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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 actual performance per watt and theoretical performance per watt.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig5: Comparison of actual performance per watt and theoretical performance per watt.

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 500 to 5000 and allocate these VMs to the hosts randomly. It represents different load levels of the data center. All idle hosts are switched to Sleep state. In this scenario, we compare actual performance per watt of DPGA with theoretical performance per watt of DPGA calculated by formula (5). As illustrated in Figure 5, theoretical performance per watt is higher than actual performance per watt when the data center is under a low loading state. Theoretical performance per watt is approximately the same as actual performance per watt when the data center is under a moderate loading state. Theoretical performance per watt is lower than actual performance per watt when the data center is under an overloading state. This is because the hosts under a moderate loading state can calculate a relatively more accurate value of power consumption by DVFS formula than the hosts under a low loading state or an overloading state. In conclusion, DPGA placement strategy for VMs deployment is more suitable for the cloud center under a moderate 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 actual performance per watt and theoretical performance per watt.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Comparison of actual performance per watt and theoretical performance per watt.
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 500 to 5000 and allocate these VMs to the hosts randomly. It represents different load levels of the data center. All idle hosts are switched to Sleep state. In this scenario, we compare actual performance per watt of DPGA with theoretical performance per watt of DPGA calculated by formula (5). As illustrated in Figure 5, theoretical performance per watt is higher than actual performance per watt when the data center is under a low loading state. Theoretical performance per watt is approximately the same as actual performance per watt when the data center is under a moderate loading state. Theoretical performance per watt is lower than actual performance per watt when the data center is under an overloading state. This is because the hosts under a moderate loading state can calculate a relatively more accurate value of power consumption by DVFS formula than the hosts under a low loading state or an overloading state. In conclusion, DPGA placement strategy for VMs deployment is more suitable for the cloud center under a moderate 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