Abstract
This paper presents an innovative prediction method for key performance indexes with multiple seasonal profiles. The proposed method, called Multiplicative Multi-Seasonal Model (MSMM) relies on a time series decomposition including multiple multiplicative seasonal profiles and a trend component. The method and its underlying model have been specifically designed to be computationally lightweight to scale to big-data scenarios envisaged in upcoming 5G-NFV environments. The MSMM performance has been evaluated on KPI traces of real operating infrastructures/services, made available by Yahoo! The obtained results outlined how the MSMM prediction method provides more accurate forest than well-known algorithm like the seasonal version of ARIMA, with much reduced computational weight.
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Acknowledgment
This work was supported by the INPUT (In-Network Programmability for next-generation personal cloUd service supporT) project, funded by the European Commission under the Horizon 2020 Programme (Grant no. 644672).
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Bruschi, R., Burgarella, G., Lago, P. (2017). A Lightweight Prediction Method for Scalable Analytics of Multi-seasonal KPIs. In: Piva, A., Tinnirello, I., Morosi, S. (eds) Digital Communication. Towards a Smart and Secure Future Internet. TIWDC 2017. Communications in Computer and Information Science, vol 766. Springer, Cham. https://doi.org/10.1007/978-3-319-67639-5_6
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