Reduct algorithm based execution times prediction in knowledge discovery cloud computing environment

Cloud environment is a complex system which includes the matching between computation resources and data resources. Efficient predicting services execution time is a key component of successful tasks scheduling and resource allocation in cloud computing environment. In this paper, we propose a framework for supporting knowledge discovery application running in cloud environment as well as a holistic approach to predict the application execution times. We use rough sets theory to determine a reduct and then compute the execution time prediction. The spirit of the algorithm, which we proposed comes from the number of attributes within a given discernibility matrix. We also propose to join dynamic data related to the performances of various knowledge discovery services in the cloud computing environment for supporting the prediction. This information can be joined as additional metadata stored in cloud environment. Experimental result verifies that the proposed algorithm in this paper supply a general solution for the problem of web service execution time prediction in