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Ant colony based graph theory (ACGT) and resource virtual network mapping (RVNM) algorithm for home healthcare system in cloud environment

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Abstract

In recent decades, new efficient heuristic algorithms are introduced which helps in alleviating both large virtual and physical networks in the case when there are a host of healthcare service providers that are part of the evaluation and analysis. Demarcating virtual resources as separate physical nodes as well as co-localization of prerequisites inherent in physical nodes is relatively time consuming. To solve this problem in this work Ant Colony based Graph Theory (ACGT) is proposed for selection of resources which eliminates infeasible mappings between fundamental and material resources available. The major aim of the study here is to provide better resource allocation mapping. This ACGT additionally in conjunction maps jointly nodes as well as links and offers the most probable optimized solutions. Algorithm here breaks-down the graph as topological sequences that are followed by ACGT to resolve mapping related issues. Any possible mapping occurs only when the virtual node capacity that is requested less in comparison than the remainder candidate physical node capacity and also when virtual link latency is comparatively greater than candidate physical path latency or that of the link. ACGT performance and its precise, heuristic and two-stage algorithms have been analyzed and studied in this cloud environment. All the methods are implemented via the use of JAVA environment and applied to google cloud.

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References

  1. Alhazmi K, Sharkh MA, Ban D, Shami A (2014) A map of the clouds: virtual network mapping in cloud computing data centers. IEEE 27th Can Conf Electr Comput Eng (CCECE): 1–6

  2. Alhazmi K, Sharkh MA, Shami A (2016) Drawing the cloud map: virtual network provisioning in distributed cloud computing data centers. IEEE Syst J: 1–12

  3. Amazon Web Services (2014) Amazon Elastic Compute Cloud (Amazon EC2), Accessed Jun. 2014. [Online]. Available: http://aws.amazon.com/fr/ec2/

  4. Bavier A, Feamster N, Huang M, Peterson L, Rexford J (2006) VINI veritas: realistic and controlled network experimentation. Proc ACM SIGCOMM: 3–14

  5. Bui M, Wang T, Jaumard B, Medhi D, Develder C (2014) Time-varying resilient virtual network mapping for multi-location cloud data centers. 16th Int Conf Transparent Optic Netw (ICTON): 1–8

  6. Cao Y, Fan W, Ma S (2016) Virtual network mapping in cloud computing: a graph pattern matching approach. Comput J 60(3):287–307

    Google Scholar 

  7. Chowdhury NMMK, Boutaba R (2010) A survey of network virtualization. Comput Netw 54(5):862–876

    Article  Google Scholar 

  8. Chowdhury M, Rahman MR, Boutaba R (2012) Vineyard: virtual network embedding algorithms with coordinated node and link mapping. IEEE/ACM Trans Netw (TON) 20(1):206–219

    Article  Google Scholar 

  9. Chu SC, Roddick JF, Pan JS (2004) Ant colony system with communication strategies. Inf Sci 167(1–4):63–76

    Article  MathSciNet  Google Scholar 

  10. Feamster N, Gao L, Rexford J (2007) How to lease the internet in your spare time. Comput Commun Rev 37(1):61–64

    Article  Google Scholar 

  11. Google Cloud Platform Home Page (2014) Accessed Jun. 2014. [Online]. Available: https: //cloud.google.com/products/app-engine

  12. Jammal M, Kanso A, Shami A (2015) Chase: component high availability-aware scheduler in cloud computing environment. Proc IEEE 8th Int Conf CLOUD Comput: 477–484

  13. Kim YK, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 30(8):1151–1171

    Article  MathSciNet  Google Scholar 

  14. Lischka J, Karl H (2009) A virtual network mapping algorithm based on subgraph isomorphism detection. In Proceedings of the 1st ACM workshop on virtualized infrastructure systems and architectures. ACM New York, NY, USA, pp. 81–88

  15. Mechtri M, Hadji M, Zeghlache D (2015) Exact and heuristic resource mapping algorithms for distributed and hybrid clouds. IEEE Trans Cloud Comput: 1–14

  16. Microsoft Azure (2014) Accessed Jun. 2014. [Online]. Available: http://azure.microsoft.com

  17. Papagianni C, Leivadeas A, Papavassiliou S, Maglaris V, Cervello-Pastor C, Monje A (2013) On the optimal allocation of virtual resources in cloud computing networks. IEEE Trans Comput 62(6):1060–1071

    Article  MathSciNet  Google Scholar 

  18. Rimal B, Choi E, Lumb I (2009) A taxonomy and survey of cloud computing systems. Proc 5th Int Joint Conf INC IMS IDC NCM: 44–51

  19. Sharkh MA, Jammal M, Shami A, Ouda A (2013) Resource allocation in a network-based cloud computing environment: design challenges. IEEE Commun Mag 51(11):46–52

    Article  Google Scholar 

  20. Stephane Z, Yves D, Christine S (2010) Solving subgraph isomorphism problems with constraint programming. Springer 15(3):327–353

  21. Sun G, Anand V, Yu HF, Liao D, Li L (2012) Optimal provisioning for elastic service oriented virtual network request in cloud computing. Global Commun Conf (GLOBECOM): 2517–2522

  22. Traces of google workloads [Online]. Available: https://code.google.com/p/googleclusterdata/

  23. Turner J, Taylor D Diversifying the internet. Proc IEEE Globecom, 2005 2, 755–760

  24. Virtual Network Embedding Simulator https://github.com/minlanyu/embed

  25. Wang C, Shanbhag S, Wolf T (2012) Virtual network mapping with traffic matrices. IEEE Int Conf Commun (ICC): 2717–2722

  26. Yu M, Yi Y, Rexford J, Chiang M (2008) Rethinking virtual network embedding: substrate support for path splitting and migration. Comput Commun Rev 38(2):17–29

    Article  Google Scholar 

  27. Zhu Y, Ammar M (2006) Algorithms for assigning substrate network resources to virtual network components. Proc IEEE INFOCOM: 1–12

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Correspondence to D. Palanikkumar.

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Palanikkumar, D., Priya, S. Ant colony based graph theory (ACGT) and resource virtual network mapping (RVNM) algorithm for home healthcare system in cloud environment. Multimed Tools Appl 79, 3743–3760 (2020). https://doi.org/10.1007/s11042-018-6908-2

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