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RETRACTED ARTICLE: DAVmS: Distance Aware Virtual Machine Scheduling approach for reducing the response time in cloud computing

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This article was retracted on 14 February 2024

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Abstract

The cloud services can be received at anytime from anywhere based on the need of the customers. According to the necessity of the customers, the virtualization of technologies is applied to deliver the cloud services accurately. A large amount of data transfers from user to host and hosts to the user, in cloud environment. To pin the virtual machine on an appropriate host and transferring the data is a challenging task. This paper explains DAVmS: Distance Aware Virtual Machine Scheduling Algorithm, which is applied to arrange virtual machines according to its capability, and pin the VMs to the nearest physical host for accessing the cloud services from the adjacent data center of the customer. This introduced virtual machine scheduling algorithm is used to reduce propagation time and enhance the execution process to reduce the response time. The main purpose of the introduced DAVmS: Distance Aware Virtual Machine Scheduling Algorithm is to deliver an enhanced virtual machine provision policy to physical hosts and access the services from the adjacent data center.

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References

  1. Kruekaew B, Kimpan W (2014) Virtual machine scheduling management on cloud computing using artificial bee colony. In: Proceedings of the international multiconference of engineers and computer scientists 2014, vol I, ISBN: 978-988-19252-5-1, ISSN: 2078-0958, (Print), ISSN: 2078-0966 (online)

  2. Singh H, Gangwar RC (2014) Comparative study of load balancing algorithms in cloud environment. Int J Recent Innovat Trends Comput Commun 2(10): 3195–3199, ISSN: 2321-8169

  3. Himthani Puneet, Saxena Amit, Manoria Manish (2015) Comparative analysis of VM scheduling algorithms in cloud environment. Int J Comput Appl 120(6):1–6

    Google Scholar 

  4. Li J, Li D, Zheng J, Quan Y (2014) Location-aware multi-user resource allocation in distributed clouds. Adv Comput Architect Commun Comput Inf Sci 451:152–162, Print ISBN: 978-3-662-44490-0, Online ISBN: 978-3-662-44491-7

  5. Liu C (2016) A load balancing aware virtual machine live migration algorithm. In: 4th international conference on sensors, measurement and intelligent materials (ICSMIM 2015), Atlantis Press, vol 1, pp 370–373

  6. Parthasarathy P, Vivekanandan S (2020) Internet of Things (IOT) in healthcare-smart health and surveillance, architectures, security analysis and data transfer: a review. International Journal of Software Innovation (IJSI) 7(2):21–40

    Google Scholar 

  7. Reguri VR, Kogatam S, Moh M (2016) Energy efficient traffic-aware virtual machine migration in green cloud data centers. In: 2016 Ieee 2nd international conference on big data security on cloud (BigDataSecurity), IEEE international conference on high performance and smart computing and international conference on intelligent data and security (IDS), vol 1, E-ISBN: 978-1-5090-2403-2, pp 268–273

  8. Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA (2019) Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement

  9. Basha AA, Vivekanandan S, Parthasarathy P (2019) Blood glucose regulation for post-operative patients with diabetics and hypertension continuum: a cascade control-based approach. J Med Syst 43(4):95

    Article  PubMed  Google Scholar 

  10. Justy Mirobi G, Arockiam L (2020) EWS: an efficient workflow scheduling algorithm for the minimization of response time in cloud environment. In: Proceeding of the international conference on computer networks, big data and ioT (ICCBI–2019), vol. 1, Issue: March 2020, ISSN: 978-3-030-43191-4, pp. 799–810

  11. Parthasarathy P, Vivekanandan S (2018) Investigation on uric acid biosensor model for enzyme layer thickness for the application of arthritis disease diagnosis. Health information science and systems 6(1):5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dou W, Xu X, Meng S, Zhang X, Hu C, Yu S, Yang J (2017) An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data. Published online in Wiley Online Library(wileyonlinelibrary.com), vol: 29, pp 1–20

