RT Conference Proceedings
T1 Forecasting for Network Management with Joint Statistical Modelling and Machine Learning
A1 Lo Schiavo, Leonardo
A1 Fiore, Marco
A1 Gramaglia, Marco
A1 Banchs Roca, Albert
A1 Costa-Pérez, Xavier
AB Forecasting is a task of ever increasing importance for the operation of mobile networks, where it supports anticipa tory decisions by network intelligence and enables emerging zero touch service and network management models. While current trends in forecasting for anticipatory networking lean towards the systematic adoption of models that are purely based on deep learning approaches, we pave the way for a different strategy to the design of predictors for mobile network environments. Specifically, following recent advances in time series prediction, we consider a hybrid approach that blends statistical modelling and machine learning by means of a joint training process of the two methods. By tailoring this mixed forecasting engine to the specific requirements of network traffic demands, we develop a Thresholded Exponential Smoothing and Recurrent Neural Network (TES-RNN) model. We experiment with TES RNN in two practical network management use cases, i.e., (i) anticipatory allocation of network resources, and (ii) mobile traffic anomaly prediction. Results obtained with extensive traffic workloads collected in an operational mobile network show that TES-RNN can yield substantial performance gains over current state-of-the-art predictors in both applications considered
PB IEEE
SN 978-1-6654-0876-9
YR 2022
FD 2022-06-14
LK https://hdl.handle.net/10016/35948
UL https://hdl.handle.net/10016/35948
LA eng
NO This work is partially supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no.101017109 DAEMON. This work is partially supported by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D 6GCLARION-OR and AEON-ZERO. The authors would like to thank Dario Bega for his contribution to developing the forecasting use case I, and Slawek Smyl for his feedback on the baseline ES-RNN model.
DS e-Archivo
RD 17 jul. 2024