Energy-Optimal and Delay-Bounded Computation Offloading in Mobile Edge Computing with Heterogeneous Clouds

By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

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.