Journal of Medical Systems

, 43:37 | Cite as

Brain Storm Optimization Graph Theory (BSOGT) and Energy Resource Aware Virtual Network Mapping (ERVNM) for Medical Image System in Cloud

  • D. PalanikkumarEmail author
  • S. Priya
Mobile & Wireless Health
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health


With the development of Internet and the make use of Internet for medical information, the demand for huge scale and reliable managing medical information has brought out the huge scale Internet data centers. This work that has been presented here highlights the structural lay out and formulation of the medical information model. The aim of presenting this to aid medical departments as well as workers to exchange information and integrate available resources that help facilitate the analysis to be conducted on the given information. Software here comprises of medical information and offers a comprehensive service structure that benefits medical data centers. VNM or Virtual Network Mapping (VNM) essentially relates to substrate network that involves the installation and structuring of on demand virtual machines. These however are subjective to certain limitations that are applicable in relation to latency, capacity as well as bandwidth. Data centers need to dynamically handle cloud workloads effectively and efficiently. Simultaneously, since the mapping of virtual and physical networks with several providers’ consumes more time along with energy. In order to resolve this issue, VNM has been mapped by making use of Graph Theory (GT) matching, a well-studied database topic. (i) Brain Storm Optimization Graph Theory (BSOGT) is introduced for modeling a virtual network request in the form of a GT with different resource constraints, and the substrate networks here is considered being a graph. For this graph the nodes and edges comprise of attributes that indicate their constraints. (ii) The algorithm that has been recently introduced executes graph decomposition into several topology patterns. Thereafter the BSOGT is executed to solve any issues that pertain to mapping. (iii) The model that has been presented here, ERVNM and the BSOGT are used with a specific mapping energy computation function.(iv) Issues pertaining to these are categorized as being those related to virtual network mapping as the ACGT and optimal solution are drawn by using effective integer linear programming. ACGT, pragmatic approach, as well as the precise and two-stage algorithms performance is evaluated by means of cloud Simulator environment. The results obtained from simulation indicate that the BSOGT algorithm attains the objectives of cloud service providers with respect to Acceptance ratio, mapping percentage, processing time as well as Convergence Time.


Virtual Network Mapping (VNM) Brain Storm Optimization Graph Theory (BSGOT) Virtualization quality of services (Qos) Distributed cloud computing and optimization 


Compliance with ethical standards

Conflict of interest

The Authors and Co-Authors have no conflicts of Interests. The Paper is not submitted to any other Journals.

Ethical approval (involving human participants and/or animals)

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.


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

Authors and Affiliations

  1. 1.Department of Information & TechnologyDr. NGP Institute of TechnologyCoimbatoreIndia
  2. 2.Department of Computer Science & EngineeringCoimbatore Institute of TechnologyCoimbatoreIndia

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