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

Moreno, Jesús Fernando Cevallos; Sattler, Rebecca; Caulier‐Cisterna, Raúl; Celsi, Lorenzo Ricciardi; Sánchez-Rodríguez, Aminael; Mecella, Massimo

doi:10.3390/fi13110278

Cited by 12 publications

(6 citation statements)

References 44 publications

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“…A CDN can be a potent tool for enhancing website speed, guaranteeing greater availability, cutting expenses associated with bandwidth, and enhancing user experience. CDNs can assist in ensuring that web material is accessible and available to people worldwide, independent of their location or the device they are using, by dispersing it across a global network of servers [37].…”

Section: Parameter Based Comparison Of Protocolsmentioning

confidence: 99%

Cloud‐based video streaming services: Trends, challenges, and opportunities

Kumar,

Sharma,

Tanwar

et al. 2024

CAAI Trans on Intel Tech

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Cloud computing has drastically changed the delivery and consumption of live streaming content. The designs, challenges, and possible uses of cloud computing for live streaming are studied. A comprehensive overview of the technical and business issues surrounding cloud‐based live streaming is provided, including the benefits of cloud computing, the various live streaming architectures, and the challenges that live streaming service providers face in delivering high‐quality, real‐time services. The different techniques used to improve the performance of video streaming, such as adaptive bit‐rate streaming, multicast distribution, and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is underlined. Issues such as improving user experience and live streaming service performance using cutting‐edge technology, like artificial intelligence and machine learning are discussed. In addition, the legal and regulatory implications of cloud‐based live streaming, including issues with network neutrality, data privacy, and content moderation are addressed. The future of cloud computing for live streaming is examined in the section that follows, and it looks at the most likely new developments in terms of trends and technology. For technology vendors, live streaming service providers, and regulators, the findings have major policy‐relevant implications. Suggestions on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector, as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.

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Section: Parameter Based Comparison Of Protocolsmentioning

confidence: 99%

Cloud‐based video streaming services: Trends, challenges, and opportunities

Kumar,

Sharma,

Tanwar

et al. 2024

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Equally often, DQN solutions adopt prioritized experience replay, which assigns higher probabilities to actions that lead to higher rewards when sampling from the experience replay buffer. Finally, different DQN variants have been proposed to mitigate the reward overestimation tendencies of the algorithm [62], such as the Dueling-DQN [63] and Double-DQN [64].…”

Section: Algorithm 3 Grey-wolf Optimization (Gwo)mentioning

confidence: 99%

Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum

Poltronieri,

Stefanelli,

Tortonesi

et al. 2023

Future Internet

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Modern computing environments, thanks to the advent of enabling technologies such as Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary network of computing resources that extend from the Edge of the network to the Cloud, which enables a dynamic and adaptive service fabric. Efficiently coordinating resource allocation, exploitation, and management in the Cloud Continuum represents quite a challenge, which has stimulated researchers to investigate innovative solutions based on smart techniques such as Reinforcement Learning and Computational Intelligence. In this paper, we make a comparison of different optimization algorithms and a first investigation of how they can perform in this kind of scenario. Specifically, this comparison included the Deep Q-Network, Proximal Policy Optimization, Genetic Algorithms, Particle Swarm Optimization, Quantum-inspired Particle Swarm Optimization, Multi-Swarm Particle Optimization, and the Grey-Wolf Optimizer. We demonstrate how all approaches can solve the service management problem with similar performance—with a different sample efficiency—if a high number of samples can be evaluated for training and optimization. Finally, we show that, if the scenario conditions change, Deep-Reinforcement-Learning-based approaches can exploit the experience built during training to adapt service allocation according to the modified conditions.

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“…Whereas, in the online scenario, the SFC orchestration strategy is adapted according to dynamic network load and the related problem is solved using migration algorithms on real time basis. In such scenario, a PSN under various resource availability constraints is considered with a set of already deployed SFC requests and the new incoming SFC requests are processed on sequential basis (in a one-by-one modality) [10]. In online scenario, adopted in this paper, the challenge for each SFC request is to find a suitable SFC instance meeting its requirements.…”

Section: Nfv-ra and Sfc Orchestration: Scenarios Strategies And Deplo...mentioning

confidence: 99%

Online QoS/QoE-driven SFC Orchestration Leveraging a DRL Approach in SDN/NFV Enabled Networks

Escheikh,

Yakoub

2023

Preprint

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The proliferation of the ever-increasing number of highly heterogeneous smart devices and the emerging of a wide range of diverse applications in 5G mobile network ecosystems impose to tackle new set of raising challenges related to agile and automated service orchestration and management. Fully leveraging key enablers technologies such as Software Defined Network (SDN), Network Function Virtualization (NFV) and Machine Learning (ML) capabilities in such environment is of paramount importance to address Service Function Chaining (SFC) orchestration issues according to user requirements and network constraints. To meet these challenges, we propose in this paper a Deep Reinforcement Learning (DRL) approach to investigate online Quality of Experience (QoE)/Quality of Service (QoS) aware SFC orchestration problem. The objective of this work is to fulfill intelligent, elastic and automated Virtual Network Functions (VNF)s/Container Network Function (CNF)s deployment optimizing end-to-end user experience while respecting QoS constraints. We implement the DRL approach through using a variant of Deep-Q-Network (DQN) algorithm referred to as Double DQN. We show how DRL agent behaves along the learning process for different PSN scales. We highlight also the impact of a set of hyper-parameters such as batch size and learning rate on solving the sequential decision problem related to SFC orchestration. The evaluation of the learning process is achieved based on the quality of learning with respect to the number of runs. In this regard, we use QoE metric to define a score quantifying the quality of learning.

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

Cited by 12 publications

References 44 publications

Cloud‐based video streaming services: Trends, challenges, and opportunities

Cloud‐based video streaming services: Trends, challenges, and opportunities

Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum

Online QoS/QoE-driven SFC Orchestration Leveraging a DRL Approach in SDN/NFV Enabled Networks

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