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Fuzzy Dynamic Load Balancing in Virtualized Data Centers of SaaS Cloud Provider

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Cloud computing provides a robust infrastructure that can facilitate computing power as a utility service. All the virtualized services are made available to end users in a pay-as-you-go basis. Serving user requests using distributed network of Virtualized Data Centers is a challenging task as response time increases significantly without a proper load balancing strategy. As the parameters involved in generating load in the Virtualized Data Center has imprecise effect on the overall load of Virtual Machine, a fuzzy load balancing strategy is required. This paper proposes two efficient fuzzy load balancing methods - Fuzzy Active Monitoring Load Balancer (FAM-LB) and Fuzzy Throttled Load Balancer (FT-LB) for the distributed SaaS cloud provider. The authors implemented a cloud model in simulation environment and compared the results of otheir novel approach with the existing techniques. Among them FT-LB has provided better performance compared to other scheduling algorithms.
Title: Fuzzy Dynamic Load Balancing in Virtualized Data Centers of SaaS Cloud Provider
Description:
Cloud computing provides a robust infrastructure that can facilitate computing power as a utility service.
All the virtualized services are made available to end users in a pay-as-you-go basis.
Serving user requests using distributed network of Virtualized Data Centers is a challenging task as response time increases significantly without a proper load balancing strategy.
As the parameters involved in generating load in the Virtualized Data Center has imprecise effect on the overall load of Virtual Machine, a fuzzy load balancing strategy is required.
This paper proposes two efficient fuzzy load balancing methods - Fuzzy Active Monitoring Load Balancer (FAM-LB) and Fuzzy Throttled Load Balancer (FT-LB) for the distributed SaaS cloud provider.
The authors implemented a cloud model in simulation environment and compared the results of otheir novel approach with the existing techniques.
Among them FT-LB has provided better performance compared to other scheduling algorithms.

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