Prediction of best cloud service provider using the QoS ranking framework

  • Authors

    • A S. Syed Fiaz
    • K S. Guruprakash
    • A S. Syed Navaz
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.10151
  • Cloud rank1, QoS ranking prediction, cloud service provider.

  • Abstract

    The ability to utilize the computing resources based on the need has taken the Cloud computing to a greater height and it has increased the potential to extend the flexibility and efficiency of any resource. Considering the advantages, there are various Cloud Services Providers (CSP) that can offer services based on the user request and finding optimal services among those Cloud Services can be a great dispute. The proposed work relies on a QoS Ranking prediction that chooses the appropriate services offered by the various different CSPs. Based on those predicted analysis, the best CSP will be marked with a Ranking framework, according to which the Services will be directed to the users.

  • References

    1. [1] Jaatun MG, Zhao G & Rong C, “Cloud Computingâ€, CloudCom: IEEE International Conference on Cloud Computing, (2009).

      [2] Alhamad M, Dillon T & Chang E, “Sla-based trust model for cloud computingâ€, 13th International Conference on Network-Based Information Systems (NBiS), (2010), pp.321-324.

      [3] Zheng Z, Zhou TC, Lyu MR & King I, “FTCloud: A component ranking framework for fault-tolerant cloud applicationsâ€, IEEE 21st International Symposium on Software Reliability Engineering (ISSRE), (2010), pp.398-407.

      [4] Ding S, Yang S, Zhang Y, Liang C & Xia C, “Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problemsâ€, Knowledge-Based Systems, Vol.56, (2014), pp.216-225.

      [5] Garg SK, Versteeg S & Buyya R, “Smicloud: A framework for comparing and ranking cloud servicesâ€, Fourth IEEE International Conference on Utility and Cloud Computing, (2011), pp.210-218.

      [6] Zheng Z, Wu X, Zhang Y, Lyu MR & Wang J, “QoS ranking prediction for cloud servicesâ€, IEEE transactions on parallel and distributed systems, Vol.24, No.6,(2013), pp.1213-1222.

      [7] Zhang Y, Zheng Z & Lyu MR, “Exploring latent features for memory-based QoS prediction in cloud computingâ€, 30th IEEE Symposium on Reliable Distributed Systems, (2011), pp.1-10.

      [8] Usha M, Akilandeswari J & Syed Fiaz AS, “An Efficient QoS Framework for Cloud Brokerage Servicesâ€, International Symposium on Cloud and Services Computing (ISCOS), (2012).

      [9] Alagi SR & Dharavath S, “Efficient Algorithm for Predicting QoS in Cloud Servicesâ€, International Journal of Advanced Research in Computer Engineering & Technology, Vol.4, No.11, (2015), pp.4179-4183.

      [10] Syed Fiaz AS, Asha N, Sumathi D & Syed Navaz AS, “Data Visualization: Enhancing Big Data More Adaptable and Valuableâ€, International Journal of Applied Engineering Research, Vol.11, No.4, (2016), pp.2801-2804.

      [11] Navinkumar R & Raghul M, “QoS Ranking Prediction for Cloud Serviceâ€, International Research Journal of Engineering and Technology, Vol.03, No.03, (2016).

      [12] Syed Navaz AS & Kadhar Nawaz GM, “Layer Orient Time Domain Density Estimation Technique Based Channel Assignment in Tree Structure Wireless Sensor Networks for Fast Data Collectionâ€, International Journal of Engineering and Technology, Vol.8, No.3, (2016), pp.1506-1512.

      [13] Subathra J & Latchoumy P, “QoS Ranking Prediction Framework for Cloud Serviceâ€, International Journal of Scientific & Engineering Research, Vol.6, No.4, (2015), pp.24-27.

  • Downloads

  • How to Cite

    S. Syed Fiaz, A., S. Guruprakash, K., & S. Syed Navaz, A. (2017). Prediction of best cloud service provider using the QoS ranking framework. International Journal of Engineering & Technology, 7(1.1), 486-488. https://doi.org/10.14419/ijet.v7i1.1.10151

    Received date: 2018-03-14

    Accepted date: 2018-03-14

    Published date: 2017-12-21