Smart data deduplication for telehealth systems in heterogeneous cloud computing

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 real-time 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.

Joint Resource Allocation Using Evolutionary Algorithms in Heterogeneous Mobile Cloud Computing Networks

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.