Graph Transformer and LSTM Attention for VNF Multi-Step Workload Prediction in SFC

The knowledge on a Service Function Chain’s (SFC’s) resource requirements is an indispensable prerequisite for proactive resource provisioning and run-time management of the SFC. However, due to the intrinsic dynamics in network environment, accurate resource requirements and workloads prediction for the Virtual Network Functions (VNFs) of a SFC, especially in a large time scale, is a non-trivial challenge. In the literature, existing works largely neglect the application-level relationship of VNFs in improving prediction accuracy, and few work investigates the multi-step prediction. In this work, we propose a deep-learning-based multi-step prediction model for accurate workload prediction for SFC VNFs in a dynamic network environment. We first demonstrate that predictability can be improved by taking into account application-level dependency by calculating the spatial conditional entropy of adjacent VNFs workloads. Then, the prediction model, named Graph Transformer Networks and sequence-to-sequence LSTM with Attention (GTN-LA) is introduced, which utilizes the Graph Transformer as the encoder to capture the application-level dependencies among VNFs, and the LSTM with attention as the decoder to extract the temporal dependencies within the time varying load information. Finally, GTN-LA is validated through intensive evaluation with a real SFC workload dataset by comparing towards several baselines.