Collaborative Fuzzy Clustering Approach for Scientific Cloud Workflows

Abstract

Cloud computing has allowed the sharing of applications with a lot of data, like scientific workflows. Using scientific workflows to process big data is expensive regarding data transfer, execution time, and bandwidth costs. A data placement method based on fuzzy sets is used to cut these costs. It helps optimize data placement and reduce the costs of processing big data. This paper presents a new method for scientific cloud workflow data placement involving fuzzy sets to realize collaborative clustering. The proposed method explores each data center's datasets through data dependencies, clusters them by the clustering algorithm Fuzzy C-Means (FCM), and re-clusters them based on the data collaboration. Our suggested method of using fuzzy sets to realize collaborative clustering can help cope with uncertainties in data and thus reduce the overall data placement amounts, with better results than previous approaches.