Optimizing Resource Scaling in Network Slicing

Abstract

Network slicing is a key technology in 5G and beyond networks to meet the diverse requirements of various applications. The resource usage forecasting in network slicing plays an important role in helping network operators scale network slices up and down accurately and timely to avoid Service Level Agreement (SLA) violations signed with network tenants. Therefore, we propose a Long Shot-term memory (LSTM)-based forecasting algorithm utilizing multivariate time series data to predict the future resource usage of virtual machines (VMs) in network slices. Through comprehensive experiments, our proposed algorithm outperforms other state-of-the-arts forecasting algorithms only processing univariate time series data in both short-term prediction and long-term prediction to help network operator reduce the costs of SLA violation and resource overprovisioning.