Performance Analysis of an IoT Service Platform with Retrial Requests, Working Vacations, Balking and Feedback

The increasing deployment of Internet of Things (IoT) service platforms has motivated the development of queueing models capable of capturing congestion, retrials, and service interruptions. This paper investigates an M/M/1 retrial queue with balking and feedback during working vacations, where a central processing unit or gateway handles requests from a large number of smart devices. Customer arrivals are modeled as a Poisson process and receive immediate service when the server is idle, and otherwise either balk or join an orbit from which customers retry for service after a random time interval. The server enters a vacation whenever the system becomes empty and provides service at a reduced rate during working vacations. In addition, after the completion of service during a working vacation, customers may either leave the system or rejoin the orbit, representing feedback. Probability generating function (PGF) techniques are employed to analyze the system’s steady-state performance, and explicit expressions for important performance measures are obtained. Numerical experiments are presented to illustrate and validate the analytical findings. The results provide insights into the impact of working vacations, balking, and feedback on system performance and demonstrate the applicability of the proposed model to IoT-based service environments.