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.

Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach / CEVALLOS MOREN, JESUS FERNANDO; Sattler, Rebecca; Caulier Caulier Cisterna, Ra('(u))l. P.; RICCIARDI CELSI, Lorenzo; S('(a))nchez S('(a))nchez Rodr('(i))guez, Aminael; Mecella, Massimo. - In: FUTURE INTERNET. - ISSN 1999-5903. - 13:11(2021). [10.3390/fi13110278]

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

Jesus Fernando Cevallos Moren
Primo
;
Lorenzo Ricciardi Ricciardi Celsi
;
Massimo Mecella
Ultimo
2021

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.
2021
live-video delivery; 5G networks; virtualized content delivery networks; network function virtualization; service function chain deployment; deep reinforcement learning
01 Pubblicazione su rivista::01a Articolo in rivista
Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach / CEVALLOS MOREN, JESUS FERNANDO; Sattler, Rebecca; Caulier Caulier Cisterna, Ra('(u))l. P.; RICCIARDI CELSI, Lorenzo; S('(a))nchez S('(a))nchez Rodr('(i))guez, Aminael; Mecella, Massimo. - In: FUTURE INTERNET. - ISSN 1999-5903. - 13:11(2021). [10.3390/fi13110278]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1654721
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