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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
Comparison of performance per watt with different state of idle hosts.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig4: Comparison of performance per watt with different state of idle hosts.

Mentions: In this experiment, we set the hosts number of the data center w = 1600 and the VMs number n = 10 as the requirements of users. We adjust the VMs number d as the original loads from 0 to 5000 and allocate these VMs to the hosts randomly. It represents different load levels of the data center. There are two policies to be formulated for idle hosts. The first policy is On/Off policy, wherein all idle hosts are switched off. The second policy is On/Sleep policy, wherein all idle hosts are switched to Sleep state. The experiment is designed for verifying the efficiency of DPGA in performance per watt of a cloud center with different policies for idle hosts. In this scenario, we compare performance per watt of DPGA with On/Off policy for idle hosts and DPGA with On/Sleep policy for idle hosts. As illustrated in Figure 4, DPGA placement strategy for VMs deployment with On/Sleep policy gets higher performance per watt than DPGA placement strategy with On/Off policy when the data center is under an approximate idle state. DPGA placement strategy for VMs deployment with On/Sleep policy gets approximately the same performance per watt as DPGA placement strategy with On/Off policy when the data center is under a loading state. This is because the idle hosts at Sleep state consume certain power while the turned-off idle hosts do not consume any power. Therefore DPGA placement strategy for VMs deployment is more suitable for the cloud center under a loading state.


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

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

Comparison of performance per watt with different state of idle hosts.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Comparison of performance per watt with different state of idle hosts.
Mentions: In this experiment, we set the hosts number of the data center w = 1600 and the VMs number n = 10 as the requirements of users. We adjust the VMs number d as the original loads from 0 to 5000 and allocate these VMs to the hosts randomly. It represents different load levels of the data center. There are two policies to be formulated for idle hosts. The first policy is On/Off policy, wherein all idle hosts are switched off. The second policy is On/Sleep policy, wherein all idle hosts are switched to Sleep state. The experiment is designed for verifying the efficiency of DPGA in performance per watt of a cloud center with different policies for idle hosts. In this scenario, we compare performance per watt of DPGA with On/Off policy for idle hosts and DPGA with On/Sleep policy for idle hosts. As illustrated in Figure 4, DPGA placement strategy for VMs deployment with On/Sleep policy gets higher performance per watt than DPGA placement strategy with On/Off policy when the data center is under an approximate idle state. DPGA placement strategy for VMs deployment with On/Sleep policy gets approximately the same performance per watt as DPGA placement strategy with On/Off policy when the data center is under a loading state. This is because the idle hosts at Sleep state consume certain power while the turned-off idle hosts do not consume any power. Therefore DPGA placement strategy for VMs deployment is more suitable for the cloud center under a loading state.

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