Skip to main content
Log in

A cost-effective cloud leasing policy for live streaming applications

  • Computer Science
  • Published:
Wuhan University Journal of Natural Sciences

Abstract

The dynamics of the globalized multimedia sources and request demands, which requires high computations and bandwidths, makes the IT infrastructure a challenge for live streaming applications. Migrating the system to a geo-distributed cloud and leasing servers is an ideal alternative for supporting large-scale live streaming applications with dynamic contents and demands. The new challenge of multimedia live streaming applications in a geo-distributed cloud is how to efficiently arrange and migrate services among different cloud sites to guarantee the distribute users’ experience at modest costs. This paper first investigates cloud leasing policies for live streaming applications and finds that there is no detailed algorithm to help live streaming applications arrange and migrate services among different cloud sites. Then, we present a quality of service (QoS) guarantee cost-effective cloud leasing policy for live streaming applications. Meanwhile, we design a genetic algorithm (GA) to deal with the leasing policy among cloud sites of diverse lease prices. Experimental results confirm the effectiveness of the proposed model and the efficiency of the involved GA.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Chen F, Zhang C, Wang F, et al. Cloud-Assisted live streaming for crowdsourced multimedia content [J]. IEEE Transactions on Multimedia, 2015, 17(9): 1–6.

    Article  Google Scholar 

  2. Hajjat M, Sun X, Sung Y W E, et al. Cloudward bound: Planning for beneficial migration of enterprise applications to the cloud[J]. ACM Sigcomm Computer Communication Review, 2010, 40(4): 243–254.

    Article  Google Scholar 

  3. Sharma U, Shenoy P, Sahu S, et al. A cost-aware elasticity provisioning system for the cloud[C]// Proceedings of the International Conference on Distributed Computing Systems. Washington D C: IEEE, 2011: 559–570.

    Google Scholar 

  4. Zhang H, Jiang G, Yoshihira K, et al. Intelligent workload factoring for a hybrid cloud computing model [C]// Proceedings of the Congress on Services-I. Washington D C: IEEE, 2009: 701–708.

    Google Scholar 

  5. Adhikari V K, Guo Y, Hao F, et al. Unreeling netflix: Understanding and improving multi-CDN movie delivery[C]// Proceedings of the INFOCOM, 2012. Washington D C: IEEE, 2012: 1620–1628.

    Chapter  Google Scholar 

  6. Aggarwal V, Gopalakrishnan V, Jana R, et al. Optimizing cloud resources for delivering IPTV services through virtualization [C] // Fourth International Conference on Communication Systems and Networks. Washington D C: IEEE, 2012: 1–10.

    Google Scholar 

  7. Wu Y, Wu C, Li B, et al. Scaling social media applications into geo-distributed clouds [C]// IEEE INFOCOM. Washington D C: IEEE, 2012: 684–692.

    Google Scholar 

  8. Feng Y, Li B, Li B. Bargaining towards maximized resource utilization in video streaming datacenters[C]//INFOCOM, 2012 Proceedings IEEE. Washington D C: IEEE, 2012: 1134–1142.

    Chapter  Google Scholar 

  9. Amazon. Amazon Elastic Compute Cloud[EB/OL]. [2016-12-20]. http://aws.amazon.com/ec2/.

  10. Amazon. Amazon Simple Storage Service[EB/OL]. [2016-12-20]. http://aws.amazon.com/s3/.

  11. Wang Z, Sun L, Wu C, et al. Joint online transcoding and geo-distributed delivery for dynamic adaptive streaming[C]// Proceedings of the INFOCOM. Washington D C: IEEE, 2014: 91–99.

    Google Scholar 

  12. Holland J H. Erratum: Genetic algorithms and the optimal allocation of trials[J]. Siam Journal on Computing, 1973, 2(2):88–105.

    Article  Google Scholar 

  13. Wei H, Sung-Kwun O, Witold P. Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs) [J]. Neural Networks, 2014, 60(C): 166–181.

    Article  Google Scholar 

  14. Huang W, Wang J. The shortest path problem on a time-dependent network with mixed uncertainty of randomness and fuzziness [J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(11): 3194–3204.

    Article  Google Scholar 

  15. Huang W, Oh S K, Guo Z, et al. A space search optimization algorithm with accelerated convergence strategies[J]. Applied Soft Computing, 2013, 13(12): 4659–4675.

    Article  Google Scholar 

  16. Mezmaz M, Melab N, Kessaci Y, et al. A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems[J]. Journal of Parallel & Distributed Computing, 2011, 71(11): 1497–1508.

    Article  Google Scholar 

  17. Wu Z, Liu X, Ni Z, et al. A market-oriented hierarchical scheduling strategy in cloud workflow systems[J]. The Journal of Supercomputing, 2013, 63(1): 256–293.

    Article  Google Scholar 

  18. Zhu Z, Zhang G, Li M, et al. Evolutionary multi-objective workflow scheduling in cloud[J]. IEEE Transactions on Parallel & Distributed Systems, 2016, 27(5): 1344–1357.

    Article  Google Scholar 

  19. Gao Y, Guan H, Qi Z, et al. A multi-objective ant colony system algorithm for virtual machine placement in cloud computing[J]. Journal of Computer & System Sciences, 2013, 79(8): 1230–1242.

    Article  Google Scholar 

  20. Zhang M, Liu L, Liu S. Genetic algorithm based QoS-aware service composition in multi-cloud[C]// Proceedings of the IEEE Conference on Collaboration and Internet Computing. Washington D C: IEEE, 2015: 113–118.

    Google Scholar 

  21. Ye Z, Zhou X, Bouguettaya A. Genetic algorithm based QoS-aware service compositions in cloud computing[C]// Proceedings of the International Conference on Database Systems for Advanced Applications. Berlin: Springer-Verlag, 2011: 321–334.

    Chapter  Google Scholar 

  22. Gen M. Genetic Algorithms and Their Applications [M]. London: Springer-Verlag, 2006.

    Book  Google Scholar 

  23. Goldberg D E, Holland J H. Genetic algorithms and machine learning [J]. Machine Learning, 1988, 3(2): 95–99.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lixin Ding.

Additional information

Foundation item: Supported by the National Key Technology R&D Program during the Twelfth Five-Year Plan Period (2015BAK27B02)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Q., Ding, L. & Yang, L. A cost-effective cloud leasing policy for live streaming applications. Wuhan Univ. J. Nat. Sci. 22, 477–481 (2017). https://doi.org/10.1007/s11859-017-1276-8

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11859-017-1276-8

Key words

CLC number

Navigation