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

Author

Listed:
  • Jesús Fernando Cevallos Moreno

    (Department of Computer Science, Automation and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy
    These authors are also with The Rielo Institute for Integral Development, 25-40 Shore Blvd, PH 20R, Astoria, NY 11102, USA.)

  • Rebecca Sattler

    (Department of Computer Science, Databases and Information Systems, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
    These authors are also with The Rielo Institute for Integral Development, 25-40 Shore Blvd, PH 20R, Astoria, NY 11102, USA.)

  • Raúl P. Caulier Cisterna

    (Centro de Imagen Biomédica, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul 7820436, Chile
    These authors are also with The Rielo Institute for Integral Development, 25-40 Shore Blvd, PH 20R, Astoria, NY 11102, USA.)

  • 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
    These authors are also with The Rielo Institute for Integral Development, 25-40 Shore Blvd, PH 20R, Astoria, NY 11102, USA.)

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.

Suggested Citation

  • Jesús Fernando Cevallos Moreno & Rebecca Sattler & Raúl P. Caulier Cisterna & Lorenzo Ricciardi Celsi & Aminael Sánchez Rodríguez & Massimo Mecella, 2021. "Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach," Future Internet, MDPI, vol. 13(11), pages 1-28, October.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:11:p:278-:d:668487
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Michael Mackay, 2022. "Editorial for the Special Issue on 5G Enabling Technologies and Wireless Networking," Future Internet, MDPI, vol. 14(11), pages 1-2, November.

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