A new metaheuristic‐based method for solving the virtual machines migration problem in the green cloud computing

2021 ◽  
Author(s):  
Yanfei Xu ◽  
Karlo Abnoosian
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Green Cloud Computing ◽  
Green Cloud

scholarly journals Dynamic Placement of Virtual Machines with Both Deterministic and Stochastic Demands for Green Cloud Computing

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wenying Yue ◽  
Qiushuang Chen
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Greenhouse Gas Emission ◽  
Multidimensional Space ◽  
It Services ◽  
Green Cloud Computing ◽  
Stochastic Demands ◽  
Space Partition ◽  
Basic Set ◽  
Green Cloud

Cloud computing has come to be a significant commercial infrastructure offering utility-oriented IT services to users worldwide. However, data centers hosting cloud applications consume huge amounts of energy, leading to high operational cost and greenhouse gas emission. Therefore, green cloud computing solutions are needed not only to achieve high level service performance but also to minimize energy consumption. This paper studies the dynamic placement of virtual machines (VMs) with deterministic and stochastic demands. In order to ensure a quick response to VM requests and improve the energy efficiency, a two-phase optimization strategy has been proposed, in which VMs are deployed in runtime and consolidated into servers periodically. Based on an improved multidimensional space partition model, a modified energy efficient algorithm with balanced resource utilization (MEAGLE) and a live migration algorithm based on the basic set (LMABBS) are, respectively, developed for each phase. Experimental results have shown that under different VMs’ stochastic demand variations, MEAGLE guarantees the availability of stochastic resources with a defined probability and reduces the number of required servers by 2.49% to 20.40% compared with the benchmark algorithms. Also, the difference between the LMABBS solution and Gurobi solution is fairly small, but LMABBS significantly excels in computational efficiency.

scholarly journals Shadow Replication: An Energy-Aware, Fault-Tolerant Computational Model for Green Cloud Computing

Energies ◽  
2014 ◽  
Vol 7 (8) ◽  
pp. 5151-5176 ◽  
Author(s):  
Xiaolong Cui ◽  
Bryan Mills ◽  
Taieb Znati ◽  
Rami Melhem
Keyword(s):  
Cloud Computing ◽  
Computational Model ◽  
Fault Tolerant ◽  
Energy Aware ◽  
Green Cloud Computing ◽  
Green Cloud

scholarly journals Green Cloud Computing: A Review

2017 ◽  
Vol 167 (9) ◽  
pp. 5-7
Author(s):  
Reem I. ◽  
Rahaf S. ◽  
Fatima S. ◽  
Sara A. ◽  
Hemalatha M.
Keyword(s):  
Cloud Computing ◽  
Green Cloud Computing ◽  
Green Cloud
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