Cloud Service Recommendation Based on a Correlated QoS Ranking Prediction

Quality-of-Service (QoS) is an important concept for service selection and user satisfaction in cloud computing. So far, service recommendation in the cloud is done by means of QoS, ranking and rating techniques. The ranking methods perform much better, when compared with the rating methods. In view of the fact that the ranking methods directly predict QoS rankings as accurately as possible, in most of the ranking methods, an individual QoS value alone is employed to predict the cloud rank. In this paper, we propose a correlated QoS ranking algorithm along with a data smoothing technique and combined with QoS to predict a personalized ranking for service selection by an active user. Experiments are conducted employing a WSDream-QoS dataset, including 300 distributed users and 500 real world web services all over the world. Six different techniques of correlated QoS ranking schemes have been proposed and evaluated. The experimental results showed that this approach improves the accuracy of ranking prediction when compared to a ranking prediction framework using a single QoS parameter.

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References

  1. Amazon.: Amazon Elastic Compute Cloud. http://aws.amazon.com/ec2/,1 (2009)
  2. Ani Brown Mary, N.: Profit maximization for SAAS using SLA based SPOT PRICING in CLOUD COMPUTING. Int. J. Emerg. Technol. Adv. Eng. 3(1), 19–25 (2013) Google Scholar
  3. Ani Brown Mary, N., Saravanan, K.: Performance factors of CLOUD COMPUTING data centers using [(M/G/1):(/GDMODEL)] queuing systems. Int. J. Grid Comput. Appl. 4(1), 1–9 (2013) Google Scholar
  4. Ani Brown Mary, N.: Profit maximization for service providers using hybrid pricing in cloud computing. Int. J. Comput. Appl. Technol. Res. 2(3), 218–223 (2013) Google Scholar
  5. Ani Brown Mary, N., Jayapriya, K.: An extensive survey on QoS in cloud computing. Int. J. Comput. Sci. Inf. Technol. 5(1), 1–5 (2014) Google Scholar
  6. Al Falasi, A., Serhani, M.A.: A framework for SLA-based cloud services verification and composition. In: Proceedings of 2011 International Conference on Innovations in Information Technology (2011)
  7. Sarwar, B., Karypis, G., Konstan, J. & Riedl J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of WWW Conference (2001)
  8. Bharathi, M., Sandeep Kumar, P., Poornima, G.V.: Performance factors of cloud computing data centers using M/G/m/m+r queuing systems. IOSR J. Eng. 2(9), 06–10. e-ISSN: 2250-3021, p-ISSN: 2278-8719. www.iosrjen.org (2012)
  9. Li, B., Song, A.M., Song, J.: A distributed QoS-constraint task scheduling scheme in cloud computing environment: model and algorithm. Adv. Inf. Sci. Serv. Sci. 4(5), 283–291 (2012) Google Scholar
  10. Mondala, B., Dasguptaa, K., Duttab, P.: Load balancing in cloud computing using stochastic hill climbing—a soft computing approach. Proced. Technol. 4, 783–789 (2012) ArticleGoogle Scholar
  11. Yeo, C.S. Buyya, R.: A taxonomy of market-based resource management systems for utility-driven cluster computing. Softw. Pract. Exp. 36, 1381–1419 (2006). Published online 8 June 2006 in Wiley InterScience (www.interscience.wiley.com). doi:10.1002/spe.725
  12. Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of ICML 2005, pp. 89–96 (2005)
  13. Angeli, D., Masala, E.: A cost-effective cloud computing framework for accelerating multimedia communication simulations. J. Parallel Distrib. Comput. 72(10), 1373–1385 (2012) ArticleGoogle Scholar
  14. Kumar, T.A.D., Sumathi, G.: Intelligent management of remote facilities and quality of cloud services. Int. J. Grid Distrib. Comput. 4(2), 43–51 (2011) Google Scholar
  15. Armstrong, D., Djemame, K.: Towards quality of service in the cloud. In: Proceedings of the School of Computing, University of Leeds, United Kingdom
  16. Wu, D., Mendel, J.M.: A vector similarity measure for linguistic approximation: interval type-2 and type-1 fuzzy sets. Inf. Sci. 178, 381–402 (2008) ArticleMathSciNetMATHGoogle Scholar
