Cluster Computing

, Volume 22, Issue 1, pp 223–239 | Cite as

Resource allocation in the cloud for video-on-demand applications using multiple cloud service providers

  • P. Muthi ReddyEmail author
  • Ansaf Ahmed
  • S. H. Manjula
  • K. R. Venugopal


Video-on-demand (VoD) applications have become extensively used nowadays. YouTube is one of the most extensively used VoD application. These applications are used for various purposes like entertainment, education, media, etc., of all age groups. Earlier, these applications were supported by private data centers and application servers. Sufficient infrastructure had to be bought and maintained, to support the demand even during unexpected peak times. This approach caused huge loss of resources when the demand is normal as a large portion of the resources remained idle. To overcome this, VoD application providers moved to the cloud, to host their video content’s. This approach reduced the wastage of resources and the maintenance cost of the VoD application provider. The problem is to determine the number of resources to handle the demand while maintaining QoS for every instance. We have designed two algorithms in this paper, namely the multiple cloud resource allocation (MCRA) algorithm and the hybrid MCRA algorithm. Most of the cloud service providers (CSPs) basically provide two types of resource allocation schemes: (i) the reservation scheme and (ii) the on-demand scheme. The reservation scheme provides time-based tariff prices, where the discount is provided for the resources depending on their quantity and reservation time. This scheme is used in the MCRA algorithm to reduce the cost of the VoD application provider. In Hybrid MCRA algorithm both the reservation scheme and on-demand scheme are implemented, to overcome the drawbacks of the MCRA algorithm which are under-subscription and over-subscription. We have analyzed both the algorithms in terms of cost and allocation of resources. These algorithms can help allocate resources in of cloud for VoD applications in a cost-effective way and at the same time not compromise on the QoS of the video content.


Cloud service provider Hybrid Resource allocation Reservation scheme Time-discount tariffs Video-on-demand 


