scholarly journals A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement in Traffic Intense Data Centers

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 58912-58923 ◽  
Author(s):  
Yao Qin ◽  
Hua Wang ◽  
Fangjin Zhu ◽  
Linbo Zhai
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Ant Colony System ◽  
Multi Objective

scholarly journals A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

2013 ◽  
Vol 79 (8) ◽  
pp. 1230-1242 ◽  
Author(s):  
Yongqiang Gao ◽  
Haibing Guan ◽  
Zhengwei Qi ◽  
Yang Hou ◽  
Liang Liu
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Ant Colony System ◽  
Multi Objective

scholarly journals An Ant Colony System for energy-efficient dynamic Virtual Machine Placement in data centers

2019 ◽  
Vol 120 ◽  
pp. 228-238 ◽  
Author(s):  
Fares Alharbi ◽  
Yu-Chu Tian ◽  
Maolin Tang ◽  
Wei-Zhe Zhang ◽  
Chen Peng ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Ant Colony System

A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement

2016 ◽  
Vol 5 (4) ◽  
pp. 165-191 ◽  
Author(s):  
Boominathan Perumal ◽  
Aramudhan M.
Keyword(s):  
Ant Colony Optimization ◽  
Virtual Machine ◽  
Bin Packing ◽  
Virtual Machines ◽  
Transition Probability ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Placement Problem ◽  
Multi Objective

In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.

scholarly journals Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Shanchen Pang ◽  
Kexiang Xu ◽  
Shudong Wang ◽  
Min Wang ◽  
Shuyu Wang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Virtual Machine ◽  
User Experience ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Local Optimum ◽  
Placement Method

Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users’ experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience.

scholarly journals A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

2020 ◽  
Vol 128 ◽  
pp. 106390
Author(s):  
Ennio Torre ◽  
Juan J. Durillo ◽  
Vincenzo de Maio ◽  
Prateek Agrawal ◽  
Shajulin Benedict ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Cloud Data ◽  
Multi Objective ◽  
Cloud Data Centers
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