Distributed Real-time Forecasting Framework for IoT Network and Service Management

Abstract

For efficient network management, it is important to monitor and analyse the data, particularly big data applications based on time series, in terms of trends and correlations, to predict network problems and be able to react preventively. Machine learning techniques can help but, given the amount and complexity of the algorithms and metrics available, the use of these techniques is laborious and requires specialized knowledge. This paper proposes a framework for distributed real-time time series forecasting with the goal to make predictions for various dynamic systems simultaneously and provide straightforward horizontal scaling, increased modularity, high robustness and a simple interface for users. Moreover, the proposed framework also enables the creation of ensemble algorithms, combining the results of multiple predictors, without changes on each individual predictor component. To demonstrate the functionalities of our framework, we show how simultaneous predictions can be made about the number of Internet sessions, using a real data stream from users of the buses in the Porto city.