Smart data deduplication for telehealth systems in heterogeneous cloud computing

Abstract

The widespread application of heterogeneous cloud computing has enabled enormous advances in the real-time performance of telehealth systems. A cloud-based telehealth system allows healthcare users to obtain medical data from various data sources supported by heterogeneous cloud providers. Employing data duplications in distributed cloud databases is an alternative approach for achieving data sharing among multiple data users. However, this approach results in additional storage space being used, even though reducing data duplications would lead to a decrease in data acquisitions and realtime performance. To address this issue, this paper focuses on developing a dynamic data deduplication method that uses an intelligent blocker to determine the working mode of data duplications for each data package in heterogeneous cloud-based telehealth systems. The proposed approach is named the SD2M (Smart Data Deduplication Model), in which the main algorithm applies dynamic programming to produce optimal solutions to minimizing the total cost of data usage. We implement experimental evaluations to examine the adaptability of the proposed approach.