Cluster Computing

, Volume 20, Issue 2, pp 1599–1614 | Cite as

Big media healthcare data processing in cloud: a collaborative resource management perspective

  • Amit Kumar Das
  • Tamal Adhikary
  • Md. Abdur Razzaque
  • Majed Alrubaian
  • Mohammad Mehedi Hassan
  • Md. Zia Uddin
  • Biao Song
Article
First Online:
Received:
Revised:
Accepted:

Abstract

Nowadays, big media healthcare data processing in cloud has become an effective solution for satisfying QoS demands of medical users. It can support various healthcare services such as pre-processing, storing, sharing, and analysis of monitored data as well as acquiring context-awareness. However, to support energy and cost savings, the union of cloud data centers termed as cloud confederation can be an promising approach, which helps a cloud provider to overcome the limitation of physical resources. However, the key challenge in it is to achieve multiple contradictory objectives, e.g., meeting the required level of services defined in service level agreement, maintaining medial users’application QoS, etc. while maximizing profit of a cloud provider. In this paper, for executing heterogeneous big healthcare data processing requests from users, we develop a local and global cloud confederation model, namely FnF, that makes an optimal selection decision for target cloud data center(s) by exploiting Fuzzy logic. The FnF trades off in between profit of cloud provider and user application QoS in selecting federated data center(s). In addition, FnF enhances its decision accuracy by precisely estimating the resource requirements for the big data processing tasks using multiple linear regression. The proposed FnF model is validated through numerical as well as experimental evaluations. Simulation results depict the effectiveness and efficiency of the FnF model compared to state-of-the-art approaches.

Keywords

Big media healthcare data Cloud confederation Multiple linear regression Quality-of-service Fuzzy logic 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (12-INF2885-02).

