In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boo...In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.展开更多
The rapid growth of computational power demand from scientific,business,and Web applications has led to the emergence of cloud-oriented data centers.These centers use pay-as-you-go execution environments that scale tr...The rapid growth of computational power demand from scientific,business,and Web applications has led to the emergence of cloud-oriented data centers.These centers use pay-as-you-go execution environments that scale transparently to the user.Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment.Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective.Load classification before prediction is necessary to improve prediction accuracy.In this paper,a novel approach is proposed to forecast the future load for cloud-oriented data centers.First,a hidden Markov model(HMM) based data clustering method is adopted to classify the cloud load.The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers.Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load.With the data from this cluster,a genetic algorithm optimized Elman network is used to forecast future load.Experimental results show that our algorithm outperforms other approaches reported in previous works.展开更多
In current stage of China Industrial Transfer, how to select the leading industries is an extremely difficult decision. In this research, we study the selection problem of Jiangbei Industrial Clusters in Anhui Provinc...In current stage of China Industrial Transfer, how to select the leading industries is an extremely difficult decision. In this research, we study the selection problem of Jiangbei Industrial Clusters in Anhui Province. Total thirty-three industries can be transferred to Jiangbei Industrial Clusters, however due to limited capacity, Jiangbei Industrial Clusters can only accommodate a few of them. The difficulties of such decision include： 1） the decisions are qualitative within a complex and uncertain environment; 2） the number of alternatives is beyond the upper bound of comparing elements in general decision making methods. We introduce a new methodology-the Orders-of-Magnitude approach of the Analytic Hierarchy Process （OM-AHP） to solve such selection decision. The OM-AHP follows AHP philosophy to structure qualitative questions and uses ＂pivot＂ element to handle the situation when the alternatives become more than seven. By employing OM-AHP methodology, we found the modern logistics industry and ship ＆ marine equipment etc., are identified to be the leading industries; particularly, we recommend to give higher priority in modern logistics industry and the advanced equipment manufacturing industry, given these two industries could dramatically improve the overall comprehensive economic strength and significantly enhance competitive advantages of the Jiangbei Industrial Clusters.展开更多
基金supported by National Natural Science Foundation of China(Nos.71131002,71071045,71231004 and 71201042)
文摘In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.
基金Project(No.71131002) supported by the National Natural Science Foundation of China
文摘The rapid growth of computational power demand from scientific,business,and Web applications has led to the emergence of cloud-oriented data centers.These centers use pay-as-you-go execution environments that scale transparently to the user.Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment.Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective.Load classification before prediction is necessary to improve prediction accuracy.In this paper,a novel approach is proposed to forecast the future load for cloud-oriented data centers.First,a hidden Markov model(HMM) based data clustering method is adopted to classify the cloud load.The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers.Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load.With the data from this cluster,a genetic algorithm optimized Elman network is used to forecast future load.Experimental results show that our algorithm outperforms other approaches reported in previous works.
基金supported by National Natural Science Foundation of China(NSFC)(No.71131002,71331002)Humanities and Social Science Projects of Ministry of Education of China(NO.13YJC630051)
文摘In current stage of China Industrial Transfer, how to select the leading industries is an extremely difficult decision. In this research, we study the selection problem of Jiangbei Industrial Clusters in Anhui Province. Total thirty-three industries can be transferred to Jiangbei Industrial Clusters, however due to limited capacity, Jiangbei Industrial Clusters can only accommodate a few of them. The difficulties of such decision include： 1） the decisions are qualitative within a complex and uncertain environment; 2） the number of alternatives is beyond the upper bound of comparing elements in general decision making methods. We introduce a new methodology-the Orders-of-Magnitude approach of the Analytic Hierarchy Process （OM-AHP） to solve such selection decision. The OM-AHP follows AHP philosophy to structure qualitative questions and uses ＂pivot＂ element to handle the situation when the alternatives become more than seven. By employing OM-AHP methodology, we found the modern logistics industry and ship ＆ marine equipment etc., are identified to be the leading industries; particularly, we recommend to give higher priority in modern logistics industry and the advanced equipment manufacturing industry, given these two industries could dramatically improve the overall comprehensive economic strength and significantly enhance competitive advantages of the Jiangbei Industrial Clusters.
