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Abstract

Although the real-time migration of virtual machines brings about multi-functional management, the service performance will decline to a certain extent during the migration process, so shorter downtime and total migration time are very desirable. The traditional load balancing algorithm reflect the load information of each physical machine, but it does not take into account the actual case of virtual machine migration. Therefore, contraposing to the features of cloud computing, the destination node positioning strategy of cloud computing is proposed. It periodically acquires physical node’s CPU, memory, bandwidth usage and request connection and perform quantification with unified standard. Then, with computation of factors of virtual machine migration the comprehensive load value is determined finally. The approach measures the utilization rate and transfer cost of node computing resources, which can locate target nodes reasonably and efficiently, so it ensures the reasonable and efficient migration of virtual machines and relative load balancing under cloud computing environments.

Keywords

Load balancing cloud computing VMM comprehensive measure migration

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References

Awad

A.I.

El-Hefnawy

N.A.

and Abdel_Kader

H.M.

, Enhanced particle swarm optimization for task scheduling in cloud computing environments, Procedia Computer Science65 (2015), 920–929.

Gopinath

P.P.G.

and Vasudevan

S.K.

, An in-depth analysis and study of load balancing techniques in the cloud computing environment, Procedia Computer Science50 (2015), 427–432.

Devi

D.C.

and Uthariaraj

V.R.

, Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks, Scientific World Journal3 (2016), 1–14.

Yang

Wang

and Wang

, Load balancing algorithm of task scheduling in cloud computing environment based on honey bee behavior, Journal of Computer Applications31(1) (2015), 638–645.

Jin

Liu

Zhang

et al., Load balancing and task scheduling strategy for the cloud computing environments, Journal of Computational Information Systems11(2) (2015), 769–781.

Cai

Jian

et al., An adaptive approach to better load balancing in a consumer-centric cloud environment, IEEE Transactions on Consumer Electronics62(3) (2016), 243–250.

Peng

Ming

et al., A load balancing method for massive data processing under cloud computing environment, Intelligent Automation & Soft Computing23(4) (2017), 1–7.

A novel load balancing strategy based on node load comprehensive measuring under cloud computing environment

Abstract

Keywords

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References