E2E network slice management framework for 5G multi-tenant networks

Chirivella‐Perez, Enrique; Salvá-García, Pablo; Sanchez-Navarro, Ignacio; Calero, José M. Alcaraz; Wang, Qi

doi:10.23919/jcn.2023.000019

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…It utilized a demand-supply model to measure the VNF interference since VNF consolidation could result in a significant decline in performance. The process of deploying a number of slices is one of the most challenging tasks associated with network slicing [25]. Most network slicing deployments have occurred in the fixed core, while E2E network slicing in 5G is still evolving.…”

Section: Slice Deployment Using Virtual Network Embeddingmentioning

confidence: 99%

An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning

Venkatapathy,

Srinivasan,

et al. 2023

Sensors

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Network slicing shows promise as a means to endow 5G networks with flexible and dynamic features. Network function virtualization (NFV) and software-defined networking (SDN) are the key methods for deploying network slicing, which will enable end-to-end (E2E) isolation services permitting each slice to be customized depending on service requirements. The goal of this investigation is to construct network slices through a machine learning algorithm and allocate resources for the newly created slices using dynamic programming in an efficient manner. A substrate network is constructed with a list of key performance indicators (KPIs) like CPU capacity, bandwidth, delay, link capacity, and security level. After that, network slices are produced by employing multi-layer perceptron (MLP) using the adaptive moment estimation (ADAM) optimization algorithm. For each requested service, the network slices are categorized as massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communications (uRLLC). After network slicing, resources are provided to the services that have been requested. In order to maximize the total user access rate and resource efficiency, Dijkstra’s algorithm is adopted for resource allocation that determines the shortest path between nodes in the substrate network. The simulation output shows that the present model allocates optimum slices to the requested services with high resource efficiency and reduced total bandwidth utilization.

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Section: Slice Deployment Using Virtual Network Embeddingmentioning

confidence: 99%

An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning

Venkatapathy,

Srinivasan,

et al. 2023

Sensors

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ServiceNet: resource-efficient architecture for topology discovery in large-scale multi-tenant clouds

Gama Garcia,

Alcaraz Calero,

Mora Mora

et al. 2024

Cluster Comput

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Modern computing infrastructures are evolving due to virtualisation, especially with the advent of 5G and future technologies. While this transition offers numerous benefits, it also presents challenges. Consequently, understanding these complex systems, including networks, services, and their interconnections, is crucial. This paper introduces ServiceNet, a groundbreaking architecture that accurately performs the important task of providing understanding of a multi-tenant architecture by discovering the complete topology, crucial in the realm of high-performance distributed computing. Experimental results have been carried out in different scenarios in order to validate our approach, demonstrating the effectiveness of our approach in comprehensive multi-tenant topology discovery. The experiments, involving up to forty tenant, highlight the adaptability of ServiceNet as a valuable tool for real-time monitoring in topology discovery purposes, even in challenging scenarios.

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On the feasibility of using an autonomic control loop and automated planning for tenant-oriented reconfiguration of network slices

Rodriguez-Vivas,

Caicedo,

Nobre

et al. 2024

Expert Systems with Applications

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E2E network slice management framework for 5G multi-tenant networks

Cited by 6 publications

References 32 publications

An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning

An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning

ServiceNet: resource-efficient architecture for topology discovery in large-scale multi-tenant clouds

On the feasibility of using an autonomic control loop and automated planning for tenant-oriented reconfiguration of network slices

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