تعداد نشریات | 50 |

تعداد شمارهها | 2,232 |

تعداد مقالات | 20,474 |

تعداد مشاهده مقاله | 25,107,218 |

تعداد دریافت فایل اصل مقاله | 22,717,426 |

## An optimal VM Placement in Cloud Data Centers Based on Discrete Chaotic Whale Optimization Algorithm | ||

Journal of Advances in Computer Engineering and Technology | ||

دوره 6، شماره 4 - شماره پیاپی 24، بهمن 2020، صفحه 201-212 اصل مقاله (934.47 K) | ||

نوع مقاله: Original Research Paper | ||

نویسندگان | ||

mohammad masdari ^{} ^{1}؛ sasan Gharehpasha^{1}؛ ahmad jafarian^{2}
| ||

^{1}Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran. | ||

^{2}Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran | ||

چکیده | ||

Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Energy efficiency in data centers has become a hot topic in recent years as more and larger data centers have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in server consolidation. In the past few years, many approaches to virtual machine placement have been proposed, but existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines. In this paper, we proposed a new approach for placement based on Discrete Chaotic whale optimization Algorithm. First goal of our presented algorithm is reducing the energy consumption in datacenters by decreasing the number of active physical machines. Second goal is decreasing waste of resources and management of them using optimal placement of virtual machines on physical machines in cloud data centers. By using the method, the increase in migration of virtual machines to physical machines is prevented. Finally, our proposed algorithm is compared to some algorithms in this area like FF, ACO, MGGA, GSA, and FCFS. | ||

کلیدواژهها | ||

Power Consumption؛ Resource management؛ Virtualization؛ Whale Optimization Algorithm | ||

مراجع | ||

[1] R. Bao, "Performance evaluation for traditional virtual machine placement algorithms in the cloud," in International Conference on Internet of Vehicles, 2016: Springer, pp. 225-231. [2] M. Masdari, S. ValiKardan, Z. Shahi, and S. I. Azar, "Towards workflow scheduling in cloud computing: a comprehensive analysis," Journal of Network and Computer Applications, vol. 66, pp. 64-82, 2016. [3] K. Braiki and H. Youssef, "Multi-objective virtual machine placement algorithm based on particle swarm optimization," in 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), 2018: IEEE, pp. 279-284. [4] S. K. Addya, A. K. Turuk, B. Sahoo, M. Sarkar, and S. K. Biswash, "Simulated annealing based VM placement strategy to maximize the profit for Cloud Service Providers," Engineering science and technology, an international journal, vol. 20, no. 4, pp. 1249-1259, 2017. [5] M. Masdari, S. Barshande, and S. Ozdemir, "CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs," The Journal of Supercomputing, vol. 75, no. 11, pp. 7174-7208, 2019. [6] Y. Qin, H. Wang, F. Zhu, and L. Zhai, "A multi-objective ant colony system algorithm for virtual machine placement in traffic intense data centers," IEEE access, vol. 6, pp. 58912-58923, 2018. [7] M. Masdari and M. Zangakani, "Green cloud computing using proactive virtual machine placement: challenges and issues," Journal of Grid Computing, pp. 1-33, 2019. [8] S. Gharehpasha, M. Masdari, and A. Jafarian, "The Placement of Virtual Machines Under Optimal Conditions in Cloud Datacenter," Information Technology and Control, vol. 48, no. 4, pp. 545-556, 2019. [9] C. Sonklin, M. Tang, and Y.-C. Tian, "A decrease-and-conquer genetic algorithm for energy efficient virtual machine placement in data centers," in 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), 2017: IEEE, pp. 135-140. [10] A. Al-Moalmi, J. Luo, A. Salah, and K. Li, "Optimal virtual machine placement based on grey wolf optimization," Electronics, vol. 8, no. 3, p. 283, 2019. [11] S. Y. Rashida, M. Sabaei, M. M. Ebadzadeh, and A. M. Rahmani, "A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment," Cluster Computing, pp. 1-40, 2019. [12] L. Hong and G. Yufei, "GACA-VMP: Virtual machine placement scheduling in cloud computing based on genetic ant colony algorithm approach," in 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015: IEEE, pp. 1008-1015. [13] R. Asemi, E. Doostsadigh, M. Ahmadi, and H. T. Malazi, "Energy efficieny in virtual machines allocation for cloud data centers using the imperialist competitive algorithm," in 2015 IEEE Fifth International Conference on Big Data and Cloud Computing, 2015: IEEE, pp. 62-67. [14] M. Masdari and M. Jalali, "A survey and taxonomy of DoS attacks in cloud computing," Security and Communication Networks, vol. 9, no. 16, pp. 3724-3751, 2016. [15] S. E. Dashti and A. M. Rahmani, "Dynamic VMs placement for energy efficiency by PSO in cloud computing," Journal of Experimental & Theoretical Artificial Intelligence, vol. 28, no. 1-2, pp. 97-112, 2016. [16] S. Mirjalili and A. Lewis, "The whale optimization algorithm," Advances in engineering software, vol. 95, pp. 51-67, 2016. [17] E. Parvizi and M. H. Rezvani, "Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach," Cluster Computing, pp. 1-23, 2020. [18] S. Azizi and D. Li, "An energy-efficient algorithm for virtual machine placement optimization in cloud data centers," Cluster Computing, pp. 1-14, 2020. [19] M. J. Moghaddam, A. Esmaeilzadeh, M. Ghavipour, and A. K. Zadeh, "Minimizing virtual machine migration probability in cloud computing environments," Cluster Computing, pp. 1-10, 2020. [20] F. Abdessamia, W.-Z. Zhang, and Y.-C. Tian, "Energy-efficiency virtual machine placement based on binary gravitational search algorithm," Cluster Computing, pp. 1-12, 2019. [21] M. K. Gupta, A. Jain, and T. Amgoth, "Power and resource-aware virtual machine placement for IaaS cloud," Sustainable Computing: Informatics and Systems, vol. 19, pp. 52-60, 2018. | ||

آمار تعداد مشاهده مقاله: 182 تعداد دریافت فایل اصل مقاله: 294 |