Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach

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 Liv...

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Bibliographic Details
Published in:Future internet 2021-11, Vol.13 (11), p.278
Main Authors: Cevallos Moreno, Jesús Fernando, Sattler, Rebecca, Caulier Cisterna, Raúl P., Ricciardi Celsi, Lorenzo, Sánchez Rodríguez, Aminael, Mecella, Massimo
Format: Article
Language:English
Subjects:
5G networks
Algorithms
Alliances
Chains
Content delivery networks
Context
Costs
Deep learning
deep reinforcement learning
Expenditures
Heuristic
Ingestion
Internet
Linear programming
live-video delivery
Machine learning
Multiple objective analysis
network function virtualization
Optimization
Quality of service
Resource utilization
Servers
service function chain deployment
Software
Streaming media
Virtual networks
virtualized content delivery networks
Web content delivery
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Summary: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 a substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds.
ISSN:1999-5903
1999-5903
DOI:10.3390/fi13110278