منابع:
[1] Abohamama, A. S., Alrahmawy, M. F., & Elsoud, M.A. (2018). Improving the dependability of cloud environment for hosting real time applications. Ain Shams Engineering Journal, 9(4), 3335-3346.
[2] Alharbi, F., Tian, Y. C., Tang, M., Zhang, W. Z., Peng, C., & Fei, M. (2019). An ant colony system for energy-efficient dynamic virtual machine placement in data centers. Expert Systems with Applications, 120, 228-238.
[3] Tang, M., & Pan, S. (2015). A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Processing Letters,41(2), 211-221.
[4] Zhihao Peng, Behnam Barzegar, Maryam Yarahmadi, Homayun Motameni, Poria Pirouzmand, “Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud”, Scientific Programming, vol. 2020, Article ID 8898059, 14 pages, 2020. https://doi.org/10.1155/2020/8898059
[5] 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.
[6] Behnam Barzegar, Homayun Motameni and Ali Movaghar, “EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters”, Journal of Intelligent & Fuzzy Systems 36, IOS Pres, Volume 36, Issue 6, pages 5135–5152, 2019.
[7] Yan, J., Zhang, H., Xu, H., & Zhang, Z. (2018). Discrete PSO-basedworkload ptimization in virtual machine placement. Personal and Ubiquitous Computing, 22(3), 589-596.
[8] Abdel-Basset, M., Abdle-Fatah, L., & Sangaiah, A. K.(2018). An improved Lévy based whale optimization Algorithm for bandwidth-efficient virtual machine placement in cloud computing environment.Cluster Computing,https://doi.org/10.1007/s10586-018-1769-z.
[9] Gao, Y., Guan, H., Qi, Z., Hou, Y., & Liu, L. (2013). A multi-objective ant colony system algorithm for Virtual Machine placement in cloud computing. Journal of Computer and System Sciences, 79(8), 1230-1242.
[10] Foo, Y. W., Goh, C., Lim, H. C., Zhan, Z. H., & Li,Y. (2015). Evolutionary neural network based energy consumption forecast for cloud computing. In 2015 International Conference on Cloud Computing Research and Innovation (ICCCRI) (pp. 53-64). IEEE.
[11] Wang, S., Gu, H., & Wu, G. (2013). A new approach to multi-objective virtual machine placement in virtualized data center. In 2013 IEEE Eighth International Conference on Networking, Architecture and Storage (pp. 331-335). IEEE.
[12] Wang, S., Liu, Z., Zheng, Z., Sun, Q., & Yang, F. (2013). Particle swarm optimization for energyaware virtual machine placement optimization in virtualized data centers. In 2013 International Conference on Parallel and Distributed Systems (pp. 102-109). IEEE.
[13] Alresheedi, S. S., Lu, S., Elaziz, M. A., & Ewees, A. A. (2019). Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing. Human-centric Computing and Information Sciences, 9(1), 15.
[14] Liu, X. F., Zhan, Z. H., Deng, J. D., Li, Y., Gu, T., & Zhang, J. (2018). An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Transactions on Evolutionary Computation, 22(1), 113-128.
[15] 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.
بدون دیدگاه