Abstract

Resources are provided mainly in the form of services in cloud computing. In the distribute environment of cloud computing, how to find the needed services efficiently and accurately is the most urgent problem in cloud computing. In cloud computing, services are the intermediary of cloud platform, services are connected by lots of service providers and requesters and construct the complex heterogeneous network. The traditional recommendation systems only consider the functional and non-functional requirements of services but ignore the links between providers and requesters of service, which result to the service position is not accurate. Focus on the problems, this study intends to model the relationship of the cloud services participants with the format of heterogeneous information network, which intend to mine the hidden relationships between services participants in the cloud computing environment. In theoretical research, we proposed a cloud service heterogeneous network extraction and automatic maintenance model, proposed a new service recommendation system based on heterogeneous service network ranking and clustering.

Keywords:

Cloud computing, clustering, heterogeneous network analysis, ranking, service recommendation system,

References

1. Armbrust, M., A. Fox and R. Griffith, 2010. A view of cloud computing. Commun. ACM, 53(4): 50-58.
   CrossRef    
2. Brin, S. and L. Page, 1998. The anatomy of a large-scale hypertextual web search engine. Comput. Networks ISDN, 30(1998): 107-117.
   CrossRef    
3. Burke, R., 2002. Hybrid recommender systems: Survey and experiments. User Model. User-Adap., 12(4): 331-370.
   CrossRef    
4. Buyya, R., C.S. Yeo and S. Venugopal, 2008. Market-oriented cloud computing: Vision, hype and reality for delivering IT services as computing utilities. Proceeding of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC), pp: 5-13.
   CrossRef    
5. Han, S.M., M. Mehedi and C.W. Yoon, 2009. Efficient service recommendation system for cloud computing market. Proceeding of the 2nd International Conference on Interaction Sciences, pp: 839-845.
   CrossRef    PMCid:PMC3122277    
6. Herlocker, J.L., J.A. Konstan and L.G. Terveen, 2004. Evaluating collaborative filtering recommender systems. ACM T. Inform. Syst., 22(1): 5-53.
   CrossRef    
7. Kleinberg, J.M., 1999. Authoritative sources in a hyperlinked environment. J. ACM, 46(5): 604-632.
   CrossRef    
8. Pazzani, M. and D. Billsus, 2007. Content-based recommendation systems. Lect. Notes Comput. Sc., 432: 325-341.
   CrossRef    
9. Sarwar, B., G. Karypis and J. Konstan, 2000. Analysis of recommendation algorithms for e-commerce. Proceeding of the 2nd ACM Conference on Electronic Commerce, pp: 158-167.
   CrossRef    
10. Sun, Y., J. Han and P. Zhao, 2009a. RankClus: Integrating clustering with ranking for heterogeneous information network analysis. Proceeding of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pp: 565-576.
    CrossRef    
11. Sun, Y.Z., Y.T. Yu and J.W. Han, 2009b. Ranking-based clustering of heterogeneous information networks with star network schema. Proceeding of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp: 97-805.
    CrossRef    
12. Xue, L. and C. Ling, 2011. Recommendations based on network analysis. Proceeding of International Conference on Advanced Computer Science and Information Systems, pp: 9-15.
    PMCid:PMC3197795    
13. Zhang, C. and Y. Han, 2007. Service recommendation with adaptive user interests modeling. Lect. Notes Comput. Sc., 4882: 265-270.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.