  13. Ebrahim Dashti S, Rahmani AM (2016) Dynamic VMs placement for energy efficiency by PSO in cloud computing. J Experim Theoret Artif Intell vol 28(1–2), Advances and Applications of Swarm Intelligence 2016, pp 97–112

  14. Aadkane Trapti, Monga Sandeep (2017) An energy efficient cost aware virtual machine migration approach for the cloud environment. International Research Journal of Advanced Engineering and Science 2(2):332–336

    Google Scholar 

  15. Thanasias V, Lee C, Hanif M, Kim E, Helal S, (2016) VM capacity-aware scheduling within budget constraints in iaas clouds. PLoS ONE 2016, 11(8): 1–21, ISSN: 1932-6203

  16. Tandon S, Kesarwani C, Srivastava P, Suryan A. Swathi JN (2017) Network aware virtual machine migration by pso optimization. Int J Res Appl Sci Eng Technol 5(5): 548–556, ISSN: 2321-9653

  17. Ilkhechi AR, Korpeoglu I, Ulusoy O (2015) Network-aware virtual machine placement in cloud data centers with multiple traffic-intensive components. Comput Netw 91(14): 508–527

  18. Rocha LA, Verdi FL (2015) A network-aware optimization for VM placement. In: 2015 IEEE 29th international conference on advanced information networking and applications, Issue: March 2015, Electronic ISBN: 978-1-4799-7905-9, CD-ROM ISBN: 978-1-4799-7904-2, pp. 619–625

  19. Afoulki Z, Bousquet A, Jonathan (2011) A security-aware scheduler for virtual machines on IaaS clouds. Eng Technol Comput Sci, pp 1–12

  20. Jo I, Jung IY, Yeom HY (2011) Workload-aware VM scheduling on multicore systems. Int J Comput Sci Eng 3(11): 3634–3644, ISSN: 0975-3397

  21. Rathor VS, Pateriya RK, Gupta RK (2015) An Efficient virtual machine scheduling technique in cloud computing environment. I.J. Modern Educ Comput Sci 7: 39–46, , ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

  22. Varadharajan ., Priyan MK, Panchatcharam P, Vivekanandan S, Gunasekaran M (2018) A new approach for prediction of lung carcinoma using back propagation neural network with decision tree classifiers. J Ambient Intell Human Comput, pp 1–12

  23. Oludele Awodele, Ogu Emmanuel C, Shade Kuyoro, Chinecherem Umezuruike (2014) On the evolution of virtualization and cloud computing: a review. Journal of Computer Sciences and Applications 2014:40–43

    Article  Google Scholar 

  24. Kaur N, Nagpal P (2017) An efficient virtual machine migration algorithm based on artificial intelligence. Int J Emerg Trends Technol Comput Sci 6(5): 178-183, ISSN: 2278-6856

  25. Parthasarathy P, Vivekanandan S (2018) A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Informatics in Medicine Unlocked 12:143–147

    Article  Google Scholar 

  26. Dinesh K, Zahid Raza (2015) A PSO based VM resource scheduling model for cloud computing. In: 2015 IEEE international conference on computational intelligence & communication technology, pp 213–219, ISBN: 978-1-4799-6023-1

  27. Mathan K, Kumar PM, Panchatcharam P, Manogaran G, Varadharajan R (2018) A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Design automation for embedded systems, pp 1–18

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Authors and Affiliations

  1. Department of Computer Science, Christopher Arts and Science College (Women), Soorangudi, Nanguneri, Tamil Nadu, 627108, India

    G. Justy Mirobi

  2. Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, 641 046, India

    G. Justy Mirobi

  3. Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli, Tamil Nadu, 620 002, India

    L. Arockiam

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  1. G. Justy Mirobi
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  2. L. Arockiam
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Correspondence to G. Justy Mirobi.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11227-024-05970-9"

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Mirobi, G.J., Arockiam, L. RETRACTED ARTICLE: DAVmS: Distance Aware Virtual Machine Scheduling approach for reducing the response time in cloud computing. J Supercomput 77, 6664–6675 (2021). https://doi.org/10.1007/s11227-020-03563-w

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