Optimizing best cloud service using the Bayesian personalized ranking framework

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Cloud computing has gaineda largest amountof popularity, Since the computing resources can be utilized in efficient manner. In other case it offers increased size in terms of flexibility and efficiency. The Cloud market has witnessed a vastincrease in the number of different cloud services, and then the best and optimal service can be selected by CSP. In our proposed system, we are using Bayesian algorithm to develop raking framework for QOS predication and based on this different CSP can be selected to offer the appropriate services based on the QOS requirement from the user. Based on the predicted analysis, the best CSP will be marked with a Ranking framework, according to which the Services will be directed to the users.


  • Keywords


    Cloud computing, bayesianpersonalized ranking framework, cloud service provider, quality of service.

  • References


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

      [2] Alhamad M, Dillon T & Chang E, “Sla-based trust model for cloud computing”, 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 applications”, 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 problems”, Knowledge-Based Systems, Vol.56, (2014), pp.216-225.

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

      [6] Zheng Z, Wu X, Zhang Y, LyuMR & Wang J, “QoS ranking prediction for cloud services”, 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 computing”, 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 Services”, International Symposium on Cloud and Services Computing (ISCOS), (2012).

      [9] Alagi SR &Dharavath S, “Efficient Algorithm for Predicting QoS in Cloud Services”, 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 Valuable”, International Journal of Applied Engineering Research, Vol.11, No.4, (2016), pp.2801-2804.

      [11] Navinkumar R &Raghul M, “QoSRanking Prediction for Cloud Service”, International Research Journal of Engineering and Technology (IRJET), Vol.03, No.03, (2016).

      [12] Syed Navaz AS, Jayalakshmi P &Asha N, “Optimization of Real-Time Video Over 3G Wireless Networks”, International Journal of Applied Engineering Research, Vol.10, No.18, (2015), pp.39724– 39730.

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

      [14] SyedNavaz AS &Kadhar Nawaz GM, “Flow Based Layer Selection Algorithm for Data Collection in Tree Structure Wireless Sensor Networks”, International Journal of Applied Engineering Research, Vol.11, No.5, (2016), pp.3359-3363.

      [15] Syed Fiaz AS, Usha M &Akilandeswari J, “A Brokerage Service Model for QoS support in Inter-Cloud Environment”, International Journal of Information and Computation Technology, Vol.3, No.3, (2013), pp.257-260.

      [16] 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 Collection”, International Journal of Engineering and Technology,Vol.8, No.3, (2016), pp.1506-1512.

      [17] Huang QY & Huang TL, “An optimistic job scheduling strategy based on QoS for Cloud Computing”, International Conference on Intelligent Computing and Integrated Systems,(2010), 673-675.


 

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Article ID: 10226
 
DOI: 10.14419/ijet.v7i1.1.10226




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