Network Traffic Prediction Using Long Short-Term Memory
Shyam Nihale, Shantanu Sharma, Lokesh Parashar, Upendra Singh
Abstract
Computer network traffic control is a torrid research topic nowadays, as this task helps in various applications like anomaly detection, congestion control and bandwidth control. Different machine learning techniques are used for this purpose earlier, such as autoregressive integrated moving averages (ARIMA), recurrent neural network (RNN), etc. Here a framework on long short term neural network is proposed for network traffic prediction. The proposed framework makes use of real network traces from TIER-1 ISP. These traces are used to make the predictions from the proposed framework that uses Long Short Term Model (LSTM). The aim is to generate the predictions at very short time scales (<; 30seconds). As there is diversity in the network traffic, a feature-based clustering framework is employed to work as the preprocessing stage to cluster similar time series together. The results state that LSTM can be used for the prediction of network traffic with low errors.