Prediction of Network Performance Resilient to Measurement Failures

• Maria C. M. M. Ferreira UECE
• Silvio E. S. B. Ribeiro UECE
• Francisco V. J. Nobre UECE
• Maria L. Linhares UECE
• Thelmo P. Araújo UECE
• Rafael L. Gomes UECE

DOI: https://doi.org/10.5753/wgrs.2024.2893

Abstract

Network monitoring services are performed by several companies and Internet Providers (ISP), which provide results of regular performance tests, such as throughput, loss, and delay, among others. These measurements help to understand the behavior of the network, as well as obtain information for strategic planning. However, when carrying out the measurements planned during network monitoring, failures may occur, which makes it difficult to carry out more complex activities, such as forecasting network performance. Within this context, this article presents a resilient and adaptive model for forecasting network performance, which includes the identification of measurement failures and applying data imputation techniques to adapt the data for the forecasting process (based on Neural Networks and Time Series Analysis). The experiments carried out, using real data from the National Education and Research Network (RNP), show that the proposal can achieve high accuracy in prediction with imputed data, as well as outperforming other existing prediction approaches.

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