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

Jesús Fernando Cevallos Moreno, Rebecca Sattler, Raúl P. Caulier Cisterna, Lorenzo Ricciardi Celsi, Aminael Sánchez Rodríguez and Massimo Mecella
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Jesús Fernando Cevallos Moreno: Department of Computer Science, Automation and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy
Rebecca Sattler: Department of Computer Science, Databases and Information Systems, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
Raúl P. Caulier Cisterna: Centro de Imagen Biomédica, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul 7820436, Chile
Lorenzo Ricciardi Celsi: ELIS Innovation Hub, Via Sandro Sandri 45-81, 00159 Rome, Italy
Aminael Sánchez Rodríguez: Microbial Systems Ecology and Evolution Hub, Universidad Técnica Particular de Loja, Loja 1101608, Ecuador
Massimo Mecella: Department of Computer Science, Automation and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy

Future Internet, 2021, vol. 13, issue 11, 1-28

Abstract: 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.

Keywords: live-video delivery; 5G networks; virtualized content delivery networks; network function virtualization; service function chain deployment; deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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