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Efficient Resource Management on Cloud Using Energy and Power Aware Dynamic Migration (EPADM) of VMs

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Abstract

In present decade, cloud computing provides utility-based IT services to the global consumers. According to pay-by-use manner, it facilitates hosting of persistent services from the user, business and technical fields. But, it is to be mentioned that the data centers hosting the cloud-based services utilize large amount of energy, power and resources. Hence, there is a need of an efficient resource management model for cloud that involves in reducing the resource consumption and computational cost. And, for managing the virtual resources with respect to the varying demands in cloud environment, dynamic virtual resource management is required. With that concern, this paper presents a model called Energy and Power Aware Dynamic Migration (EPADM). Based on the model design, the main objectives such as, efficient resource mapping and provisioning algorithms are presented. The dynamic Virtual Migration (VM) operation comprises the VM relocation and consolidation parts for achieving desirable results. Moreover, the paper also concentrates on reducing the SLA (Service Level Agreement) based violation, which is a significant factor to be considered on cloud. The proposed EPADM model is evaluated using the CloudSim toolkit. The results illustrate that the proposed model has massive potential than others, as it provides energy-power efficiency, reduced SLA violations under distinctive workload cases.

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References

  1. Voorsluys, W., Broberg, J., Venugopal, S., & Buyya, R. (2009). Cost of virtual machine live migration in clouds: a performance evaluation. In: Proceedings of the 1st international conference on cloud computing, CloudCom 2009. Beijing: Springer.

  2. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., et al. (2009). A view of cloud computing. Communications of the ACM, 53(4), 50–58.

    Article  Google Scholar 

  3. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616.

    Article  Google Scholar 

  4. Younge, A. J., Henschel, R., Brown, J. T., von Laszewski, G., Qiu, J., & Fox, G. C. (2011). Analysis of virtualization technologies for high performance computing environments. In: IEEE international conference on cloud computing (CLOUD). IEEE, 2011, pp. 9–16.

  5. Lenk, A., Klems, M., Nimis, J., Tai, S., & Sandholm, T. (2009). What’s inside the cloud? an architectural map of the cloud landscape. In: Proceedings of the 2009 ICSE workshop on software engineering challenges of cloud computing. IEEE Computer Society, 2009, pp. 23–31.

  6. Buyya, R., Garg, S. K., & Calheiros, R. N. (2011). Sla-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions. In: IEEE international conference on cloud and service computing (CSC), 2011, pp. 1–10.

  7. Shiraz, M., Gani, A., Shamim, A., Khan, S., & Ahmad, R. W. (2015). Energy efficient computational offloading framework for mobile cloud computing. Journal of Grid Computing, 13(1), 1–18.

    Article  Google Scholar 

  8. Datta, D., Mishra, S., & Rajest, S. S. (2020). Quantification of tolerance limits of engineering system using uncertainty modeling for sustainable energy. International Journal of Intelligent Networks, 1, 1–8.

    Article  Google Scholar 

  9. Cardosa, M., Korupolu, M. R., & Singh, A. (2009). Shares and utilities based power consolidation in virtualized server environments. In: IFIP/IEEE international symposium integrated network management, IM’09, pp. 327–334.

  10. Speitkamp, B., & Bichler, M. (2010). A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Transactions on Services Computing, 3(4), 266–278.

    Article  Google Scholar 

  11. Stillwell, M., Schanzenbach, D., Vivien, F., & Casanova, H. (2010). Resource allocation algorithms for virtualized service hosting platforms. Journal of Parallel and Distributed Computing, 70(9), 962–974.

    Article  Google Scholar 

  12. Bobroff, N., Kochut, A., & Beaty, K. (2007). Dynamic placement of virtual machines for managing SLA violations. In: 10th IFIP/IEEE international symposium integrated network management, 2007. IM’07, pp. 119–128.

  13. Khanna, G., Beaty, K., Kar, G., & Kochut, A. (2006). Application performance management in virtualized server environments. In: 10th IEEE/IFIP network operations and management symposium, 2006. NOMS 2006, pp. 373–381.

