Risk analysis of cloud service providers by analyzing the frequency of occurrence of problems using E-Eclat algorithm

  • C. MuralidharanEmail author
  • R. Anitha


Cloud is a large scale distributed system where the resource provisioning is a challenging and important problem. It may be statically or dynamically provisioned and must meet the QoS parameters such as throughput, availability, security, transit delay and thereby ensuring SLA. Nowadays the number of service providers tends to increase tremendously in the market, hence selecting an optimal service provider is a tedious process which leads to trust a third party known as cloud service brokers. They analyzes the problems with the provider and explores the providers to the consumers to make the service available. But the risks with the providers are not known by the consumers and as well as the trusted cloud service brokers. Hence the proposed work indulges in finding the risk with the providers in the broker’s registry by analyzing the past histories of them. The analysis will be carried out for a period of time for all the providers and the most important problems that occur during the period will be considered for prioritizing the providers. Hence this paper proposes a novel Enhanced Eclat algorithm for finding the frequent problems at the providers level and also the frequency of occurrence of the same with each provider. The proposed E-Eclat algorithm is compared with two other algorithms and found that it consumes reduced time than other algorithms. It is also observed that the complexity is much low than other two algorithms as transpose scanning is used.


Risk analysis Cloud provider selection Cloud provider analysis 



This work is financially supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India, under the Early Career Research Award Scheme. The Grant Number of the project is ECR/2016/000546.


  1. 1.
    Mouratidis, H., Islam, S., Kalloniatis, C., & Gritzalis, S. (2013). A framework to support selection of cloud providers based on security and privacy requirements. The Journal of Systems and Software,83, 2276–2293.CrossRefGoogle Scholar
  2. 2.
    Sun, L., Dong, H., Hussain, F. K., Hussain, O. K., & Chang, E. (2014). Cloud service selection: State-of-the-art and future research directions. Journal of Network and Computer Applications,45, 134–150.CrossRefGoogle Scholar
  3. 3.
    Fan, W., & Perros, H. (2014). A novel trust management framework for multi-cloud environments based on trust service providers. Knowledge-Based Systems,70, 392–406.CrossRefGoogle Scholar
  4. 4.
    Javed, B., Bloodsworth, P., Rasool, R., Munir, K., & Rana, O. (2015). Cloud market maker: An automated dynamic pricing marketplace for cloud users. Future Generation Computer Systems,54, 52–67.CrossRefGoogle Scholar
  5. 5.
    Wang, X., Cao, J., & Xiang, Y. (2014). Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing. The Journal of Systems and Software,100, 195–210.CrossRefGoogle Scholar
  6. 6.
    Sun, L., Ma, J., Zhang, Y., Dong, H., & Hussain, F. K. (2015). Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection. Future Generation Computer Systems,57, 42–55.CrossRefGoogle Scholar
  7. 7.
    Garg, S. K., Versteeg, S., & Buyya, R. (2013). A framework for ranking of cloud computing services. Future Generation Computer Systems,29(4), 1012–1023.CrossRefGoogle Scholar
  8. 8.
    Grandison, T., & Sloman, M. (2000). A survey of trust in internet applications. IEEE Communications Surveys and Tutorials,3, 2–16.CrossRefGoogle Scholar
  9. 9.
    Jøsang, A., Ismail, R., & Boyd, C. (2007). A survey of trust and reputation systems for online service provision. Decision Support Systems,43(2), 618–644.CrossRefGoogle Scholar
  10. 10.
    Sato, H., Kanai, A., & Tanimoto, S. (2010). A cloud trust model in a security aware cloud. In Proceedings of 10th IEEE/IPSJ international symposium on applications and the internet (SAINT) (pp. 121–124). IEEE.Google Scholar
  11. 11.
    Li, X., Zhou, L., Shi, Y., & Guo, Y. (2010). A trusted computing environment model in cloud architecture. In Proceedings of 2010 international conference on machine learning and cybernetics (ICMLC) (Vol. 6, pp. 2843–2848). IEEE.Google Scholar
  12. 12.
    Schryen, G., Volkamer, M., Ries, S., & Habib, S. M. (2011) A formal approach towards measuring trust in distributed systems. In Proceedings of the 2011 ACM symposium on applied computing (pp. 1739–1745). ACM.Google Scholar
  13. 13.
    Alhamad, M., Dillon, T., & Chang, E. (2011). A trust-evaluation metric for cloud applications. Proceedings of International Journal of Machine Learning and Computing,1(4), 416–421.CrossRefGoogle Scholar
  14. 14.
    Noor, T., & Sheng, Q. (2011). Trust as a service: A framework for trust management in cloud environments. In Web information system engineeringWISE 2011 (pp. 314–321).Google Scholar
  15. 15.
    Abbadi, I. M., & Alawneh, M. (2012). A framework for establishing trust in the cloud. Computers & Electrical Engineering,38(5), 1073–1087.CrossRefGoogle Scholar
  16. 16.
    Petri, I., Rana, O. F., Silaghi, G. C., & Rezgui, Y. (2014). Risk assessment in service provider communities. Future Generation Computer Systems,41, 32–43.CrossRefGoogle Scholar
  17. 17.
    Ghosh, N., Ghosh, S. K., & Das, S. K. (2015). SelCSP: A framework to facilitate selection of cloud service providers. IEEE Transactions on Cloud Computing,3(1),  66–79.CrossRefGoogle Scholar
  18. 18.
    Madria, S., & Sen, A. (2015). Offline risk assessment of cloud service providers. IEEE Cloud Computing, 2, 50–57.CrossRefGoogle Scholar
  19. 19.
    Anitha, R., & Mukherjee, S. (2014). Bloom filter based metadata placement and management in cloud computing. Internationl Journal of Recent Trends in Engineering & Technology,11, 93–104.CrossRefGoogle Scholar
  20. 20.
    Pacheco, C. (2005). Eclat: Automatic generation and classification of test inputs. Massachusetts: Massachusetts Institute of Technology.Google Scholar
  21. 21.
    Zak, M. J., Gouda, K. (2003) Fast vertical mining using diffsets. In Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 326–335).Google Scholar
  22. 22.
    Agrawal, R., & Srikant, R. (1994) Fast algorithms for mining association rules in large databases. In Proceedings 20th international conference very large data bases (VLDB "94) (pp. 478–499).Google Scholar
  23. 23.
    Moens, S., Aksehirli, E., & Goethals, B. (2013). Frequent itemset mining for big data. In IEEE international conference on big data (pp. 111–118).Google Scholar
  24. 24.
    Huang, Y., & Lin, S. (2003). Mining sequential patterns using graph search techniques. In Proceedings 27th annual international computer software and applications conference (pp. 4–9).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Sri Venkateswara College of EngineeringSriperumbudurIndia

Personalised recommendations