Intelligent Network Monitoring System Using an ISP Central Points of Presence

Abstract

The proliferation of both internet usage and users have been remarkably increased due to certain situations that influenced face-to-face communications, which in turn have created high pressure on Internet Service Providers (ISPs). This research mainly aims to boost ISP services by conducting near real-time analysis for customer’s behavior movements based on their score of central Points of Presence (POP). In addition, this study focuses on establishing special Recurrent Artificial Intelligence (RNN) architecture to make daily sales predictions based on various central POPs. The process utilizes different RNN architectures, Long Short Time Memory (LSTM) and Gated Recurrent Unit (GRU), and compares them in order to make smart scoring measurements for customers’ high-dimensional data. As a result, it can be concluded that LSTM architecture has achieved much better Mean squared Error (MSE) than GRU architecture. LSTM outperforms GRU in forecasting less sensitive outliers, with an average Mean Absolute Error (MAE) of 1.354 for LSTM and 1.554 for GRU. Additionally, LSTM performs better in forecasting outliers, with an average MSE of 3.592 compared to GRU’s average of 4.8. Thereafter, the obtained results are merged over private Application Programming Interface (API) and monitored over smart reports. Eventually, the outcomes of this research can be summarized in providing several benefits for customers such as increasing internet performance, reaching promised speed, and shortening activation times. ISP-related benefits such as gaining reputation, promoting sales, and reducing customers’ negative support tickets can be achieved as well.