With the rapid development in cloud data centers and cloud service customers,the demand for high quality cloud service has been grown rapidly.To face this reality,this paper focuses on service optimization issues in c...With the rapid development in cloud data centers and cloud service customers,the demand for high quality cloud service has been grown rapidly.To face this reality,this paper focuses on service optimization issues in cloud computing environment.First,a service-oriented architecture is proposed and programmable network facilities are utilized in it to optimize specific cloud services.Then various cloud services are categorized into two subcategories;static services and dynamic services.Furthermore,the concepts of cloud service quality and cloud resource idle rate are defined,and the aforementioned concepts have also been taken into consideration as parameters in the service optimization algorithm to improve the cloud service quality and optimize system workload simultaneously.Numerical simulations are conducted to verify the effectiveness of the proposed algorithm in balancing the workload of all servers.展开更多
This article analyzed advantages and shortages of classical load balancing algorithms based on dynamic feed-back on server cluster, and combined stimulated annealing with this strategy to put forward an optimized mode...This article analyzed advantages and shortages of classical load balancing algorithms based on dynamic feed-back on server cluster, and combined stimulated annealing with this strategy to put forward an optimized model of dynamic load balancing. This model uses stimulated annealing algorithm to calculate accurate performance parameters of load information on every service node, then estimates the actual load of nodes by dynamic feed-back, in order to insure tasks distribution reasonable. Experimental result shows that in the case of large amount of requests, this algorithm, in comparison with classical load balancing strategy of dynamic feedback, can effectively reduce response time of tasks and ensure high throughput which could improve the whole system performance.展开更多
基金Supported by the National Natural Science Foundation of China(No.61272508,61472033,61202432)
文摘With the rapid development in cloud data centers and cloud service customers,the demand for high quality cloud service has been grown rapidly.To face this reality,this paper focuses on service optimization issues in cloud computing environment.First,a service-oriented architecture is proposed and programmable network facilities are utilized in it to optimize specific cloud services.Then various cloud services are categorized into two subcategories;static services and dynamic services.Furthermore,the concepts of cloud service quality and cloud resource idle rate are defined,and the aforementioned concepts have also been taken into consideration as parameters in the service optimization algorithm to improve the cloud service quality and optimize system workload simultaneously.Numerical simulations are conducted to verify the effectiveness of the proposed algorithm in balancing the workload of all servers.
文摘This article analyzed advantages and shortages of classical load balancing algorithms based on dynamic feed-back on server cluster, and combined stimulated annealing with this strategy to put forward an optimized model of dynamic load balancing. This model uses stimulated annealing algorithm to calculate accurate performance parameters of load information on every service node, then estimates the actual load of nodes by dynamic feed-back, in order to insure tasks distribution reasonable. Experimental result shows that in the case of large amount of requests, this algorithm, in comparison with classical load balancing strategy of dynamic feedback, can effectively reduce response time of tasks and ensure high throughput which could improve the whole system performance.
