Mobile traffic forecasting using a combined FFT/LSTM strategy in SDN networks

Abstract

Over the last few years, networks' infrastructures are experiencing a profound change initiated by Software Defined Networking (SDN) and Network Function Virtualization (NFV). In such networks, avoiding the risk of service degradation increasingly involves predicting the evolution of metrics impacting the Quality of Service (QoS), in order to implement appropriate preventive actions. Recurrent neural networks, in particular Long Short Term Memory (LSTM) networks, already demonstrated their efficiency in predicting time series, in particular in networking, thanks to their ability to memorize long sequences of data. In this paper, we propose an improvement that increases their accuracy by combining them with filters, especially the Fast Fourier Transform (FFT), in order to better extract the characteristics of the time series to be predicted. The proposed approach allows improving prediction performance significantly, while presenting an extremely low computational complexity at run-time compared to classical techniques such as Auto-Regressive Integrated Moving Average (ARIMA), which requires costly online operations.

A novel group management scheme of clustered federated learning for mobile traffic prediction in mobile edge computing systems

“…Basically, since traffic prediction requires obtaining the temporal dependence that appears in the time-series data set; long short-term memory (LSTM) is usually used due to proper recognition of sequential patterns [11], [22], [23]. Also, [24], proposed the use of data pre-processing before injecting them into an LSTM neural network for time series prediction. Even though, FL was presented to allow model training in distributed manner when data usage is limited to the local domain without data…”

“…LSTM method in mobile traffic [11], [22], [23] Use the proper recognition of sequential patterns. [24] Use data pre-processing before injecting them into an LSTM neural network for time series prediction. Federated meta-learning algorithm [25] To achieve efficient mobile traffic prediction at the edge they introduced a modelagnostic meta-learning (MAML) algorithm based on the FL framework.…”

“…Finally, the first round ends and we send the chromosomes to the second round to create a new generation (lines [18][19][20][21]. This general process will be iterated until the maximum number of the optimal fitness value for chromosomes (optimal association rule) is reached (lines [22][23][24].…”