  1. 1.
    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)CrossRefGoogle Scholar
  2. 2.
    Liu, Y., Guo, Y., Liang, C.: A survey on peer-to-peer video streaming systems. Peer-to-Peer Netw. Appl. 1(1), 18–28 (2008)CrossRefGoogle Scholar
  3. 3.
    Muthi Reddy, P., Manjula, S.H., Venugopal, K.R.: Secure data sharing in cloud computing: a comprehensive review. Int. J. Comput. (IJC) 25(1), 80–115 (2017)Google Scholar
  4. 4.
    Iqbal, W., Dailey, M.N., Carrera, D.: Unsupervised learning of dynamic resource provisioning policies for cloud-hosted multitier web applications. IEEE Syst. J. 10(4), 1435–1446 (2016)CrossRefGoogle Scholar
  5. 5.
    Marinescu, D.C., Paya, A., Morrison, J.P.: A cloud reservation system for big data applications. IEEE Trans. Parallel Distrib. Syst. 28(3), 606–618 (2017)CrossRefGoogle Scholar
  6. 6.
    Alasaad, A., Shafiee, K., Behairy, H.M., Leung, V.C.: Innovative schemes for resource allocation in the cloud for media streaming applications. IEEE Trans. Parallel Distrib. Syst. 26(4), 1021–1033 (2015)CrossRefGoogle Scholar
  7. 7.
    Armstrong, M., Vickers, J.: Competitive non-linear pricing and bundling. Rev. Econ. Studies 77(1), 30–60 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Peichang, S., Huaimin, W., Gang, Y., Fengshun, L., Tianzuo, W.: Prediction-based federated management of multi-scale resources in cloud. AISS: Adv. Inf. Sci. Serv. Sci. 4(6), 324–334 (2012)CrossRefGoogle Scholar
  9. 9.
    Gürsun, G., Crovella, M., and Matta, I.: Describing and forecasting video access patterns. In: INFOCOM, 2011 Proceedings IEEE. IEEE, pp. 16–20 (2011)Google Scholar
  10. 10.
    Niu, D., Xu, H., Li, B., and Zhao, S.: Quality-assured cloud bandwidth auto-scaling for video-on-demand applications. In: INFOCOM, 2012 Proceedings IEEE. IEEE, pp. 460–468 (2012)Google Scholar
  11. 11.
    Muthireddy, P., Manjula, S.H., and Venugopal, K.R.: Energy optimization for virtual machines scheduling in cloud data centers. In: IEEEFORUM, Proceedings of IEEEForum International Conference on Computer Science, Industrial Electronics (ICCSIE), pp. 26–30 (2018)Google Scholar
  12. 12.
    Mashayekhy, L., Nejad, M.M., Grosu, D., Vasilakos, A.V.: An online mechanism for resource allocation and pricing in clouds. IEEE Trans. Comput. 65(4), 1172–1184 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Wang, W., Jiang, Y., Wu, W.: Multiagent-based resource allocation for energy minimization in cloud computing systems. IEEE Trans. Syst. Man Cybern.: Syst. 47(2), 205–220 (2017)Google Scholar
  14. 14.
    Adam, O.Y., Lee, Y.C., Zomaya, A.Y.: Constructing performance-predictable clusters with performance-varying resources of clouds. IEEE Trans. Comput. 65(9), 2709–2724 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Liu, G., Shen, H., Wang, H.: Deadline guaranteed service for multi-tenant cloud storage. IEEE Trans. Parallel Distrib. Syst. 27(10), 2851–2865 (2016)CrossRefGoogle Scholar
  16. 16.
    Lim, N., Majumdar, S., Ashwood-Smith, P.: Mrcp-rm: a technique for resource allocation and scheduling of mapreduce jobs with deadlines. IEEE Trans. Parallel Distrib. Syst. 28(5), 1375–1389 (2017)CrossRefGoogle Scholar
  17. 17.
    Xiao, Z., Song, W., Chen, Q.: Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 24(6), 1107–1117 (2013)CrossRefGoogle Scholar
  18. 18.
    Shi, L., Zhang, Z., Robertazzi, T.: Energy-aware scheduling of embarrassingly parallel jobs and resource allocation in cloud. IEEE Trans. Parallel Distrib. Syst. 28(6), 1607–1620 (2017)CrossRefGoogle Scholar
  19. 19.
    Zhu, X., Wang, J., Guo, H., Zhu, D., Yang, L.T., Liu, L.: Fault-tolerant scheduling for real-time scientific workflows with elastic resource provisioning in virtualized clouds. IEEE Trans. Parallel Distrib. Syst. 27(12), 3501–3517 (2016)CrossRefGoogle Scholar
  20. 20.
    Zhao, Y., Jiang, H., Zhou, K., Huang, Z., Huang, P.: Dream-(l) g: a distributed grouping-based algorithm for resource assignment for bandwidth-intensive applications in the cloud. IEEE Trans. Parallel Distrib. Syst. 27(12), 3469–3484 (2016)CrossRefGoogle Scholar
  21. 21.
    Muthi Reddy, P., Rekha Rangappa Dasar, Tanuja, R., Manjula, S.H., and Venugopal, K.R.: Forward secrecy in authentic and anonymous cloud with time optimization. In: Proceedings of the IEEE Fifteenth International Conference on Wireless and Optical Communications Networks (WOCN 2018) 7(2): 10036–10043 (2018). ISBN:978-1-5386-4798-1,Google Scholar
  22. 22.
    Guan, X., Wan, X., Choi, B.-Y., Song, S., Zhu, J.: Application oriented dynamic resource allocation for data centers using docker containers. IEEE Commun. Lett. 21(3), 504–507 (2017)CrossRefGoogle Scholar
  23. 23.
    Zhang, X., Huang, Z., Wu, C., Li, Z., Lau, E.: Online auctions in iaas clouds: welfare and profit maximization with server costs. ACM SIGMETRICS Perform. Eval. Rev. 43(1), 3–15 (2015)CrossRefGoogle Scholar
  24. 24.
    Wang, Z., Hayat, M.M., Ghani, N., Shaban, K.B.: Optimizing cloud-service performance: efficient resource provisioning via optimal workload allocation. IEEE Trans. Parallel Distrib. Syst. 28(6), 1689–1702 (2017)CrossRefGoogle Scholar
  25. 25.
    Simão, J., Veiga, L.: Partial utility-driven scheduling for flexible sla and pricing arbitration in clouds. IEEE Trans. Cloud Comput. 4(4), 467–480 (2016)CrossRefGoogle Scholar
  26. 26.
    Wang, H., Kang, Z., Wang, L.: Performance-aware cloud resource allocation via fitness-enabled auction. IEEE Trans. Parallel Distrib. Syst. 27(4), 1160–1173 (2016)CrossRefGoogle Scholar
  27. 27.
    Pillai, P.S., Rao, S.: Resource allocation in cloud computing using the uncertainty principle of game theory. IEEE Syst. J. 10(2), 637–648 (2016)CrossRefGoogle Scholar
  28. 28.
    Hwang, E., Kim, S., Yoo, T.-K., Kim, J.-S., Hwang, S., Choi, Y.-R.: Resource allocation policies for loosely coupled applications in heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 27(8), 2349–2362 (2016)CrossRefGoogle Scholar
  29. 29.
    Muthi Reddy, P., Rekha Rangappa Dasar, Tanuja, R., Manjula, S.H., Venugopal, K.R.: Coirs: cost optimized identity based ring signature with forward secrecy in cloud computing. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 16(3), 71–79 (2018)Google Scholar
  30. 30.
    Graiet, M., Mammar, A., Boubaker, S., Gaaloul, W.: Towards correct cloud resource allocation in business processes. IEEE Trans. Serv. Comput. 10(1), 23–36 (2017)CrossRefGoogle Scholar
  31. 31.
    Faiz, M., Anuar, N.B., Wahab, A.W.A., Shamshirband, S., Chronopoulos, A.T.: Source camera identification: a distributed computing approach using hadoop. J. Cloud Comput. 6(1), 18 (2017)CrossRefGoogle Scholar
  32. 32.
    Shamshirband, S., Anuar, N.B., Kiah, M.L.M., Patel, A.: An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. Eng. Appl. Artif. Intell. 26(9), 2105–2127 (2013)CrossRefGoogle Scholar
  33. 33.
    Bashirpour, H., Bashirpour, S., Shamshirband, S., Chronopoulos, A.T.: An improved digital signature protocol to multi-user broadcast authentication based on elliptic curve cryptography in wireless sensor networks (wsns). Math. Comput. Appl. 23(2), 17 (2018)Google Scholar
  34. 34.
    Tajiki, M.M., Akbari, B., Mokari, N., and Chiaraviglio, L.: Sdn-based resource allocation in mpls networks: a hybrid approach. arXiv preprint arXiv:1803.11486 (2018)
  35. 35.
    Al-Janabi, S., and Al-Shourbaji, I.: A smart and effective method for digital video compression. In: 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT). IEEE, pp. 532–538 (2016)Google Scholar
  36. 36.
    Murthi Reddy, P., Manjula, S.H., and Venugopal, K.R.: Sdspg: secured data sharing with privacy as a group in cloud computing. Int. J. Curr. Adv. Res. 7(2), 10036–10043 (2018)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity Visvesvaraya College of Engineering, Bangalore UniversityBengaluruIndia

Personalised recommendations