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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 original loads.
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Related In: Results  -  Collection


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fig2: Comparison of performance per watt with different original loads.

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. 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 under different original loads. In this scenario, we compare performance per watt of DPGA with ST (static threshold) which sets the utilization threshold to 0.9, IQR (interquartile range) which sets the safety parameter to 1.5, LR (local regression) which sets the safety parameter to 1.2, LRR (local regression robust) which sets the safety parameter to 1.2, and MAD (median absolute deviation) which sets the safety parameter to 2.5 [30]. As illustrated in Figure 2, DPGA placement strategy for VMs deployment under different original loads gets higher performance per watt than other placement strategies. Further, when d ≤ 1000, namely, the data center under an approximate idle state, performance per watt of DPGA placement strategy increases rapidly. When 1000 < d ≤ 2000, namely, the data center under a low loading state, performance per watt of DPGA placement strategy increases at a relatively flat rate. When 2000 < d ≤ 4000, namely, the data center under a moderate loading state, performance per watt of DPGA placement strategy is relatively stable. When d > 4000, namely, the data center under an overloading state, performance per watt of DPGA placement strategy begins to decline gradually. This is because the hosts under the state from idle to load or under the overload states consume more power than the hosts under the state of a certain load. In conclusion, DPGA has a better performance per watt and is relatively more stable because DPGA placement strategy is the heuristic approach. It takes the performance per watt as evaluation standard and tends towards stability by two step iterations.


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 original loads.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Comparison of performance per watt with different original loads.
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. 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 under different original loads. In this scenario, we compare performance per watt of DPGA with ST (static threshold) which sets the utilization threshold to 0.9, IQR (interquartile range) which sets the safety parameter to 1.5, LR (local regression) which sets the safety parameter to 1.2, LRR (local regression robust) which sets the safety parameter to 1.2, and MAD (median absolute deviation) which sets the safety parameter to 2.5 [30]. As illustrated in Figure 2, DPGA placement strategy for VMs deployment under different original loads gets higher performance per watt than other placement strategies. Further, when d ≤ 1000, namely, the data center under an approximate idle state, performance per watt of DPGA placement strategy increases rapidly. When 1000 < d ≤ 2000, namely, the data center under a low loading state, performance per watt of DPGA placement strategy increases at a relatively flat rate. When 2000 < d ≤ 4000, namely, the data center under a moderate loading state, performance per watt of DPGA placement strategy is relatively stable. When d > 4000, namely, the data center under an overloading state, performance per watt of DPGA placement strategy begins to decline gradually. This is because the hosts under the state from idle to load or under the overload states consume more power than the hosts under the state of a certain load. In conclusion, DPGA has a better performance per watt and is relatively more stable because DPGA placement strategy is the heuristic approach. It takes the performance per watt as evaluation standard and tends towards stability by two step iterations.

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