A personalized trustworthy service selection method is proposed to fully express the features of trust, emphasize the importance of user preference and improve the trustworthiness of service selection. The trustworthi...A personalized trustworthy service selection method is proposed to fully express the features of trust, emphasize the importance of user preference and improve the trustworthiness of service selection. The trustworthiness of web service is defined as customized multi-dimensional trust metrics and the user preference is embodied in the weight of each trust metric. A service selection method combining AHP （analytic hierarchy process） and PROMETHEE （preference ranking organization method for enrichment evaluations） is proposed. AHP is used to determine the weights of trust metrics according to users＇ preferences. Hierarchy and pairwise comparison matrices are constructed. The weights of trust metrics are derived from the highest eigenvalue and eigenvector of the matrix. to obtain the final rank of candidate services. The preference functions are defined according to the inherent characteristics of the trust metrics and net outranking flows are calculated. Experimental results show that the proposed method can effectively express users＇ personalized preferences for trust metrics, and the trustworthiness of service ranking and selection is efficiently improved.展开更多
Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,...Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.展开更多
基金The National Natural Science Foundation of China(No.60973149)the Open Funds of State Key Laboratory of Computer Science of the Chinese Academy of Sciences(No.SYSKF1110)+1 种基金the Doctoral Fund of Ministry of Education of China(No.20100092110022)the College Industrialization Project of Jiangsu Province(No.JHB2011-3)
文摘A personalized trustworthy service selection method is proposed to fully express the features of trust, emphasize the importance of user preference and improve the trustworthiness of service selection. The trustworthiness of web service is defined as customized multi-dimensional trust metrics and the user preference is embodied in the weight of each trust metric. A service selection method combining AHP （analytic hierarchy process） and PROMETHEE （preference ranking organization method for enrichment evaluations） is proposed. AHP is used to determine the weights of trust metrics according to users＇ preferences. Hierarchy and pairwise comparison matrices are constructed. The weights of trust metrics are derived from the highest eigenvalue and eigenvector of the matrix. to obtain the final rank of candidate services. The preference functions are defined according to the inherent characteristics of the trust metrics and net outranking flows are calculated. Experimental results show that the proposed method can effectively express users＇ personalized preferences for trust metrics, and the trustworthiness of service ranking and selection is efficiently improved.
基金ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. The research has been partly supported by National Natural Science Foundation of China No. 61272528 and No. 61034005, and the Central University Fund （ID-ZYGX2013J073）.
文摘Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.
