ScholarWorks@숙명여자대학교

초록

With the development of cloud computing technology, many scientists want to perform their experiments in cloud environments. Because of the pay-per-use method, it is cost-optimal for scientists to only pay for the cloud services needed for their experiments. However, selection of suitable resources is difficult because they are composed of various characteristics. Therefore, a method of classification is needed to effectively take advantage of cloud resources. Static classification of a resource can derive inaccurate results, while scientists submit various experiment intentions and requirements. Thus, a dynamic resource-clustering method is needed to accurately determine application characteristics and scientists requirements. A cost-effective resource recommendation service is also needed. In this paper, a resource-clustering analysis, which considers application characteristics, and a cost-effective recommendation method in a hybrid cloud environment are proposed. The resource clustering analysis applies a self-organizing map and the k-means algorithm to cluster similar resources dynamically. In addition, the cost-effective resource recommendation method applies an efficiency metric based on application-aware resource clustering. Performance is verified by comparing the proposed clustering method with other studies resource classification methods. Results show that the proposed method can classify similar resource cluster reflecting application characteristics and recommend cost-effective resources.

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