Prediction and preemptive control of network congestion in distributed real-time environment

Due to ever increasing demand for network capacity, the congestion problem is inflating. Congestion results in queuing within the network, packet loss and increased delays. It should be controlled to increase the system throughput and quality of service. The existing congestion control approaches such as source throttling and re-routing focus on controlling congestion after it has already happened. However, it is much more desirable to predict future congestion based on the current state and historical data, so that efficient controlling techniques can be applied to prevent congestion from happening in future. We have proposed a Neural Network Prediction-based routing (NNPR) protocol to predict as well as control the network traffic in distributed real time environment. A distributed real time transaction processing simulator (DRTTPS) has been used as the test-bed. For predictions, multi-step neural network model is developed in SPSS Modeler, which predicts congestion in future. ADAPA (Adaptive Decision and Predictive Analytics) scoring engine has been used for real-time scoring. An ADAPA wrapper calls the prediction model through web services and predicts the congestion in real-time. Once predicted results are obtained, messages are re-routed to prevent congestion. To compare our proposed work with existing techniques, two routing protocols are also implements "" Dijkstra's Shortest Path (DSP) and Routing Information Protocol (RIP). The main metric used to analyze the performance of our protocol is the percentage of transactions which complete before their deadline. The NNPR protocol is analyzed with various simulation runs having parameters both inside and outside the neural network input training range. Various parameters which can cause congestion were studied. These include bandwidth, worksize, latency, max active transactions, mean arrival time and update percentage. Through experimentation, it is observed that NNPR consistently outperforms DSP and RIP for all congestion loads. --Leaves [i]-ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b214474