Evolutionary Algorithms for Optimizing Cost and QoS on Cloud-based Content Distribution Networks


Content Distribution Networks (CDN) are key for providing worldwide services and content to end-users. In this work, we propose three multiobjective evolutionary algorithms for solving the problem of designing and optimizing cloud-based CDNs. We consider the objectives of minimizing the total cost of the infrastructure (including virtual machines, network, and storage) and the maximization of the quality-of-service provided to end-users. The proposed model considers a multi-tenant approach where a single cloud-based CDN is able to host multiple content providers using a resource sharing strategy. The proposed evolutionary algorithms address the offline problem of provisioning infrastructure resources while a greedy heuristic method is proposed for addressing the online problem of routing contents. The experimental evaluation of the proposed methods is performed over a set of realistic problem instances. Results indicate that the proposed approach is effective for designing and optimizing cloud-based CDNs reducing total costs by up to 10.3% while maintaining an adequate quality of service.

This is a preview of subscription content, access via your institution.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.


  1. 1

    Gao, G., Zhang, W., Wen, Y., Wang, Z., and Zhu, W. Towards cost-efficient video transcoding in media cloud: insights learned from user viewing patterns, IEEE Trans. Multimedia, 2015, vol. 17, no. 8, pp. 1286–1296.

    Article  Google Scholar 

  2. 2

    Hu, M., Luo, J., Wang, Y., and Veeravalli, B. Practical resource provisioning and caching with dynamic resilience for cloud-based content distribution networks, IEEE Trans. Parallel Distrib. Syst., 2014, vol. 25, no. 8, pp. 2169–2179.

    Article  Google Scholar 

  3. 3

    Jokhio, F., Ashraf, A., Lafond, S., and Lilius, J. A computation and storage trade-off strategy for cost-efficient video transcoding in the cloud, Proc. 39th Euromicro Conf. Series on Software Engineering and Advanced Applications, Santander, 2013, pp. 365–372.

  4. 4

    Xiao, W., Bao, W., Zhu, X., Wang, C., Chen, L., and Yang, L.T., Dynamic request redirection and resource provisioning for cloud-based video services under heterogeneous environment, IEEE Trans. Parallel Distrib. Syst., 2016, vol. 27, no. 7, pp. 1954–1967.

    Article  Google Scholar 

  5. 5

    Zhang, J., Huang, H., and Wang, X., Resource provision algorithms in cloud computing: a survey, J. Network Comput. Appl., 2016, vol. 64, pp. 23–42.

    Article  Google Scholar 

  6. 6

    Nesmachnow, S., Iturriaga, S., and Dorronsoro, B., Efficient heuristics for profit optimization of virtual cloud brokers, IEEE Comput. Intell. Mag., 2015, vol. 10, no. 1, pp. 33–43.

    Article  Google Scholar 

  7. 7

    Busari, M. and Williamson, C., ProWGen: a synthetic workload generation tool for simulation evaluation of web proxy caches, Comput. Networks, 2002, vol. 38, no. 6, pp. 779–794.

    Article  Google Scholar 

  8. 8

    Park, K. and Willinger, W., Self-Similar Network Traffic and Performance Evaluation, Chichester: Wiley, 2000.

    Google Scholar 

  9. 9

    Crandall, R., Crandall, W., and Chen, C., Principles of Supply Chain Management, Boca Raton: CRC Press, 2014.

    Google Scholar 

  10. 10

    Glover, F., Future paths for integer programming and links to artificial intelligence, Comput. Oper. Res., 1986, vol. 13, no. 5, pp. 533–549.

    MathSciNet  Article  Google Scholar 

  11. 11

    Nesmachnow, S., An overview of metaheuristics: accurate and efficient methods for optimization, Int. J. Metaheuristics, 2014, vol. 3, no. 4, pp. 320–347.

    Article  Google Scholar 

  12. 12

    Bäck, T., Fogel, D., and Michalewicz, Z., Handbook of Evolutionary Computation, Boca Raton, FL: CRC Press, 1997.

    Google Scholar 

  13. 13

    Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, Chichester: Wiley, 2001.

    Google Scholar 

  14. 14

    Coello, C., Lamont, G., and van Veldhuizen, D., Evolutionary Algorithms for Solving Multi-Objective Problems, New York: Springer-Verlag, 2007.

    Google Scholar 

  15. 15

    Nesmachnow, S., Rossit, D., and Toutouh, J., Comparison of multiobjective evolutionary algorithms for prioritized urban waste collection in Montevideo, Uruguay, Electron. Not. Discrete Math., 2018, vol. 69, pp. 93–100.

    Article  Google Scholar 

  16. 16

    Péres, M., Ruiz, G., Nesmachnow, S., and Olivera, A.C., Multiobjective evolutionary optimization of traffic flow and pollution in Montevideo, Uruguay, Appl. Soft Comput., 2018, vol. 70, pp. 472–485.

    Article  Google Scholar 

  17. 17

    Massobrio, R., Toutouh, J., Nesmachnow, S., and Alba, E., Infrastructure deployment in vehicular communication networks using a parallel multiobjective evolutionary algorithm, Int. J. Intell. Syst., 2017, vol. 32, no. 8, pp. 801–829.

    Article  Google Scholar 

  18. 18

    Nebro, A., Alba, E., Molina, G., Chicano, F., Luna, F., and Durillo, J., Optimal antenna placement using a new multi-objective CHC algorithm, Proc. 9th Annu. Conf. on Genetic and Evolutionary Computation, London, 2007, pp. 876–883.

  19. 19

    Eshelman, L., The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination, in Foundations of Genetics Algorithms, San Francisco, CA: Morgan Kaufmann, 1991, pp. 265–283.

    Google Scholar 

  20. 20

    Beume, N., Naujoks, B., and Emmerich, M., SMS-EMOA: multiobjective selection based on dominated hypervolume, Eur. J. Oper. Res., 2007, vol. 181, no. 3, pp. 1653–1669.

    Article  Google Scholar 

  21. 21

    Chen, F., Guo, K., Lin, J., and Porta, T.L., Intra-cloud lightning: building CDNs in the cloud, Proc. IEEE INFOCOM, Orlando, 2012, pp. 433–441.

  22. 22

    Papagianni, C., Leivadeas, A., and Papavassiliou, S., A cloud-oriented content delivery network paradigm: modeling and assessment, IEEE Trans. Dependable Secure Comput., 2013, vol. 10, no. 5, pp. 287–300.

    Article  Google Scholar 

  23. 23

    Hollander, M., Wolfe, D., and Chicken, E., Nonparametric Statistical Methods, Chichester: Wiley, 2013.

    Google Scholar 

Download references


The work of S. Iturriaga and S. Nesmachnow is partially supported by ANII and PEDECIBA, Uruguay. The work of B. Dorronsoro is partially supported by the Ministry of Science, Innovation and Universities and ERDF with the fundings of the project RTI2018-100754-B-I00 (iSUN).

Author information



Corresponding authors

Correspondence to S. Iturriaga or S. Nesmachnow or G. Goñi or B. Dorronsoro or A. Tchernykh.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Iturriaga, S., Nesmachnow, S., Goñi, G. et al. Evolutionary Algorithms for Optimizing Cost and QoS on Cloud-based Content Distribution Networks. Program Comput Soft 45, 544–556 (2019). https://doi.org/10.1134/S0361768819080127

Download citation