  17. Adomavicius, G., Kwon, Y.O.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012) ArticleGoogle Scholar
  18. Google, App Engine. http://code.google.com/appengine/. 17 February 2009
  19. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7, 76–80 (2003) ArticleGoogle Scholar
  20. Liu, G., Liu, C., Yang, C. Li, D.: Scheduling research based on genetic algorithm and QoS constraints of cloud computing resources. J. Theor. Appl. Inf. Technol. 51(1), 91–96 (2013) Google Scholar
  21. Xue, G.R., Lin, C., Yang, Q., Xi, W., Zeng, H.J., Yu, Y. Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: Proceedings of SIGIR (2005)
  22. Ma, H., King, I., Lyu, M.R.: Effective missing data prediction for collaborative filtering. In: 30th International ACM SIGIR Conference Research and Development in Information Retrieval (SIGIR’07), pp. 39–46 (2007)
  23. Lawrance, H., Silas, S.: Efficient Qos based resource scheduling using PAPRIKA method for cloud computing. Int. J. Eng. Sci. Technol. 5(3), 638–643 (2013) Google Scholar
  24. Dean, J. Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of the OSDI 2004
  25. Lin, J.W., Chen, C.H., Chang, J.M.: QoS-aware data replication for data intensive applications in cloud computing systems. IEEE Trans. Cloud Comput. 1(1), 101–115 (2013) ArticleGoogle Scholar
  26. Wu, J., Chen, L., Feng, Y., Zheng, Z., Zhou, M.C., Wu, Z.: Predicting quality of service for selection by neighborhood-based collaborative filtering. IEEE Trans. Syst. Man Cybern. Syst. 43(2), 428–439 (2013) ArticleGoogle Scholar
  27. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison (1998)
  28. Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington, DC (2003)
  29. Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying userbased and itembased collaborative filtering approaches by similarity fusion. In: Proceedings of the SIGIR’06, Seattle, Washington, USA, August 6–11 (2006)
  30. Kim, Kyong Hoon, Lee, Wan Yeon, Kim, Jong, Buyya, Rajkumar: SLA-based scheduling of bag-of-tasks applications on power-aware cluster systems. IEICE Trans. Inf. Syst. E93-D(12), 3194–3201 (2010) ArticleGoogle Scholar
  31. L.S.V. Singh, J.A.: A greedy algorithm for task scheduling and resource allocation problems in cloud computing. Int. J. Res. Dev. Technol. Manag. Sci. Kailash 21(1), (2014). ISBN: 978-1-63102-445-0
  32. Wu, L., Garg, S.K., Buyya, R.: SLA-based admission control for a software-as-a-service provider in cloud computing environments. J. Comput. Syst. Sci. 78, 1280–1299 (2012) ArticleGoogle Scholar
  33. Si, L., Jin, R.: Flexible mixture model for collaborative filtering. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC (2003)
  34. Devgan, M., Dhindsa, K.S.: A study of different QoS management techniques in cloud computing. Int. J. Soft Comput. Eng. 3(3), (2013). ISSN: 2231-2307
  35. Dodge, M.: Finding the source of the Amazon.com: hype of the “EARTH’S biggest bookstore. In: Proceedings of the Centre for Advanced Spatial Analysis Working Paper Series
  36. Khatr, M.: Cosine similarity function for the temporal dynamic web data. Int. J. Comput. Sci. Eng. Technol. 3(8), 315–318 (2012) Google Scholar
  37. Deshpande, M., Karypis, G.: Item-based top-n recommendation. ACM Trans. Inf. Syst. 22(1), 143–177 (2004) ArticleGoogle Scholar
  38. Sultan, N.: Cloud computing for education: A new dawn? Int. J. Inf. Manag. 30(2), 109–116 (2010) ArticleGoogle Scholar
  39. Liu, N.N., Yang, Q.: EigenRank: a ranking-oriented approach to collaborative filtering. In: Proceedings of the SIGIR’08, New York, USA
  40. Garraghan, P., Townend, P., Xu, J.: Real-time fault-tolerance in federated cloud environments. In: Proceedings of the 2012 IEEE 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops
  41. Wu, Q., Iyengar, A., Subramanian, R., Rouvellou, I., Silva-Lepe, I., Mikalsen, T.: Combining quality of service and social information for ranking services. In: IBM T.J. Watson Research Center, Skyline Drive, Hawthorne, NY 10532, USA
