simple and efficient heuristic for global optimization over continuous spaces," Journal of Global Optimization, vol. 11, no. 4, pp.341-359, 1997. https://doi.org/10.1023/A:1008202821328
  6. Z. Ye, X. Zhou and A. Bouguettaya, "Genetic Algorithm Based QoS-Aware Service Compositions in Cloud Computing," in Proc. of 16th International Conference on DASFAA, pp. 321-334, 2011.
  7. M. Zhang, L. Liu, S. Liu, "Genetic Algorithm Based QoS-aware Service Composition in Multi-Cloud," in Proc. IEEE Conference on Collaboration & Internet Computting, pp.113-118, 2015.
  8. Q. Yu, L. Chen, and B. Li, "Ant colony optimization applied to web service compositions in Cloud computing," Computers & Electrical Engineering, vol. 41, pp. 18-27, 2015. https://doi.org/10.1016/j.compeleceng.2014.12.004
  9. M. Shojafar, N. Cordeschi, D. Amendola and et al, "Energy-saving adaptive computing and traffic engineering for real-time-service data centers," in Proc. of IEEE International Conference on Communication Workshop. IEEE, pp.1800-1806, 2015.
  10. M. Shojafar, N. Cordeschi and et al., "Energy-efficient Adaptive Resource Management for Real-time Vehicular Cloud Service," IEEE Transactions on Cloud Computing, pp99:1-1, 2016.
  11. M. Shojafar, S. Javanmardi, S. Abolfazli, et al, "FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method," Cluster Computing, 2015, vol. 18, no. 2, pp.829-844. https://doi.org/10.1007/s10586-014-0420-x
  12. Y. Yao, "A Rule-Based Web Service Composition Approach," in Proc. of International Conference on Autonomic and Autonomous Systems (ICAS), pp.150-155, 2010.
  13. J. Cao , X. Sun, and et al, "Efficient Multi-objective Services Selection Algorithm Based on Particle Swarm Optimization," in Proc. of IEEE Asia-pacific Services Computing Conference, pp:603-608, 2010.
  14. H. Wada, J. Suzuki, and et al, "E3: A Multi objective Optimization Framework for SLA-Aware Service Composition," IEEE Transactions on Services Computing, vol. 5, no. 3, pp.358-371, 2012. https://doi.org/10.1109/TSC.2011.6
  15. J. Feng, L. Kong, "A Fuzzy Multi-objective Genetic Algorithm for QoS-based Cloud Service Composition," in Proc. of International Conference on Semantics, pp. 202-206, 2015.
  16. L. Liu, M. Zhang, "Multi-objective Optimization Model with AHP Decision-making for Cloud Service Composition," KSII Transactions on Internet & Information Systems, vol. 9, no. 9, pp. 3293-3311, 2015. https://doi.org/10.3837/tiis.2015.09.002
  17. S. K. Garg, A. N. Toosi and et al "SLA-based Virtual Machine Management for Heterogeneous Workloads in a Cloud Datacenter," Journal of Network and Computer Applications, vol. 45, no. 10, pp. 108-120, 2014. https://doi.org/10.1016/j.jnca.2014.07.030
  18. G. Canfora M. Di Penta, and et al, "An approach for QoS-aware service composition based on genetic algorithms," in Proc. of Conference on Genetic and Evolutionary Computation, pp. 1069-1075, 2005.
  19. R. Storn, "On the usage of differential evolution for function optimization," Fuzzy Information Processing Society, pp. 519-523, 1996.
  20. Y. Zhou, C. Zhang, et al, "Multi-objective service compositon optimization using differential evolution," in Proc. of 11t International Conference on Natural Computation, pp. 233-238, 2015.
  21. S. Das, A. Abraham, et al, A. Konar, "Differential evolution using a neighborhood based mutation operator," IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp.526-553, 2009. https://doi.org/10.1109/TEVC.2008.2009457
  22. S. Das, A. Konar, and et al , "Two Improved Differential Evolution Schemes for Faster Global Search," in Proc. of Genetic & Evolutionary Computation Conference, pp. 991- 998, 2015.
  23. N. Noman, H. Lba, "Enhancing differential evolution performance with local search for high dimensional function optimization" in Proc. of Genetic & Evolutionary Computation Conference, pp. 967-974, 2015.
  24. K. Deb, A. Sinha, S. Kukkonen, "Multi-Objective Test Problems, Linkages, and Evolutionary Methodologies," in Proc. of Genetic & Evolutionary Computation Conference, pp. 1141-1148, 2016.
  25. Q. Zhang, et al, "MOEA/D: A multi objective evolutionary algorithm based on decomposition," IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712-731, 2008. https://doi.org/10.1109/TEVC.2007.892759
  26. C. A. C. Coello, G.T. Pulido, and M.S Lechuga, "Handling multiple objectives with particle swarm optimization," IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256-279, 2004. https://doi.org/10.1109/TEVC.2004.826067
  27. W. Dong, L. Kang, W. Zhang, "Opposition-based particle swarm optimization with adaptive mutation strategy," Soft Computing, pp. 1-10, 2016.
  28. E. Zitzler, L. Thiele, "Multi objective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach," IEEE Transactions on Evolutionary Computation, vol. 3, no.4, pp. 257-271, 2000.
  29. K. Deb, "Multi-objective Optimization Using Evolutionary Algorithms: An Introduction," John Wiley & Sons, vol. 2, no. 3, pp. 509, 2011.
  30. http://www.uoguelph.ca/-qmahmoud/qws/.
  31. M.A. Abido, "Multi objective Evolutionary Algorithms for Electric Power Dispatch Problem," IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp.315-329.

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