AdpVDLTS: Adaptive Spatio-Temporal VNF Placement and Load-Balanced Traffic Scheduling in Edge Computing Networks

Abstract

The rise of network function virtualization (NFV) technology has enabled virtual network functions (VNF) and service function chains (SFCs) to develop into standard paradigms for service delivery. The uncertain traffic brought about by edge computing has made it a key issue to figure out how to deploy VNFs for network load balancing. However, traditional methods are limited to SFC embedding solutions and resource management and pay less attention to traffic. To address the above issue, this paper takes into account the spatio-temporal characteristics of network traffic and the traffic scheduling after VNF deployment to solve the load balanced VNF deployment problem. We formalize the problem into an NP-hard nonlinear integer programming problem, which will be solved with the proposed algorithm adaptive VNF deployment and load balanced traffic scheduling (AdpVDLTS). AdpVDLTS divides network time into large and small time slots to operate on traffic and VNFs simultaneously, and achieves load balancing through traffic prediction and collaboration with VNF deployment. Compared with the excellent existing algorithms, AdpVDLTS can maintain more stable load balancing, higher throughput, lower deployment cost, and lower latency. In addition, the effectiveness of the traffic prediction algorithm is proved by ablation experiments.