An Abstract Model for Adaptive Access Control in Cloud Computing

  • Amardeep KaurEmail author
  • Amandeep Verma
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)


Cloud computing is a paradigm that presents network access to pooled configurable computing resources on demand. Resource management has an immense role in authorization and access control. In computing clouds, it is desirable, to avoid underutilization and over-utilization of computing resources because these may result wasting of resources or leads to lengthy response times. The factors related to operational and situational awareness can affect an access control system and ultimately the utilization of resources. The present study is intended to develop an adaptive access control model. The user behaviour is assessed in terms of the usage of resources by characterizing the cloud workload. This assessment is stored in the knowledge base. A recommender system uses the knowledge base to make the decisions about the adaption of access control policies, in order to get effective usage of the resources of cloud. The present paper presents an abstract representation of such model and its operational behaviour.


Access control model Cloud computing 


  1. 1.
    Younis, Y.A., Kifayat, K., Merabti, M.: An access control model for cloud computing. J. Inf. Secur. Appl. 19, 45–60 (2014)Google Scholar
  2. 2.
    Xiong, H., Chen, X., Zhang, B., Wang, G.: A finer-grained resource management model oriented to role-based access control. In: CCIS 2014—Proceedings of 2014 IEEE 3rd International Conference on Cloud Computing Intelligence System, pp. 198–206 (2014)Google Scholar
  3. 3.
    Lin, W., Wang, J.Z., Liang, C., Qi, D.: A threshold-based dynamic resource allocation scheme for cloud computing. Proc. Eng. 23, 695–703 (2011)CrossRefGoogle Scholar
  4. 4.
    Moreno, I.S., Garraghan, P., Townend, P., Xu, J.: Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. IEEE Trans. Cloud Comput. 2, 208–221 (2014)CrossRefGoogle Scholar
  5. 5.
    Zhang, W., Liu, J., Liu, C., Zheng, Q., Zhang, W.: Workload modeling for virtual machine-hosted application. Expert Syst. Appl. 42, 1835–1844 (2015)CrossRefGoogle Scholar
  6. 6.
    Yazir, Y.O., Matthews, C., Farahbod, R., Neville, S., Guitouni, A., Ganti, S.: Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis. In: Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference (2010)Google Scholar
  7. 7.
    Shaikh, R.A., Adi, K., Logrippo, L.: Dynamic risk-based decision methods for access control systems. Comput. Secur. 31, 447–464 (2012)CrossRefGoogle Scholar
  8. 8.
    Malik, A.A., Anwar, H., Shibli, M.A.: Self-adaptive access control and delegation in cloud computing. In: 2016 IEEE/ACIS 17th International Conference Software Engineering Artificial Intelligence Network Parallel/Distributed Computing, SNPD 2016, pp. 169–176 (2016)Google Scholar
  9. 9.
    Ma, S., Wang, Y.: Self-adaptive access control model based on feedback loop. In: 2013 International Conference Cloud Computing Big Data, pp. 597–602 (2013)Google Scholar
  10. 10.
    An, C., Zhou, J., Liu, S., Geihs, K.: A multi-tenant hierarchical modeling for cloud computing workload. Intell. Autom. Soft Comput. 8587, 1–8 (2016)Google Scholar
  11. 11.
    Magalhães, D., Calheiros, R.N., Buyya, R., Gomes, D.G.: Workload modeling for resource usage analysis and simulation in cloud computing. Comput. Electr. Eng. 47, 69–81 (2015)CrossRefGoogle Scholar
  12. 12.
    Patel, J., et al.: Workload estimation for improving resource management decisions in the cloud. In: Proceedings of 2015 IEEE 12th International Symposium Autonomous Decentralized System, ISADS 2015, pp. 25–32 (2015)Google Scholar
  13. 13.
    Kousiouris, G., Menychtas, A., Kyriazis, D., Gogouvitis, S., Varvarigou, T.: Dynamic, behavioral-based estimation of resource provisioning based on high-level application terms in cloud platforms. Futur. Gener. Comput. Syst. 32, 27–40 (2014)CrossRefGoogle Scholar
  14. 14.
    Tavizi, T., Shajari, M., Dodangeh, P.: A usage control based architecture for cloud environments. In: 2012 IEEE 26th International Parallel Distributed Processing Symposium Workshops and PhD Forum, pp. 1534–1539 (2012)Google Scholar
  15. 15.
    Di, S., Kondo, D., Cappello, F.: Characterizing and modeling cloud applications/jobs on a Google data center. J. Supercomput. 69, 139–160 (2014)CrossRefGoogle Scholar
  16. 16.
    Panneerselvam, J., Liu, L., Antonopoulos, N., Bo, Y.: Workload analysis for the scope of user demand prediction model evaluations in cloud environments. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014, pp. 883–889 (2014)Google Scholar
  17. 17.
    Tiwari, V., Bindal, U., Pandey, S.: Cloud computing: a next generation revolution in IT with e-governance. Netw. Commun. Eng. 4(6), 324–330 (2012)Google Scholar
  18. 18.
    Baumeister, J., Striffler, A.: Knowledge-driven systems for episodic decision support. Knowl. Based Syst. 88, 45–56 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Punjabi University Regional CentreMohaliIndia

Personalised recommendations