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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.

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Comparison of performance per watt with different user requests.
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Related In: Results  -  Collection


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fig3: Comparison of performance per watt with different user requests.

Mentions: In this experiment, we set the hosts number of the data center w = 1600 and the VMs number d = 3000 as the original loads. Then we allocate these VMs to the hosts randomly. We adjust the VMs number n as the requirements of users from 10 to 50. 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 requirements of users. In this scenario, we compare performance per watt of DPGA with ST, IQR, LR, LRR, and MAD that take the same parameters as the experiment in Section 4.1. As illustrated in Figure 3, DPGA placement strategy for VMs deployment with different requirements of users gets higher performance per watt than other placement strategies. Further, with the increase of the requirements of users, DPGA placement strategy for VMs deployment gets more stable performance per watt than other placement strategies.


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 user requests.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Comparison of performance per watt with different user requests.
Mentions: In this experiment, we set the hosts number of the data center w = 1600 and the VMs number d = 3000 as the original loads. Then we allocate these VMs to the hosts randomly. We adjust the VMs number n as the requirements of users from 10 to 50. 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 requirements of users. In this scenario, we compare performance per watt of DPGA with ST, IQR, LR, LRR, and MAD that take the same parameters as the experiment in Section 4.1. As illustrated in Figure 3, DPGA placement strategy for VMs deployment with different requirements of users gets higher performance per watt than other placement strategies. Further, with the increase of the requirements of users, DPGA placement strategy for VMs deployment gets more stable performance per watt than other placement strategies.

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