Reduct Algorithm Based Execution Times Prediction in Knowledge Discovery Cloud Computing Environment

Kun Gao, Qin Wang, and Lifeng Xi
Computer and Information Technology College, Zhejiang Wanli University, China

Abstract: Cloud environment is a complex system which includes the matching between computation resources and data resources. Efficient predicting tasks 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 heuristic reduct algorithm is based on frequencies of attributes appeared in discernibility matrix. We also propose to add dynamic information about the performances of various knowledge discovery tools over specific data sources to the Cloud Computing Environment for supporting the prediction. This information can be added as additional metadata stored in Cloud environment. Experimental result validates our solution that rough sets provide a formal framework for the problem of application execution time prediction in Cloud environment.

Keywords: Distributed computing, cloud computing, knowledge discovery, rough set.

 

Received April 25 2012; accepted January 17, 2013

  

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