Deep Learning Based Resource Allocation For Auto-Scaling VNFs

With the advancement in Software-Defined Networking (SDN), the virtualization of network functions (VNFs) (such as virtual firewalls, virtual routers, etc.) in the cloud often becomes complex to scale VNFs and to deal with reroute traffic demands to cope with the increasing dynamicity of network services. Therefore, the auto-scaling of VNFs has been receiving significant research attention. In contrast to the prior works on auto-scaling applying the reactive approach on traffic changes, in this work we propose a proactive approach using LSTM based deep learning for predicting auto-scaling of VNFs ahead of time in response to dynamic traffic variations. We model the VNF allocation problem as minimum cost VNF provisioning with prediction accuracy. The problem is proven to be NP-hard, and our proposed solution has O((TNSV)-V-2 (V logV + NlogN)(N + ElogV)) time complexity. Through both the theoretical performance analysis as well as extensive trace-driven simulation, we demonstrate that the proposed scheme can realize higher service availability while reducing the total incurred cost compared to the current approaches.