T-For: An Adaptable Forecasting Model for Throughput Performance

Network monitoring services are performed by several companies and Internet Service Providers (ISPs), which provide results of regular performance tests, where throughput is one of the most essential information. However, the monitoring tools still need to evolve in order to encompass more complex a...

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Bibliographic Details
Published in:IEEE eTransactions on network and service management 2024-06, Vol.21 (3), p.2791-2801
Main Authors: Portela, Ariel L. C., Ribeiro, Silvio E. S. B., Menezes, Rafael A., Araujo, Thelmo P. de, Gomes, Rafael L.
Format: Article
Language:English
Subjects:
Adaptation models
Data models
Forecasting
Forecasting model
Monitoring
network performance
neural network
Neural networks
Performance tests
Predictive models
Quality of service
Throughput
throughput measurement
Time measurement
Time series analysis
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Summary:Network monitoring services are performed by several companies and Internet Service Providers (ISPs), which provide results of regular performance tests, where throughput is one of the most essential information. However, the monitoring tools still need to evolve in order to encompass more complex activities, such as forecasting. Within this context, this paper presents a Throughput performance Forecasting model (called T-For), based on Neural Networks and Time Series Analysis, which estimates future network performance in specific time periods, according to past throughput measurements. The experiments, using real data from the National Education and Research Network (RNP), show that the proposed model outperformed the existing approaches, reaching high levels of forecast accuracy.
ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2024.3349701