  42. Cannon, R.L., Dave, J.V., Bezdek, J.C.: Efficient implementation of the fuzzy-c-means clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 8(2), 248–255 (1986) ArticleMATHGoogle Scholar
  43. Jin, R., Chai, J.Y., Si, L.: An automatic weighting scheme for collaborative filtering. In: Proceedings of SIGIR (2004)
  44. Salesforce.com. CRM salesforce.com. http://www.salesforce.com/
  45. Garg, S.K., Versteeg, S., Buyya, R.: SMICloud: a framework for comparing and ranking cloud services. In: Proceedings of 2011 Fourth IEEE International Conference on Utility and Cloud Computing
  46. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., Ghalsasi, A.: Cloud computing—the business perspective. Decis. Support Syst. 51(1), 176–189 (2011) ArticleGoogle Scholar
  47. Sforce: the client/service application development utility. www.salesforce.com
  48. Dubey, S., Agrawal, S.: QoS driven task scheduling in cloud computing. Int. J. Comput. Appl. Technol. Res. 2(5), 595–600 (2013) Google Scholar
  49. Ferretti, S., Ghini, V., Panzieri, F., Pellegrini, M., Turrini, E.: QoS-aware clouds. In: 2010 IEEE 3rd International Conference on Cloud Computing
  50. Subashini, S., Kavitha, V.: A survey on security issues in service delivery models of cloud computing. J. Netw. Comput. Appl. 34, 1–11 (2011) ArticleGoogle Scholar
  51. Chattopadhyay, S.: A comparative study of fuzzy-c-means algorithm and entropy-based fuzzy clustering algorithms. Comput. Inf. 30, 701–720 (2011) Google Scholar
  52. Hofmann, T., Puzicha, J.: Latent class models for collaborative filtering. In: IJCAI, pp. 688–693 (1999)
  53. Hofmann, T.: Collaborative filtering via gaussian probabilistic latent semantic analysis. In: Proceedings of the SIGIR’03, Toronto, Canada, July 28–August 1 (2003)
  54. Velmurugan, T.: Performance based analysis between K-Means and Fuzzy-C-Means clustering algorithms for connection oriented telecommunication data. Appl. Soft Comput. 19, 134–146 (2014) ArticleGoogle Scholar
  55. Kantere, V., Dash, D., Francois, G., Kyriakopoulou, S., Ailamaki, A.: Optimal service pricing for a cloud cache. IEEE Trans. Knowl. Data Eng. 23(9), 1345–1358 (2011) ArticleGoogle Scholar
  56. Emeakaroha, V.C., Netto, M.A.S., Calheiros, R.N., Brandic, I., Buyya, R., De Rose, C.A.F.: Towards autonomic detection of SLA violations in Cloud infrastructures. Future Gener. Comput. Syst. 28(7), 1017–1029 (2012) ArticleGoogle Scholar
  57. Qiu, W., Zheng, Z., Wang, X., Yang, X., Lyu, M.R.: Reputation-aware QoS Value prediction of web services. In: Proceedings of the 2013 IEEE 10th International Conference on Services Computing
  58. Zheng, Z., Wu, X., Zhang, Y., Lyu, M.R., Wang, J.: QoS ranking prediction for cloud services. IEEE Trans. Parallel Distrib. Syst. 24(6), 1213–1222 (2013) ArticleGoogle Scholar
  59. Zheng, Z., Ma, H., Lyu, M.R., King, I.: QoS-aware web service recommendation by collaborative filtering. Proc. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011) ArticleGoogle Scholar
  60. Zheng, Z., Zhang, Y., Lyu, M.R.: Distributed QoS evaluation for real-world web services. In: Proceedings of the 2010 IEEE International Conference on Web Services
  61. Arvelin, K.J., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002) ArticleGoogle Scholar

Acknowledgments

The authors would like to thank Zibin Zheng, Yilei Zhang and Michael R. Lyu for providing the WSDream-QoS datasets [60] that were publicly released from the website (http://www.wsdream.net). This dataset was very helpful for our research purposes. We would also like to thank the anonymous reviewers for their valuable and insightful suggestions.

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Authors and Affiliations

  1. Vin Solutions, 1st Floor, No 40, North Street, Rajarajeshwari Nagar, Tirunelveli, 627007, India K. Jayapriya
  2. Department of Computer Science, Regional Centre of Anna University, Tirunelveli, 627007, India N. Ani Brown Mary
  3. Department of Computer Science, M.S. University, Tirunelveli, 627012, India R. S. Rajesh
  1. K. Jayapriya