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


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.


Big media healthcare data Cloud confederation Multiple linear regression Quality-of-service Fuzzy logic 



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).


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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

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