Double-Machine-Learning-Based Resource Scheduling Method for Offloading Transfers

Abstract

Dynamically scheduling the bandwidth based on the traffic variation is important for a task offloading system. However, it faces two challenges. On one hand, the time-varying nature of the offloading traffic makes it difficult to be predicted accurately. On the other hand, differentiated mechanisms are applied to different offloading task types, which greatly complicates the behavior of the task offloading system. It is hence difficult to estimate the performance metrics accurately, especially when the metric values are extremely small. To tackle this, we present a double-machine-learning-based resource scheduling (DML-RS) method for task offloading traffic in this paper. The features of DML-RS are as follows: i) the wavelet transform and the sliding time window are incorporated with the LSTM traffic prediction model, which can capture the periodic and volatile natures of the offloading traffic and hence improve the prediction accuracy; ii) the logarithmic converting is applied to the ANN estimation models, which can improve the sensitivity of the ANN models to the small values and hence provides higher estimation accuracy. As a result, DML-RS can predict the traffic demand of the next network reconfiguration time point and optimize the resource allocation based on the performance estimations in advance. Results show that DML-RS offers near-optimal results compared with the existing method.