Network traffic volume estimation and prediction is an important research topic that attracts persistent attention from the networking community and the machine learning community. Although there has been extensive work on estimating or predicting the traffic matrix using time series models, low rank matrix decomposition et. al, to the best of our knowledge, there is few work investigating the problem whether we are able to estimate and predict the traffic volume based on some statistics of the traffic which are much less costly to collect, for example, the flow counts. In this paper, we propose to model the relationship between the traffic volume and simple statistics about flows using a Hidden Markov Model based on which we can avoid direct measurement of the traffic volume but instead we estimate and predict the hidden traffic volume based on those simple flow statistics which are collected by some sketch techniques. We demonstrate the feasibility and effectiveness of our proposed method using some semi-simulation and real data experimental results.

With the growth of mobile data and the increasing density of base stations, mobility management in future cellular networks will face great challenges. One such challenge is the high frequency of handovers. With high-frequency handovers, packet delay caused by forwarding in handovers cannot be ignored any more. In this paper, we propose a novel data prefetching strategy to reduce handover delays in cellular networks. The strategy is deployed on software-defined cellular networks, taking advantage of centralized computing capability and packet path control. According to the prediction of a user's next location, we prefetch data packets to possible target base stations, so that the delay caused by packet forwarding in handovers can be reduced. The numerical results show that the data prefetching strategy performs very well. The average delay of the whole user group has a dramatic decrease. We also study the impacts of different parameters on system performance in this paper.