We analyze a city-wide dataset of 4G mobile network traffic obtained directly from user-side logs, allowing fine-grained analyses of different application services over time and space. We group applications in classes and analyze their traffic patterns: the analysis reveals great heterogeneity in the usage of different applications and in their space/time correlations, with important implications for future networking services such as network slicing and resource allocations.
The Cloud-RAN (C-RAN) paradigm is envisioned to increase the efficiency of future mobile networks by moving the computational resources needed at the Remote Radio Heads (RRH) to the cloud infrastructure. In this work, we provide a framework that optimizes the number of allocated virtual resources by considering both the computational requirements of the RRH and the Quality of Service of users, which could experience loss of service due to reassociations between the RRH and the virtual machines. The provided optimization framework is supported by data coming from a real mobile network of a middle-sized European city, which provides an estimate for the computational loads coming from the RRH. We evaluate the performance of the framework in different scenarios, analyzing the impact of different forecasting algorithms as well as different look-ahead intervals for the predictions (short-term / long-term). The results obtained by our framework can be used to assist network operators in the optimization of C-RAN resources and shed some light on the interplay between forecasting errors and overall performance.
We consider the problem of forecasting highfrequency sampled mobile cellular traffic starting from a lowerfrequency sampled time series. We use a dataset of real downlink/uplink traffic traces obtained from a mobile cellular network and apply different methodologies for performing forecasts at different sampling frequencies. Through extensive evaluation we show that such type of forecasting is possible and in some cases is also able to outperform forecast results obtained starting directly from the high frequency time series. The outcomes of this work can be used for several scenarios of cognitive networking, including prediction of data traffic requests in specific locations, as well as for data storage optimization and improvement of BBU clustering tasks.
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