Virtual Machine Placement Using Multi-Objective Bat Algorithm With Decomposition in Distributed Cloud

2021 ◽  
Vol 12 (4) ◽  
pp. 62-77
Author(s):  
Arunkumar Gopu ◽  
NeelaNarayanan Venkataraman

Virtual machine placement in cloud computing considering multiple objectives is one of the significant issues in modern virtualized datacenters. Many businesses and organizations are outsourcing their computational workload to the cloud datacenters, which increases datacenter energy consumption and emission of CO2. In particular, allocating a virtual machine to a physical server in the community cloud model is even challenging due to its dynamic nature. Unlike public clouds, cloud servers are not always available in the same location. In this paper, a bio-inspired bat algorithm using decomposition (MOBA/D) is proposed to reduce three different objectives namely minimization of power consumption, minimization of network latency, and maximization of economical revenue. The performance of the proposed algorithm is compared with other multi-objective algorithms in terms of feasible solutions and execution time.

2016 ◽  
Vol 5 (4) ◽  
pp. 165-191 ◽  
Author(s):  
Boominathan Perumal ◽  
Aramudhan M.

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.


2020 ◽  
Vol 50 (8) ◽  
pp. 2370-2383 ◽  
Author(s):  
Yao Qin ◽  
Hua Wang ◽  
Shanwen Yi ◽  
Xiaole Li ◽  
Linbo Zhai

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