An Absorbing Markov Chain Based Model to Solve Computation and Communication Tradeoff in GPU-Accelerated MDRUs for Safety Confirmation in Disaster Scenarios
Bomin Mao, Fengxiao Tang, Zubair Md. Fadlullah, Nei Kato · IEEE Transactions on Computers · 2019
The fast increasing chip processing capacities driven by the Moore's Law have encouraged the academia and industry to consider more about general hardware architectures since they allow the repeated use for multiple purposes through the installations of applications. Some techniques utilizing the general hardware architectures have been developed to improve the flexibility of computer networks, such as the Software Defined Networking (SDN) and the Network Functions Virtualization (NFV). For these networks, the applications are required to be computation/communication-efficient since the installed applications share the hardware. In this paper, we study the resource-limited disaster recovery networks constructed by the Movable and Deployable Resource Units (MDRUs) which consist of various general computation platforms. We propose an efficient safety confirmation method through the photo sharing by the survivors. In the proposal, the Absorbing Markov Chain is utilized to model the safety confirmation process, transition matrix of which can be adopted to choose the suitable photo size for optimizing the traffic overhead and buffer consumption. Through periodical update of the photo database, unnecessary packet transmissions can be further avoided with reasonable sacrifice of the computation overhead. To expedite the computation, the GPU-accelerated MDRU is considered to conduct the matrix calculations in a parallel fashion.