Online Service Function Chain Deployment for Live-Video in virtualized Content Delivery Networks, a Deep Reinforcement Learning approach
Jesús F. Cevallos Moreno, Rebecca Sattler, Raúl P. Caulier Cisterna, Lorenzo Ricciardi Celsi, Aminael Sánchez R., and Massimo Mecella.
Code for the paper in published at MDPI's Future Internet Journal .
Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in virtualized content delivery networks using deep reinforcement learning. We use an Enhanced Exploration, Dense-reward mechanism over a Dueling Double Deep Q Network (E2-D4QN). Our model assumes to use network function virtualization at the container level. We carefully model processing times as a function of current resource utilization in data ingestion and streaming processes. We assess the performance of our algorithm under bounded network resource conditions to build a safe exploration strategy that enables the market entry of new bounded-budget vCDN players. Trace-driven simulations with real-world data reveal that our approach is the only one to adapt to the complexity of the particular context of Live-Video delivery concerning the state-of-art algorithms designed for general-case service function chain deployment. In particular, our simulation test revealed substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds.
In this repository, the final training and evaluation code is available. For visibility of the datasets used, data pre-processing and other questions, please contact the corresponding author asanchez2ATutpl.edu.ec or write to jesusfcevallos@gmail.com