Open Access
ITM Web Conf.
Volume 54, 2023
2nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
Article Number 01017
Number of page(s) 8
Section Computing
Published online 04 July 2023
  1. Koomey, J. (2011). Growth in data center electricity use 2005 to 2010. A report by Analytical Press, completed at the request of The New York Times, 9(2011), 161. [Google Scholar]
  2. 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. [CrossRef] [Google Scholar]
  3. Baliga, J., Ayre, R. W., Hinton, K., & Tucker, R. S. (2010). Green cloud computing: Balancing energy in processing, storage, and transport. Proceedings of the IEEE, 99 (1), 149–167. [Google Scholar]
  4. You, X., Li, Y., Zheng, M., Zhu, C., & Yu, L. (2017). A survey and taxonomy of energy efficiency relevant surveys in cloud-related environments. IEEE Access, 5, 14066–14078. [CrossRef] [Google Scholar]
  5. Mishra, S. K., Khan, M. A., Sahoo, S., & Sahoo, B. (2019). Allocation of energy-efficient task in cloud using DVFS. International Journal of Computational Science and Engineering, 18(2), 154–163. [CrossRef] [Google Scholar]
  6. Katal, A., Dahiya, S., & Choudhury, T. (2022). Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing, 1–31 [Google Scholar]
  7. L.A. Barroso and U. Holzle, “The case of energy-proportional computing”, in Computer, vol. 40, 2007, pp. 33–37. [CrossRef] [Google Scholar]
  8. Chaurasia, N., Kumar, M., Chaudhry, R., & Verma, O. P. (2021). Comprehensive survey on energy-aware server consolidation techniques in cloud computing. The Journal of Supercomputing, 77, 11682–11737. [CrossRef] [Google Scholar]
  9. Clark, C., Fraser, K., Hand, S., Hansen, J. G., Jul, E., Limpach, C., … & Warfield, A. (2005, May). Live migration of virtual machines. I. Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation-Volume 2 (pp. 273–286). [Google Scholar]
  10. Beloglazov, A., & Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 24(13), 1397–1420. [CrossRef] [Google Scholar]
  11. Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 28(5), 755–768. [CrossRef] [Google Scholar]
  12. Pinheiro, E., Bianchini, R., Carrera, E. V., & Heath, T. (2001). Load balancing and unbalancing for power and performance in cluster-based systems. Rutgers University. [Google Scholar]
  13. Fu, X., & Zhou, C. (2015). Virtual machine selection and placement for dynamic consolidation in Cloud computing environment. Frontiers of Computer Science, 9, 322–330. [CrossRef] [Google Scholar]
  14. Taheri, M. M., & Zamanifar, K. (2011, December). 2-phase optimization method for energy aware scheduling of virtual machines in cloud data centers. I. 2011 International Conference for Internet Technology and Secured Transactions (pp. 525–530). IEEE. [Google Scholar]
  15. Li, L., Dong, J., Zuo, D., & Wu, J. (2019). SLA-aware and energy-efficient VM consolidation in cloud data centers using robust linear regression prediction model. IEEE Access, 7, 9490–9500. [CrossRef] [Google Scholar]
  16. Buyya, R., & Murshed, M. (2002). Gridsim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and computation: practice and experience, 14(13-15), 1175–1220. [CrossRef] [Google Scholar]
  17. Chun, B., Culler, D., Roscoe, T., Bavier, A., Peterson, L., Wawrzoniak, M., & Bowman, M. (2003). Planetlab: an overlay testbed for broad-coverage services. ACM SIGCOMM Computer Communication Review, 33(3), 3–12. [CrossRef] [Google Scholar]

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