Modeling of OpenFlow-Based SDN Node with Taking into Account the Differences of Serving TCP and UDP Trafic Streams

OpenFlow is one of the widely used protocols controller to switch communications in software-defined networking (SDN). The queueing model that capture the communication between single switch and controller is constructed. In the model two types of traffic flows are used to take into account the differences of serving TCP and UDP traffic streams. The first flow represents the process of coming and serving TCP-packets. The corresponding flow is described by Poisson model. The second flow represents the process of coming and serving UDP-packets. The corresponding flow is also described by Poisson model. It is supposed that switch and controller have finite buffers and maximum allowed time for packets to be in the buffers is restricted. In the model it is assumed that UDP-packets have priority in occupying the switch. It means that when the process of packet servicing is about to complete in the switch, and if there are TCP and UDP packets in the buffer of the switch, the priority will be given to the UDP packets over TCP for servicing. All random variables used in the model have exponential distribution with corresponding mean values. Using the model the main performance measures of interest are given with help of values of probabilities of model's stationary states. The model and derived algorithms of characteristics calculation can be used for estimation of performance characteristics of controller to switch communications and size of buffers

Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness