The accurate estimation of future traffic loads is a key enabler for anticipatory mobile networking. In this paper, we investigate the prediction of the traffic generated by different mobile service classes over base station clusters, at an order-of-minute granularity and using relatively short historical data. This scenario is relevant to mobile edge computing (MEC), where resources need to be orchestrated for individual services separately across multiple base stations, at fairly long timescales. To address the prediction problem, we propose a novel forecasting model based on an autoregressive multiple-input single-output (MISO) approach, where the inputs are collected from regions exhibiting strong correlations in the offered load of a specific mobile service. Experiments on real-world data collected in an operational 3G/4G network demonstrate the effectiveness of our model, which attains average relative errors between 0.4 % and 5 % when forecasting 5-minute-aggregate traffic of individual mobile service classes.

Forecasting Mobile Service Demands for Anticipatory MEC / S. Ntalampiras, M. Fiore - In: 2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)[s.l] : IEEE, 2018 Aug. - ISBN 9781538647257. - pp. 14-19 (( Intervento presentato al 19. convegno A World of Wireless, Mobile and Multimedia Networks tenutosi a Chania nel 2018 [10.1109/WoWMoM.2018.8449803].

Forecasting Mobile Service Demands for Anticipatory MEC

S. Ntalampiras
;
M. Fiore
2018

Abstract

The accurate estimation of future traffic loads is a key enabler for anticipatory mobile networking. In this paper, we investigate the prediction of the traffic generated by different mobile service classes over base station clusters, at an order-of-minute granularity and using relatively short historical data. This scenario is relevant to mobile edge computing (MEC), where resources need to be orchestrated for individual services separately across multiple base stations, at fairly long timescales. To address the prediction problem, we propose a novel forecasting model based on an autoregressive multiple-input single-output (MISO) approach, where the inputs are collected from regions exhibiting strong correlations in the offered load of a specific mobile service. Experiments on real-world data collected in an operational 3G/4G network demonstrate the effectiveness of our model, which attains average relative errors between 0.4 % and 5 % when forecasting 5-minute-aggregate traffic of individual mobile service classes.
Settore INF/01 - Informatica
ago-2018
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
38 wowmom18_traffic-prediction_camera-ready.pdf

accesso riservato

Tipologia: Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione 635.69 kB
Formato Adobe PDF
635.69 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/588052
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 8
social impact