scholarly journals Reinforcement Learning for Service Function Chain Reconfiguration in NFV-SDN Metro-Core Optical Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 167944-167957
Author(s):  
Sebastian Troia ◽  
Rodolfo Alvizu ◽  
Guido Maier
Keyword(s):  
Reinforcement Learning ◽  
Optical Networks ◽  
Service Function ◽  
Service Function Chain

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.

Dependence-Aware Service Function Chain Embedding in Optical Networks

2019 ◽  
Author(s):  
Danyang Zheng ◽  
Evrim Guler ◽  
Chengzong Peng ◽  
Guangchun Luo ◽  
Ling Tian ◽  
...  
Keyword(s):  
Optical Networks ◽  
Service Function ◽  
Service Function Chain

Dynamic Service Function Chain Embedding for NFV-Enabled IoT: A Deep Reinforcement Learning Approach

2020 ◽  
Vol 19 (1) ◽  
pp. 507-519 ◽  
Author(s):  
Xiaoyuan Fu ◽  
F. Richard Yu ◽  
Jingyu Wang ◽  
Qi Qi ◽  
Jianxin Liao
Keyword(s):  
Reinforcement Learning ◽  
Learning Approach ◽  
Service Function ◽  
Service Function Chain

Reinforcement Learning for Service Function Chain Allocation in Fog Computing

2021 ◽  
pp. 147-173
Author(s):  
José Santos ◽  
Tim Wauters ◽  
Bruno Volckaert ◽  
Filip De Turck
Keyword(s):  
Reinforcement Learning ◽  
Fog Computing ◽  
Service Function ◽  
Service Function Chain

Service Function Chain Embedding for NFV-Enabled IoT Based on Deep Reinforcement Learning

2019 ◽  
Vol 57 (11) ◽  
pp. 102-108 ◽  
Author(s):  
Xiaoyuan Fu ◽  
F. Richard Yu ◽  
Jingyu Wang ◽  
Qi Qi ◽  
Jianxin Liao
Keyword(s):  
Reinforcement Learning ◽  
Service Function ◽  
Service Function Chain

scholarly journals Reliable Service Function Chain Deployment Method Based on Deep Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2733
Author(s):  
Hua Qu ◽  
Ke Wang ◽  
Jihong Zhao
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Network Function Virtualization ◽  
Node Degree ◽  
Average Delay ◽  
Service Function ◽  
Network Function ◽  
Resource Requirements ◽  
Current Resource ◽  
Service Function Chain

Network function virtualization (NFV) is a key technology to decouple hardware device and software function. Several virtual network functions (VNFs) combine into a function sequence in a certain order, that is defined as service function chain (SFC). A significant challenge is guaranteeing reliability. First, deployment server is selected to place VNF, then, backup server is determined to place the VNF as a backup which is running when deployment server is failed. Moreover, how to determine the accurate locations dynamically with machine learning is challenging. This paper focuses on resource requirements of SFC to measure its priority meanwhile calculates node priority by current resource capacity and node degree, then, a novel priority-awareness deep reinforcement learning (PA-DRL) algorithm is proposed to implement reliable SFC dynamically. PA-DRL determines the backup scheme of each VNF, then, the model jointly utilizes delay, load balancing of network as feedback factors to optimize the quality of service. In the experimental results, resource efficient utilization, survival rate, and load balancing of PA-DRL were improved by 36.7%, 35.1%, and 78.9% on average compared with benchmark algorithm respectively, average delay was reduced by 14.9%. Therefore, PA-DRL can effectively improve reliability and optimization targets compared with other benchmark methods.

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