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 Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

2018 ◽  
Vol 22 (1) ◽  
pp. 113-128 ◽  
Author(s):  
Xiao-Fang Liu ◽  
Zhi-Hui Zhan ◽  
Jeremiah D. Deng ◽  
Yun Li ◽  
Tianlong Gu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Energy Efficient ◽  
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.

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