🤖 AI Summary
This study addresses the joint optimization of energy efficiency and quality of service under bursty traffic in emerging scenarios such as 5G and IoT by analyzing a discrete-time queue with batch arrivals, infinite buffer capacity, two-phase service (mandatory followed by optional), and single/multiple server vacations. By constructing a bivariate probability generating function, the work fully characterizes—for the first time—the joint steady-state distribution of queue length and server status at an arbitrary time slot, including vacation termination instants, and integrates optional service with a queue-length-dependent vacation policy. Leveraging discrete-phase-type distribution modeling and sensitivity analysis, the authors derive closed-form expressions for key system performance metrics. Numerical experiments validate the analytical model and elucidate the impact of critical parameters on system behavior.
📝 Abstract
The discrete time queueing system is highly applicable to modern telecommunication systems, where it provides adaptive packet handling, congestion controlled security/inspection, energy efficient operation, and supports bursty traffic common in 5G, Internet of Things (IoT), and edge computing environments. In this article, we analyze an infinite-buffer discrete-time batch-arrival queue with single and multiple vacation policy where customers are served in batches, in two phases, namely first essential service (FES) and second optional service (SOS). In such systems, the FES corresponds to basic data processing or packet routing, while SOS represents secondary tasks such as encryption, error checking, data compression, or deep packet inspection that may not be necessary for every packet. Here, we derive the bivariate probability generating functions for the joint distribution of the number of packets waiting for transmission and the number are being processed immediately after the completion of both the FES and SOS. Furthermore, the complete joint distribution at arbitrary time slots, including vacation completion states, is established. Numerical illustrations demonstrate the applicability of the proposed framework, including an example with discrete phase type service time distribution. Finally, the sensitivity analysis of the key parameters on marginal system's probabilities and different performance measures have been investigated through several graphical representations.