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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Autores principales:	Jesús Fernando Cevallos Moreno, Rebecca Sattler, Raúl P. Caulier Cisterna, Lorenzo Ricciardi Celsi, Aminael Sánchez Rodríguez, Massimo Mecella
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	live-video delivery 5G networks virtualized content delivery networks network function virtualization service function chain deployment deep reinforcement learning Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/69253122f11745158542494e995533c2
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spelling	oai:doaj.org-article:69253122f11745158542494e995533c22021-11-25T17:39:44ZOnline Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach10.3390/fi131102781999-5903https://doaj.org/article/69253122f11745158542494e995533c22021-10-01T00:00:00Zhttps://www.mdpi.com/1999-5903/13/11/278https://doaj.org/toc/1999-5903Video 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.Jesús Fernando Cevallos MorenoRebecca SattlerRaúl P. Caulier CisternaLorenzo Ricciardi CelsiAminael Sánchez RodríguezMassimo MecellaMDPI AGarticlelive-video delivery5G networksvirtualized content delivery networksnetwork function virtualizationservice function chain deploymentdeep reinforcement learningInformation technologyT58.5-58.64ENFuture Internet, Vol 13, Iss 278, p 278 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	live-video delivery 5G networks virtualized content delivery networks network function virtualization service function chain deployment deep reinforcement learning Information technology T58.5-58.64
spellingShingle	live-video delivery 5G networks virtualized content delivery networks network function virtualization service function chain deployment deep reinforcement learning Information technology T58.5-58.64 Jesús Fernando Cevallos Moreno Rebecca Sattler Raúl P. Caulier Cisterna Lorenzo Ricciardi Celsi Aminael Sánchez Rodríguez Massimo Mecella Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach
description	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.
format	article
author	Jesús Fernando Cevallos Moreno Rebecca Sattler Raúl P. Caulier Cisterna Lorenzo Ricciardi Celsi Aminael Sánchez Rodríguez Massimo Mecella
author_facet	Jesús Fernando Cevallos Moreno Rebecca Sattler Raúl P. Caulier Cisterna Lorenzo Ricciardi Celsi Aminael Sánchez Rodríguez Massimo Mecella
author_sort	Jesús Fernando Cevallos Moreno
title	Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach
title_short	Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach
title_full	Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach
title_fullStr	Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach
title_full_unstemmed	Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach
title_sort	online service function chain deployment for live-streaming in virtualized content delivery networks: a deep reinforcement learning approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/69253122f11745158542494e995533c2
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Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach

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