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A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

Dong YS, Xu GC, Fu XD - ScientificWorldJournal (2014)

Bottom Line: To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization.Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well.The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China.

ABSTRACT
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

Show MeSH
Distributed parallel genetic algorithm of VMs placement.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig1: Distributed parallel genetic algorithm of VMs placement.

Mentions: From the view of users, cloud center should select the physical hosts with more remaining resources to load the VMs with the objective of improving the QoS. From the view of cloud operators, cloud center should improve the utilization rates of resources and decrease the energy costs that aim at reducing the operating costs. Taken together, we assign the performance per watt to evaluation standard, namely, maximizing performance as well as minimizing energy costs. As shown in Figure 1, the idea of DPGA is divided into two stages. In the first stage, genetic algorithm is executed in parallel on g selected physical hosts. We select initial populations dispersedly and averagely by a certain step size in solution space for these physical hosts. Selection process chooses the solution vectors according to the probability which is proportional to the fitness value. Then the algorithm crosses the selected solution vectors and mutates the crossed solution vectors in the direction conducive to the fitness value. After crossover and mutation process, the algorithm iterates the first stage until it meets the iterative terminal conditions. In the second stage, the algorithm collects the solutions obtained from each selected physical host in the first stage, and then it executes the genetic algorithm again as in the first stage with collected solutions as initial population.


A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

Dong YS, Xu GC, Fu XD - ScientificWorldJournal (2014)

Distributed parallel genetic algorithm of VMs placement.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4109368&req=5

fig1: Distributed parallel genetic algorithm of VMs placement.
Mentions: From the view of users, cloud center should select the physical hosts with more remaining resources to load the VMs with the objective of improving the QoS. From the view of cloud operators, cloud center should improve the utilization rates of resources and decrease the energy costs that aim at reducing the operating costs. Taken together, we assign the performance per watt to evaluation standard, namely, maximizing performance as well as minimizing energy costs. As shown in Figure 1, the idea of DPGA is divided into two stages. In the first stage, genetic algorithm is executed in parallel on g selected physical hosts. We select initial populations dispersedly and averagely by a certain step size in solution space for these physical hosts. Selection process chooses the solution vectors according to the probability which is proportional to the fitness value. Then the algorithm crosses the selected solution vectors and mutates the crossed solution vectors in the direction conducive to the fitness value. After crossover and mutation process, the algorithm iterates the first stage until it meets the iterative terminal conditions. In the second stage, the algorithm collects the solutions obtained from each selected physical host in the first stage, and then it executes the genetic algorithm again as in the first stage with collected solutions as initial population.

Bottom Line: To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization.Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well.The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China.

ABSTRACT
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

Show MeSH