Application, Workload, and Infrastructure Models for Virtualized Content Delivery Networks Deployed in Edge Computing Environments

2018 ◽  
Author(s):  
Thang Le Duc ◽  
Per-Olov Oestberg
Keyword(s):  
Content Delivery ◽  
Edge Computing ◽  
Content Delivery Networks ◽  
Computing Environments ◽  
Delivery Networks

Content Delivery Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 1-34 ◽  
Author(s):  
Behrouz Zolfaghari ◽  
Gautam Srivastava ◽  
Swapnoneel Roy ◽  
Hamid R. Nemati ◽  
Fatemeh Afghah ◽  
...  
Keyword(s):  
Content Delivery ◽  
Content Delivery Networks ◽  
Delivery Networks

Deep Reinforcement Learning-based Content Migration for Edge Content Delivery Networks with Vehicular Nodes

2021 ◽  
pp. 1-1
Author(s):  
Sepideh Malektaji ◽  
Amin Ebrahimzadeh ◽  
Halima Elbiaze ◽  
Roch Glitho ◽  
Somayeh Kianpishe
Keyword(s):  
Reinforcement Learning ◽  
Content Delivery ◽  
Content Delivery Networks ◽  
Edge Content ◽  
Delivery Networks

scholarly journals Dynamic cache management in content delivery networks

2021 ◽  
Vol 187 ◽  
pp. 107822
Author(s):  
Srujan Teja Thomdapu ◽  
Palash Katiyar ◽  
Ketan Rajawat
Keyword(s):  
Content Delivery ◽  
Cache Management ◽  
Content Delivery Networks ◽  
Delivery Networks

scholarly journals The Impact of Content Delivery Networks on the Internet Ecosystem

2013 ◽  
Vol 3 ◽  
pp. 304
Author(s):  
Manuel Palacin ◽  
Miquel Oliver ◽  
Jorge Infante ◽  
Simon Oechsner ◽  
Alex Bikfalvi
Keyword(s):  
Content Delivery ◽  
The Internet ◽  
Content Delivery Networks ◽  
Delivery Networks ◽  
The Impact

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

2021 ◽  
Vol 13 (11) ◽  
pp. 278
Author(s):  
Jesús Fernando Cevallos Moreno ◽  
Rebecca Sattler ◽  
Raúl P. Caulier Cisterna ◽  
Lorenzo Ricciardi Celsi ◽  
Aminael Sánchez Rodríguez ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Content Delivery ◽  
Network Function Virtualization ◽  
Live Streaming ◽  
Video Delivery ◽  
Network Systems ◽  
Service Function ◽  
Content Delivery Networks ◽  
Service Function Chain ◽  
Delivery Networks

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.

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