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

Network congestion must be managed to increase system throughput and quality of service. The existing congestion control approaches such as source throttling and rerouting focus on controlling congestion after it has already occurred. We propose a multistep Neural Network Prediction-based Routing (NNPR) protocol to predict as well as control network traffic before congestion actually happens. A distributed real time transaction processing simulator serves as the test-bed and a cloud-based scoring engine has been used to obtain results in real-time; messages are then rerouted to prevent congestion. Various parameters which can cause congestion are studied. These include bandwidth, work size, latency, max active transactions, mean arrival time and update percentage. The performance of proposed protocol is compared with existing protocols. Through experimentation, it is demonstrated that NNPR consistently provides superior performance for all congestion loads.