Virtualized Network Function Provisioning in Stochastic Cloud Environment

Network Function Virtualization (NFV) provides a new paradigm for provisioning network service where network functions are deployed as Virtual Network Functions (VNFs). Due to the advantages of NFV, many Network Function Virtualization Providers (NFVPs) offer their NFV services by deploying VNFs with purchased cloud resources in cloud environment to save the provisioning expense. However, existing VNF provisioning solutions ignore the influences of the dynamics of cloud environment, which may lead to over-provisioning and high deployment expense. In this paper, we study the problem of how should the NFVPs purchase cloud resources to provide NFV services for customers in order to minimize the expense of NFVPs, considering the dynamics of the system. We first abstract the system model of this problem and formulate it as a stochastic optimization programming problem. Then, we present our VIrtual Network functiOn proviSioning (VINOS) approach that can efficiently solve the stochastic optimization programming with a rolling horizon procedure. In particular, it first leverages Long Short Term Memory (LSTM) networks to predict future exogenous information and then optimally solves a deterministic problem over short horizon. We conduct extensive numerical experiments to evaluate the proposed approach. The experiment results suggest that our approach achieves total cost of 1.2 times offline optimum, and outperforms the benchmark algorithm by 8%, averagely.