Increasing popularity of Cloud computing lead to research problem for building high quality cloud application . Quality of service provides valuable information for optimal decision. From the set of functionally equivalent service candidates optimal cloud selection is taken. In this paper different QoS ranking prediction techniques are studied . The techniques are collaborative filtering and cloud rank framework. Collaborative filtering techniques have three methods neighborhood, model based and memory based. In this ranking oriented approach accurate missing value prediction may not lead to accuracy ranking. Real-world invocations are required on the service candidates for obtaining Quality of Service (QoS) values. QoS ranking prediction is framework (Cloudrank framework) that avoids time-consuming and expensive real world service invocation. QoS ranking prediction framework for cloud services by taking advantages of past service usage experiences of other consumers.

1. Zibin Zheng, Xiamiao Wu, Yliei Zhang, Michael R Lyu and Jianmin Wang "QoS ranking prediction for cloud services", IEEE Transactions on Parallel and distributed System,Vol. 24, No. 6,pp. 1213-1222, June2013
2. Zibin Zheng, Yliei Zhang,and Michael R Lyu "CloudRank: A QoS Driven Component Ranking Framework for Cloud Computing", IEEE International Symposium on Reliable Distributed Systems, pp. 184-193, 2010
3. Zibin Zheng,,Hao Ma ,Michael R Lyu and Irwin King "QoS Aware Web Service Recommendation by Collaborative Filtering", IEEE Transaction on service computing, Vol. ,4 No. 2, pp. 140-152, Apr-June 2011
4. Z. Zheng, H. Ma, M. R. Lyu, and I. King ,"WSRec: Collaborative Filtering Based Web Service Recommender System," Proc . Seventh Int'l Conf. Web Services(ICWS'09), pp. 437-444, 2009
5. Z. Zheng and M. R. Lyu, "WS-DREAM: A Distributed Reliability Assessment Mechanism for Web Services," Proc. 38th Int'l Conf. Dependable Systems and Networks(DSN'08), pp. 392-397, 2008.
6. Debajyoti Mukhopadhyay, Falguni J. Chathly, Nagesh N. Jadhav, "QoS Based Framework for Effective Web Services in Cloud Computing", Journal of Software Engineering and Applications, pp952-960,2012.
7. L. Zeng, B. Benatallah , A. H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang, "QoS- Aware Middleware for Web Services Composition," IEEE Trans. Software Engineering. , Vol. 30,no. 5,pp. 311-327,May 2004
8. J. Wu, L. Chen, Y. Feng, Z. Zheng ,M. Zhou,and Z. Wu, "Predicting QoS for Service Selection by Neighborhood-Based Collaborative Filtering," IEEE Trans. System, Man, and Cybernetics, Vol. 43, No. 2, March 2013.
9. H. Ma, I. King ,and M. R. Lyu , "Effective Missing Data Prediction for Collaborative Filtering," Proc. 30th Int'l ACMSIGIR Conf. Research and Development in Information Retrieval(SIGIR'07), pp. 39-46,2007.
10. Zibin Zheng, Yliei Zhang,and Michael R Lyu "Distributed QoS Evaluation for Real World Web Services", IEEE International Conference on Web Services ,pp 83-90 2010.
11. Mandeep Devgan, Kanwalvir Singh Dhinsa,"A Study Different QoS Management Techniques in Cloud Computing "International Journal of Soft Computing and Engineering,Vol. 3 ,pp 37-41,July 2013.
12. Y. Liu, A. H. Ngu, and L. Zeng, "QoS computation and policing in dynamic web service selection," in Proc. Int. World Wide Web Conf. , Manhattan, NY, 2004, pp. 66–73.
13. L. Chen, Y. Feng, J. Wu, and Z. Zheng, "An enhanced QoS prediction approach for service selection," in Proc. Int. Conf. Serv. Comput. , 2011, pp. 727–728.

Qos Ranking Prediction Cloud Computing Cloudrank.