Comprehensive Analysis of AggSessA C Method for Revenue Maximization Using OMNeT++

摘要

The growing demand for Internet connection of various devices with an ability to provide smarter online services and the rapid growth of mobile applications significantly increases the number of processed data flows. All the generated flows require selective and priority-based flow admission strategy. Network operators are interested in effective utilization of their infrastructure as well as in minimizing rejection probability of higher priority flows while maximizing their revenue, especially in peak hours. The existing connection admission control (CAC) schemes are largely based on serialized processing strategies of new flows without any comparison among consequent requests. However, evolution of Internet and present performance capabilities of routers allows us to offer a new approach for admission control - our developed Aggregated Session Admission Control (AggSessAC). We propose to handle service requests using a new operation paradigm of CAC, where requests are temporarily collected and processed using mutually comparisons among them, thus facilitating selectivity and ensuring network revenue maximization as well as operator gain. In order to evaluate the proposed algorithm, OMNeT++ simulation platform with the INET Framework was used and a new output queue of router has been developed including all relevant entities of proposed admission control. Simulation results are compared with conventional threshold admission control method, which only uses available link bandwidth for decision-making process and serialized flow processing strategy. The proposed method shows that selective and comparative flow control allows maximizing the number of accepted higher priority flows and is able to significantly increase the total network revenues in peak hours, compared to the standard threshold based approach. We assume that AggSessAC can be effectively used as the potential admission control mechanism in Next Generation Networks (NGN).