JOSP: Joint Optimization of Flow Path Scheduling and Virtual Network Function Placement for Delay-Sensitive Applications

With the rapid development of network function virtualization, delay-sensitive applications including auto-driving, online gaming, and multimedia conferencing can be served by virtual network function (VNF) chains with low operation expense/capital expense and high flexibility. However, as the service requests are highly dynamic and different services require distinct bandwidth occupation amount and time, how to schedule the paths of flows and place VNFs efficiently to guarantee the performances of network applications and maximize the utilization of the underlying network is a challenging problem. In this paper, we present a joint optimization approach of flow path scheduling and VNF placement, named JOSP, which explores the best utilization of bandwidth from two different aspects to reduce the network delay. We first present a delay scaling strategy that adds the penalty to the link bandwidth occupation that may cause congestion in accordance with the network placement locations. Then we consider the bandwidth occupation time and present a long-short flow differentiating strategy for the data flows with different duration. Furthermore, we present a reinforcement learning framework and use both the flow path delay and the network function-related delay to calculate the reward of placing VNFs adaptively. Performance evaluation results show that the JOSP can reduce the network delay by 40% on average compared with the existing methods.