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Cloud Computing Resource Dynamic Optimization Considering Load Energy Balancing Consumption

Journal from gdlhub / 2016-11-05 04:42:41
Oleh : Lao Zhihong, Larisa Ivascu, Telkomnika
Dibuat : 2016-06-01, dengan 1 file

Keyword : Scheduling of virtual resource; Cloud environment; Genetic algorithm; Load balancing
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4344

The difficulties of the existing of load balancing and optimal span are under the cloud environment; for this reason, intelligent optimizing strategy of virtual resources scheduling is designed in this paper; which fully takes into account advantages of cloud virtual resource, improves the selection and cross processing in GA, takes the optimal span and load function as the double fitness function to make the efficiency of resources improved obviously. the resource can be obtained by this scheme from cluster of cloud service to fully ensure it in load balancing condition. At the same time, CloudSim is used to make simulate test in this paper; test research indicates that scheduling efficiency can be promoted and the load balancing can be improved obviously by this method at the same time under the conditions of large-scale tasks, which indicates that this method has great effectiveness.

Deskripsi Alternatif :

The difficulties of the existing of load balancing and optimal span are under the cloud environment; for this reason, intelligent optimizing strategy of virtual resources scheduling is designed in this paper; which fully takes into account advantages of cloud virtual resource, improves the selection and cross processing in GA, takes the optimal span and load function as the double fitness function to make the efficiency of resources improved obviously. the resource can be obtained by this scheme from cluster of cloud service to fully ensure it in load balancing condition. At the same time, CloudSim is used to make simulate test in this paper; test research indicates that scheduling efficiency can be promoted and the load balancing can be improved obviously by this method at the same time under the conditions of large-scale tasks, which indicates that this method has great effectiveness.

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