Forecasting Mobile Service Demands for Anticipatory MEC

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.