  14. Kumar, K. V., Jayasankar, T., Eswaramoorthy, V., & Nivedhitha, V. (2020). SDARP: Security based data aware routing protocol for ad hoc sensor networks. International Journal of Intelligent Networks, 1, 36–42.

    Article  Google Scholar 

  15. Gmach, D., Rolia, J., Cherkasova, L., Belrose, G., Turicchi, T., & Kemper, A. (2008). An integrated approach to resource pool management: Policies, efficiency and quality metrics. In: IEEE international conference dependable systems and networks with FTCS and DCC, 2008, pp. 326–335.

  16. Foster, G., Keller, G., Tighe, M., Lutfiyya, H., & Bauer, M. (2013). The right tool for the job: Switching data centre management strategies at runtime. In: IFIP/IEEE international symposium integrated network management (IM 2013), pp. 151–159.

  17. Xiao, Z., Song, W., & Chen, Q. (2013). Dynamic resource allocation using virtual machines for cloud computing environment. EEE Transactions on Parallel and Distributed Systems, 24(6), 1107–1117.

    Article  Google Scholar 

  18. Elnozahy, E., Kistler, M., & Rajamony, R. (2003). Energy-efficient server clusters. Power-aware computer systems 2003, pp. 179–197.

  19. Chase, J. S., Anderson, D. C., Thakar, P. N., Vahdat, A. M., & Doyle, R. P. (2001). Managing energy and server resources in hosting centers. In: Proceedings of the 18th ACM symposium on operating systems principles (pp. 103–116). New York: ACM.

  20. Pinheiro, E., Bianchini, R., Carrera, E. V., & Heath, T. (2001). Load balancing and unbalancing for power and performancee in cluster-based systems. In: Proceedings of the workshop on compilers and operating systems for low power, pp. 182–195.

  21. Nathuji, R., & Schwan, K. (2007). Virtualpower: coordinated power management in virtualized enterprise systems. ACM SIGOPS Operating Systems Review, 41(6), 265–278.

    Article  Google Scholar 

  22. Raghavendra, R., Ranganathan, P., Talwar, V., Wang, Z., & Zhu, X. (2008). No ‘‘power’’ struggles: coordinated multi-level power management for the data center. SIGARCH Computer Architecture News, 36(1), 48–59.

    Article  Google Scholar 

  23. Marzolla, M., Babaoglu, O., & Panzieri, F. (2011). Server consolidation in clouds through gossiping. In: Proceedings of 2011 IEEE international symposium on a world of wireless, mobile and multimedia networks (WoWMoM’11), Lucca, Italy, June 2011, pp. 1–6.

  24. Nzanywayingoma, F., & Yang, Y. (2017). Efficient resource management techniques in cloud computing environment: A review and discussion. Telkomnika, pp. 1917–1933.

  25. Sudha, M. R., Sumathi, C. P., & Saravanakumar, A. (2019). An optimal energy consumption based resource management in mobile cloud computing. International Journal of Recent Technology and Engineering, 8(24), 103–109.

    Google Scholar 

  26. Dhanapal, R., Akila, T., Shuja Hussain, S., & Mavaluru, D. (2019). A Cost-aware method for tasks allocation on the internet of things by grouping the submitted tasks. Journal of Internet Technology, 20(7), 2055–2062.

    Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, Sri Krishna College of Technology, Coimbatore, Tamilnadu, 641042, India

    V. Roopa

  2. Department of Electrical & Electronics Engineering, Kumaraguru College of Technology, Coimbatore, Tamilnadu, 641049, India

    K. Malarvizhi

  3. Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, Tamilnadu, 641035, India

    S. Karthik

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  1. V. Roopa
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  2. K. Malarvizhi
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Correspondence to V. Roopa.

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Roopa, V., Malarvizhi, K. & Karthik, S. Efficient Resource Management on Cloud Using Energy and Power Aware Dynamic Migration (EPADM) of VMs. Wireless Pers Commun 117, 3327–3342 (2021). https://doi.org/10.1007/s11277-020-07990-z

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