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
Despite the rapid advances in mobile technology, many constraints still prevent mobile devices from running resource-demanding applications in mobile environments. Cloud computing with flexibility, stability and scala...Despite the rapid advances in mobile technology, many constraints still prevent mobile devices from running resource-demanding applications in mobile environments. Cloud computing with flexibility, stability and scalability enables access to unlimited resources for mobile devices, so more studies have focused on cloud computingbased mobile services. Due to the stability of wireless networks, changes of Quality of Service （QoS） level and user＇ real-time preferences, it is becoming challenging to determine how to adaptively choose the ＂appropriate＂ service in mobile cloud computing environments. In this paper, we present an adaptive service selection method. This method first extracts user preferences from a service＇s evaluation and calculates the similarity of the service with the weighted Euclidean distance. Then, they are combined with user context data and the most suitable service is recommended to the user. In addition, we apply the fuzzy cognitive imps-based model to the adaptive policy, which improves the efficiency and performance of the algorithm. Finally, the experiment and simulation demonstrate that our approach is effective.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.
基金the third level of 2011 Zhejiang Province 151 Talent Project and National Natural Science Foundation of China under Grant No.61100043
文摘Despite the rapid advances in mobile technology, many constraints still prevent mobile devices from running resource-demanding applications in mobile environments. Cloud computing with flexibility, stability and scalability enables access to unlimited resources for mobile devices, so more studies have focused on cloud computingbased mobile services. Due to the stability of wireless networks, changes of Quality of Service （QoS） level and user＇ real-time preferences, it is becoming challenging to determine how to adaptively choose the ＂appropriate＂ service in mobile cloud computing environments. In this paper, we present an adaptive service selection method. This method first extracts user preferences from a service＇s evaluation and calculates the similarity of the service with the weighted Euclidean distance. Then, they are combined with user context data and the most suitable service is recommended to the user. In addition, we apply the fuzzy cognitive imps-based model to the adaptive policy, which improves the efficiency and performance of the algorithm. Finally, the experiment and simulation demonstrate that our approach is effective.