References

  1. 1.
    Das, A. K., Adhikary, T., Razzaque, Md. A., Hong, C. S.: An intelligent approach for virtual machine and qos provisioning in cloud computing. In: International Conference on Information Networking (ICOIN), pp. 462–467 (2013)Google Scholar
  2. 2.
    Beloglazov, A., Buyya, R.: Openstack neat: a framework for dynamic and energy-efficient consolidation of virtual machines in openstack clouds. Concurr. Comput. Pract. Exp. 27(5), 1310–1333 (2015)CrossRefGoogle Scholar
  3. 3.
    Goiri, I., Guitart, J., Torres, J.: Characterizing cloud federation for enhancing providers’ profit. In: IEEE 3rd International Conference on Cloud Computing (CLOUD), pp. 123–130 (2010)Google Scholar
  4. 4.
    Song, B., Hassan, Md M., Alamri, A., Alelaiwi, A., Tian, Y., Pathan, M., Almogren, A.: A two-stage approach for task and resource management in multimedia cloud environment. Computing 98(1—-2), 119–145 (2016)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Khanna, P., Jain, S.: Distributed cloud federation brokerage: a live analysis. In: IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), pp. 738–743 (2014)Google Scholar
  6. 6.
    Mashayekhy, L., Nejad, M.M.: Cloud federations in the sky: formation game and mechanism. Cloud Comput. IEEE Trans. 3(1), 14–27 (2015)CrossRefGoogle Scholar
  7. 7.
    Abdo, J.B., Demerjian, J., Chaouchi, H., Barbar, K., Pujolle, G.: Broker-based cross-cloud federation manager. In: 2013 8th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 244–251, (2013)Google Scholar
  8. 8.
    Toosi, A.N., Calheiros, R.N., Thulasiram, R.K., Buyya, R.: Resource provisioning policies to increase iaas provider’s profit in a federated cloud environment. In: 2011 IEEE 13th International Conference on High Performance Computing and Communications (HPCC), pp. 279–287 (2011)Google Scholar
  9. 9.
    Hadji, M., Zeghlache, D.: Mathematical programming approach for revenue maximization in cloud federations. Cloud Comput. IEEE Trans., PP(99):1–1 (2015)Google Scholar
  10. 10.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)CrossRefGoogle Scholar
  11. 11.
    Hassan, M.M., Abdullah-Al-Wadud, M, Almogren, A., Song, B., Alamri, A.: Energy-aware resource and revenue management in federated cloud: a game-theoretic approach. IEEE Syst. J. (2015)Google Scholar
  12. 12.
    Rochwerger, B., Breitgand, D., Epstein, A., Hadas, D., Loy, I., Nagin, K., Tordsson, J., Ragusa, C., Villari, M., Clayman, S., Levy, E., Maraschini, A., Massonet, P., Muoz, H., Tofetti, G.: Reservoir-when one cloud is not enough. Computer 44(3), 44–51 (2011)CrossRefGoogle Scholar
  13. 13.
    Hassan, M.M., Song, B, Huh, E.-N.: Distributed resource allocation games in horizontal dynamic cloud federation platform. In: 2011 IEEE 13th International Conference on High Performance Computing and Communications (HPCC), pp. 822–827 (2011)Google Scholar
  14. 14.
    Hassan, M.M., Abdullah-Al-Wadud, M., Almogren, A, Rahman, S.K., Alelaiwi, A, Alamri, A., Hamid, Md., et al. Qos and trust-aware coalition formation game in data-intensive cloud federations. Concurr. Comput. Pract. Exp. (2015). doi: 10.1002/cpe.3543
  15. 15.
    Saad, W., Han, Z., Debbah, M., Hjorungnes, A.: A distributed coalition formation framework for fair user cooperation in wireless networks. Wirel. Commun. IEEE Trans. 8(9), 4580–4593 (2009)CrossRefGoogle Scholar
  16. 16.
    Mashayekhy, L., Grosu, D.: A merge-and-split mechanism for dynamic virtual organization formation in grids. Parallel Distrib. Syst. IEEE Trans. 25(3), 540–549 (2014)CrossRefGoogle Scholar
  17. 17.
    Zhang, Z., Zhang, X.: Realization of open cloud computing federation based on mobile agent. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, 2009. ICIS 2009, vol. 3, pp. 642–646 (2009)Google Scholar
  18. 18.
    Casola, V., Rak, M., Villano, U.: Identity federation in cloud computing. In: 2010 Sixth International Conference on Information Assurance and Security (IAS), pp. 253–259 (2010)Google Scholar
  19. 19.
    Coulouris, G., Dollimore, J., Kindberg, T., Blair, G.: Distributed Systems: Concepts and Design, 5th edn. Addison-Wesley Publishing Company, USA (2011)MATHGoogle Scholar
  20. 20.
    Rochwerger, B., Breitgand, D., Levy, E., Galis, A., Nagin, K., Llorente, I.M., Montero, R., Wolfsthal, Y., Elmroth, E., Cáceres, J., Ben-Yehuda, M., Emmerich, W., Galán, F.: The reservoir model and architecture for open federated cloud computing. IBM J. Res. Dev. 53(4), 535–545 (2009)CrossRefGoogle Scholar
  21. 21.
    Patel, K.S., Sarje, A.K.: VM provisioning method to improve the profit and sla violation of cloud service providers. In: 2012 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), pp. 1–5 (2012)Google Scholar
  22. 22.
    Das, A.K., Adhikary, T., Razzaque, Md.A., Cho, E.J., Hong, C.S.: A qos and profit aware cloud confederation model for iaas service providers. In: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, ICUIMC ’14, pp. 42:1–42:7, Siem Reap, Cambodia, ACM (2014)Google Scholar
  23. 23.
    Shpiner, A., Keslassy, I., Arad, C., Mizrahi, T., Revah, Y.: Sal: Scaling data centers using smart address learning. In: 2014 10th International Conference on Network and Service Management (CNSM), pp. 248–253 (2014)Google Scholar
  24. 24.
    Xu, W., Huang, L., Fox, A., Patterson, D., Jordan, M.I.: Online system problem detection by mining patterns of console logs. In: Ninth IEEE International Conference on Data Mining, 2009. ICDM ’09, pp. 588–597 (2009)Google Scholar
  25. 25.
    Lin, Z.-C., Wu, W.-J.: Multiple linear regression analysis of the overlay accuracy model. Semicond. Manuf. IEEE Trans. 12(2), 229–237 (1999)CrossRefGoogle Scholar
  26. 26.
    Jiang, Y., Perng, C.-S., Li, T., Chang, R.N.: Cloud analytics for capacity planning and instant VM provisioning. IEEE Trans Netw Serv. Manag. 10(3), 312–325 (2013)CrossRefGoogle Scholar
  27. 27.
    Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 100, 9–34 (1999)CrossRefGoogle Scholar
  28. 28.
    Akramizadeh, A., Akbarzadeh-T, M.-R., Khademi, M.: Fuzzy discrete event system modeling and temporal fuzzy reasoning in urban traffic control. In Automation Congress, 2004. Proceedings. World, vol. 16, pp. 181–186 (2004)Google Scholar
  29. 29.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)CrossRefMATHGoogle Scholar
  30. 30.
    Mohammad, G.: Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system. Cluster Comput. 18(2), 795–802 (2015)CrossRefGoogle Scholar
  31. 31.
    Hossain, M.S., Alamri, A., El Saddik, A.: A biologically inspired framework for multimedia service management in a ubiquitous environment. Concurr. Comput. Pract. Exp. 21(11), 1450–1466 (2009)Google Scholar
  32. 32.
    Hossain, M.S., Muhammad, G.: Cloud-assisted Industrial Internet of Things (IIoT)—enabled framework for health monitoring. Comput. Netw. 101(2016), 192–202 (2016)CrossRefGoogle Scholar
  33. 33.
    Hossain, M.S., Zaman, M., Muhammad, G., Ghoneim, A., Alamri, A.: Big data-driven services composition using parallel clustered particle swarm optimization in mobile environment. IEEE Trans. Serv. Comput. 9(5), 806–817 (2016)CrossRefGoogle Scholar
  34. 34.
    Hassan, M.M., et al.: Cooperative game-based distributed resource allocation in horizontal dynamic cloud collaboration platform. Inf. Syst. Frontiers 16(4), 523–542 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Amit Kumar Das
    • 1
  • Tamal Adhikary
    • 1
  • Md. Abdur Razzaque
    • 1
  • Majed Alrubaian
    • 2
  • Mohammad Mehedi Hassan
    • 2
  • Md. Zia Uddin
    • 3
  • Biao Song
    • 4
  1. 1.Green Networking Research Group, Department of Computer Science & EngineeringUniversity of DhakaDhakaBangladesh
  2. 2.College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  3. 3.Department of Computer EducationSungkyunkwan UniversitySeoulRepublic of Korea
  4. 4.Kyung Hee UniversitySuwonRepublic of Korea

Personalised recommendations