A single-hop selection strategy of VNFs based on traffic classification in NFV

Abstract

Network Function Virtualization (NFV) has become a hot technology since it provides the flexible management of network functions and efficient sharing of network resources. Network resources in NVF require an appropriate management strategy which often manifests as a difficult online decision making task. Resource management in NFV can be thought of as a process of virtualized network functions (VNFs) selection or deployment. This paper proposes a single-hop VNFs selection strategy to realize network resource management. For satisfying quality requirements of different network services, this strategy is based on the results of traffic classification which utilizes Multi-Grained Cascade Forest (gcForest) to distinguish user behaviors on the internet. In the order of VNFs, a network is divided into several layers where each arrived packet needs to queue. The scheduler of each layer selects a layer which hosts the next VNF for the packets in the queue. Experiments prove that the proposed traffic classification method increases the precision by 7.7% and improves the real-time performance. The model of VNFs selection reduces network congestion compared to traditional single-hop scheduling models. Moreover, the number of packets which fail to reach target node in time drops 30% to 50% using the proposed strategy compared to the strategy without the section of traffic classification.