Network Resource Allocation System for QoE-Aware Delivery of Media Services in 5G Networks

Martín, Ángel; Egana, Jon; Flórez, Jesús; Montalbán, Jon; Olaizola, Igor G.; Quartulli, Marco; Viola, R.; Zorrilla, Mikel

doi:10.1109/tbc.2018.2828608

Cited by 79 publications

(49 citation statements)

References 35 publications

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“…Martin et al [4] developed a network resource allocator system that fosters self-configuration, self-optimization and self-healing capabilities by means of ML, SDN and NFV technologies. The system enables QoE-aware autonomous network management which dynamically and proactively provisions a network topology to adapt to changing demands of media services.…”

Section: A Academic Research Workmentioning

confidence: 99%

AI-Driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions

2020

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The foreseen complexity in operating and managing 5G and beyond networks has propelled the trend toward closedloop automation of network and service management operations. To this end, the ETSI Zero-touch network and Service Management (ZSM) framework is envisaged as a next-generation management system that aims to have all operational processes and tasks executed automatically, ideally with 100% automation. Artificial Intelligence (AI) is envisioned as a key enabler of self-managing capabilities, resulting in lower operational costs, accelerated time-to-value and reduced risk of human error. Nevertheless, the growing enthusiasm for leveraging AI in a ZSM system should not overlook the potential limitations and risks of using AI techniques. The current paper aims to introduce the ZSM concept and point out the AI-based limitations and risks that need to be addressed in order to make ZSM a reality.

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Section: A Academic Research Workmentioning

confidence: 99%

AI-Driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions

2020

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“…A first QoE-centric approach provides a service quality prediction model for UHD real time video streaming [15]. A network resource allocation system for autonomous QoE-aware 5G network management is discussed in [14].…”

Section: Background and Related Workmentioning

confidence: 99%

Estimating Video Streaming QoE in the 5G Architecture Using Machine Learning

Schwarzmann

Marquezan

Bosk

et al. 2019

Proceedings of the 4th Internet-QoE Workshop on QoE-based Analysis and Management of Data Communication Networks

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Compared to earlier mobile network generations, the 5G system architecture has been significantly enhanced by the introduction of network analytics functionalities and extended capabilities of interacting with third party Application Functions (AFs). Combining these capabilities, new features for Quality of Experience (QoE) estimation can be designed and introduced in next generation networks. It is, however, unclear how 5G networks can collect monitoring data and application metrics, how they correlate to each other, and which techniques can be used in 5G systems for QoE estimation. This paper studies the feasibility of Machine Learning (ML) techniques for QoE estimation and evaluates their performance for a mobile video streaming use-case. A simulator has been implemented with OMNeT++ for generating traces to (i) examine the relevance of features generated from 5G monitoring data and (ii) to study the QoE estimation accuracy (iii) for a variable number of used features.

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“…Fifth-generation (5G) networks have been designed to attain a 1000-fold capacity, five-fold reduced latency and 10-fold longer battery life than 4G networks [ 1 , 2 ]. This evolution is due to the proliferation of high-demand mobile applications and the traffic growth exchanged daily by billions of mobile wireless devices worldwide [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

RELIABLE: Resource Allocation Mechanism for 5G Network using Mobile Edge Computing

Pereira

Lieira

Silva

et al. 2020

Sensors

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Technological advancement is currently focused on the miniaturization of devices, and integrated circuits allow us to observe the increase in the number of Internet of Things (IoT) devices. Most IoT services and devices require an Internet connection, which needs to provide the minimum processing, storage and networking requirements to best serve a requested service. One of the main goals of 5G networks is to comply with the user’s various Quality of Service (QoS) requirements in different application scenarios. Fifth-generation networks use Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) concepts to achieve these QoS requirements. However, the computational resource allocation mechanisms required by the services are considered very complex. Thus, in this paper, we propose an allocation and management resources mechanism for 5G networks that uses MEC and simple mathematical methods to reduce the model complexity. The mechanism decides to allocate the resource in MEC to meet the requirements requested by the user. The simulation results show that the proposed mechanism provides a larger amount of services, leading to a reduction in the service lock number and as a reduction in the blocking ratio of services due to the accuracy of the approach and its load balancing in the process of resource allocation.

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Network Resource Allocation System for QoE-Aware Delivery of Media Services in 5G Networks

Cited by 79 publications

References 35 publications

AI-Driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions

AI-Driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions

Estimating Video Streaming QoE in the 5G Architecture Using Machine Learning

RELIABLE: Resource Allocation Mechanism for 5G Network using Mobile Edge Computing

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