Elastic Virtual Network Function Orchestration Policy Based on Workload Prediction

Abstract

By separating network functions from hardware-dependent middleboxes, network function virtualization (NFV) is expected to lead to significant cost reduction and the flexibility improvement in network management. Elastic orchestration of virtual network functions (VNF) is a key factor to achieve NFV goals. However, most existing VNF orchestration researches are limited to offline policy, ignoring the dynamic characteristics of the workload. To reduce the operational expenditure of NFV providers, this paper proposes an Elastic Virtual Network Function Orchestration (EVNFO) policy based on workload prediction. We adapt the online learning algorithm for predicting the flows rate of service function chains (SFC), which can help to obtain the VNF scaling decision. We further design the online instance provisioning strategy (OIPS) to accomplish the deployment of VNF instances according to the decision. The simulation proves that EVNFO can provide good performance with dynamic resource provision. The throughput of VNF is improved by 11.1%–22.9%, and the total operational expenditure can be reduced by 13.8% compared with other online approaches.