DSPPV: Dynamic service function chains placement with parallelized virtual network functions in mobile edge computing

This study configures an architecture based on Deep Reinforcement Learning (DRL) with the aim of providing online services to end users in Mobile Edge Computing (MEC) networks. Network Function Virtualization (NFV) technology can provide these services in MEC by turning hardware middleboxes to Virtual Network Functions (VNFs) for mobile users. Network services are chained as an ordered sequence of VNFs named Service Function Chains (SFCs), which are provided by directing traffic to the required VNFs. Meanwhile, the SFC placement problem is challenging for provisioning online service requests under limited resource availability and improving quality of service. We propose DRL-based Dynamic SFC Placement method with Parallelized VNFs (DSPPV) to solve this problem that seeks to maximize long-term expected cumulative reward. By sharing VNFs in parallel, DSPPV can achieve computational acceleration in providing online services. Any pair of VNFs that do not conflict on traffic can process the packet simultaneously, so configuring SFC with parallelized VNFs will reduce the SFC length and thus reduce the latency. In addition, DSPPV increases the ability to process future requests by extracting the distribution of initialized VNFs. The performed simulations show the effectiveness of the proposed architecture. Specifically, the obtained numerical results show that the average number of accepted requests has improved between 4% and 15%.