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Deep Spatiotemporal Correlation Network Model for Load Prediction in Service Function Chaining

For flexible and elastic management of network function virtualization (NFV)-enabled service function chaining (SFC), the resource demands of virtual network functions (VNFs) need to be predicted appropriately. Even though several prediction schemes have been introduced in the literature, they do not work well in multiple SFC environments. To overcome this limitation, we propose a novel prediction model called the deep spatiotemporal correlation network (DSCN). DSCN learns temporal correlation in the VNF load from the past resource usage patterns. At the same time, DSCN defines upstream/downstream VNF instances across multiple SFCs and learns spatial resource usage patterns by extracting features of them. Evaluation results demonstrate that DSCN can improve the prediction accuracy by up to 13.2% and 10.9% compared to the existing models.