A Queueing Model for Video Analytics Applications of Smart Cities

This paper aims to find a proper methodology for evaluating job scheduling strategies for a data-intensive application such as video analytics applications used for smart cities that involve edge and cloud computing. To compare two simulation methods with the analytical modeling for such evaluation, we proposed a queueing model for a system consisting of some heterogeneous edge processors and one cloud processor and compared it with a simple simulation approach. We first defined the system's characteristics and developed a queueing model for the system to calculate the edges and cloud processors' working times. We use the state-space diagram of the system to determine the set of differential equations of the system and solved them to calculate the system components' performance measures. The results show that the proposed queueing model's computational time is significantly less than other existing techniques like the simulation.