scholarly journals Adaptive Evaluation of Virtual Machine Placement and Migration Scheduling Algorithms Using Stochastic Petri Nets

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 79810-79824 ◽  
Author(s):  
Ying Liu ◽  
Ke Wang ◽  
Liang Ge ◽  
Lei Ye ◽  
Jingde Cheng
2018 ◽  
Vol 111 ◽  
pp. 222-250 ◽  
Author(s):  
Manoel C. Silva Filho ◽  
Claudio C. Monteiro ◽  
Pedro R.M. Inácio ◽  
Mário M. Freire

Author(s):  
Thuan Hong Duong-Ba ◽  
Thinh Nguyen ◽  
Bella Bose ◽  
Tuan Tho Tran

2019 ◽  
Vol 8 (2) ◽  
pp. 6464-6468

Current techniques for Virtual machine placement in cloud data center is avoiding multiple resources due to its complexity and hardness of the problem. Due to this each Virtual Machine Placement increased overall frequency of server consolidation and migration. In this paper, we have overcome these limitations by providing local search-based unified approximation framework which utilized multiple resources of server and reduced the frequency of server consolidation and migration. The framework is evaluated on Azure cloud data center benchmark data sets and it has surpassed existing methods with improvement by 32% in overall virtual machine placement


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