Diffusion Model of Preemptive-Resume Priority Systems and Its Application to Performance Evaluation of SDN Switches

The increasing use of Software-Defined Networks brings the need for their performance analysis and detailed analytical and numerical models of them. The primary element of such research is a model of a SDN switch. This model should take into account non-Poisson traffic and general distributions of service times. Because of frequent changes in SDN flows, it should also analyze transient states of the queues. The method of diffusion approximation can meet these requirements. We present here a diffusion approximation of priority queues and apply it to build a more detailed model of SDN switch where packets returned by the central controller have higher priority than other packets.

Spectrum handoff analysis for multiple secondary users in cognitive radio networks

<p>The cognitive radio network permits secondary users to reach unused spectrum of primary users. In this paper, a mixed preemptive/non-preemptive resume priority M/G/1 queuing model is proposed for characterizing multiple handoff delay and to minimize the extended data delivery time of secondary users that has experienced multiple spectrum handoff during its transmission. The proposed queuing model supports delay-sensitive secondary user applications. The secondary users’ traffic is classified into two priority classes, the highest priority class for delay sensitive services and the lowest priority class for delay insensitive services. Furthermore, the proposed model assigns higher priority for the interrupted secondary users over uninterrupted secondary users for each class of secondary users in order to minimize the handoff delays for secondary users that experience multiple interruptions. Analytical formulas for the average extended data delivery time are derived for two different proactive spectrum handoff strategies (always stay and always change strategies) for both classes of secondary users. Simulation of proposed system model was performed to validate the analytical results and a good agreement was obtained. The performance of the suggested model is assessed and compared with other spectrum handoff models. Numerical results illustrate that the proposed system model outperforms existing models and can reduce the extended data delivery time for the secondary users.</p>

Machine Learning Implementation in Cognitive Radio Networks with Game-Theory Technique

Significant enhancement of spectrum utilization can be performed by means of Cognitive Radio technology. A game theory based Cognitive Radio Network with Dynamic Spectrum Allocation model is proposed in this paper. M|M|1 queuing model is implemented along with Preemptive Resume Priority for accommodation of all the cases. An Incremental Weights-Decremental Ratios (IW-DR) algorithm based on priority-based scheduling is used for supplementing this theory. Regression models are used for restructuring and improving the efficiency of the system.