Virtualized C-RAN Orchestration with Docker, Kubernetes and OpenAirInterface

Novaes, Camila; Nahum, Cleverson; Trindade, Igor; Cederholm, Daniel; Patra, Gyanesh; Klautau, Aldebaro

doi:10.14209/sbrt.2019.1570558820

Cited by 3 publications

(5 citation statements)

References 3 publications

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“…Its ability to automate deployment, scaling, and management of containerized applications makes it an integral part of the Network Function Virtualization (NFV) and Network Slicing (NS) paradigm. Kubernetes simplifies complex management tasks and has underpinned several proofs of concept, demonstrating the efficacy of Cloud-Native scenarios in mobile networks [25,26,27]. Notably, research in [28] introduced a CNF-scaler in Kubernetes, enhancing the scalability of 5G network services by adjusting CNFs' capacity based on their workload, validated using NRF and AUSF functions in the 5G Core.…”

Section: B 5g Core Evolutionmentioning

confidence: 99%

A 5G NTN Emulation Platform for VNF Orchestration: Design, Development, and Evaluation

Muro,

Baena,

Cola

et al. 2024

Preprint

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The integration of Non-Terrestrial Networks (NTN) with 5G represents a monumental leap in wireless network capabilities, significantly enhancing capacity, range, and reliability. This fusion fosters technological innovation and global connectivity. Crucial to this development is the softwarization of 5G networks through Virtual Network Functions (VNFs) within the OpenRAN paradigm, which disaggregates radio access networks into Central and Distributed Units (CU and DU). This approach accelerates deployment and facilitates upgrades. However, incorporating edge computing in Low Earth Orbit (LEO) satellites into this virtualized infrastructure presents management complexities, particularly in the interaction and resource sharing among VNFs, including satellite components. The absence of advanced emulators capable of accurately simulating these dynamics is a significant hurdle. To address this, this study introduces a groundbreaking emulation framework based on the Open Air Interface platform, tailored to reflect OpenRAN’s disaggregated network architecture. This framework precisely emulates the functional behavior of 5G-NTN systems and, with the integration of Kubernetes, enhances VNF lifecycle management, boosting efficiency in hybrid networks. The framework’s efficacy in VNF deployment and resource optimization is validated by extensive performance metrics, proving its vital role in emulating 5G-NTN systems. This innovation lays the groundwork for future research in efficient resource management within complex network ecosystems, offering a vital tool for exploring diverse VNF deployment scenarios and enabling networks to adapt dynamically to advanced wireless communication demands.

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Section: B 5g Core Evolutionmentioning

confidence: 99%

A 5G NTN Emulation Platform for VNF Orchestration: Design, Development, and Evaluation

Muro,

Baena,

Cola

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…As for cloud‐native deployments, a method to orchestrate and manage a container‐based C‐RAN using Kubernetes and OpenAirInterface (OAI) is presented in ref. [4]. Authors in ref.…”

Section: Related Workmentioning

confidence: 99%

“…Second (2), UE1 starts a 100 Mbps UDP downlink iperf connection from an iperf server located in the core node. Then (3), UE2 initiates an iperf of the same characteristics until (4), where the iperf stops and the SMF re-assigns UE2 to the MEC UPF. 4 A new iperf connection is then started by UE2 pointing to the edge node iperf server in ( 5) until (6), the moment at which both UEs stop their corresponding iperf connections.…”

Section: Upf Re-selection Towards the Edgementioning

confidence: 99%

“…Then (3), UE2 initiates an iperf of the same characteristics until (4), where the iperf stops and the SMF re-assigns UE2 to the MEC UPF. 4 A new iperf connection is then started by UE2 pointing to the edge node iperf server in ( 5) until (6), the moment at which both UEs stop their corresponding iperf connections. Finally, the whole deployment (5GC CNFs) is terminated, and the Open5GS containers are deleted in the core and edge nodes.…”

Section: Upf Re-selection Towards the Edgementioning

confidence: 99%

“…Certainly, multi‐tenant networks must support network slices, which require monitoring key performance indicators (KPIs) commonly belonging to different technological domains and managed by different entities. For instance, a network operator shall focus on the KPIs of the components running the network slice (e.g., RAN, cloud/edge, routers), while the slice owner may consider high‐level KPIs (e.g., end‐to‐end delay) for SLA validation [3]. In both cases, monitoring turns into an imperative aspect.…”

Section: Introductionmentioning

confidence: 99%

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Deploying cloud‐native experimental platforms for zero‐touch management 5G and beyond networks

et al. 2023

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An experimental framework for managing 5G and beyond networks through cloud‐native deployments and end‐to‐end monitoring is presented. The framework uses containerised network functions in a Kubernetes cluster across a multi‐domain network spanning cloud and edge hosts. End‐to‐end monitoring is demonstrated through Grafana dashboards that showcase both infrastructure resources and radio metrics in two scenarios involving UPF re‐selection and user mobility. As a third scenario, the authors demonstrate how a decision engine interacts with the experimental platform to perform zero‐touch containerised application relocation, highlighting the potential for enabling dynamic and intelligent management of 5G networks and beyond.

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Dynamic Optimization in 5G Network Slices: A Comparative Study of Whale Optimization, Particle Swarm Optimization, and Genetic Algorithm

Okindo,

Kamucha,

Oyie

2024

IJEER

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This study presents a comprehensive framework for optimizing 5G network slices using metaheuristic algorithms, focusing on Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine Type Communications (mMTC) scenarios. The initial setup involves a MATLAB-based 5G New Radio (NR) Physical Downlink Shared Channel (PDSCH) simulation and OpenAir-Interface (OAI) 5G network testbed, utilizing Ubuntu 22.04 Long Term Support (LTS), MicroStack, Open-Source MANO (OSM), and k3OS to create a versatile testing environment. Key network parameters are identified for optimization, including power control settings, signal-to-noise ratio targets, and resource block allocation, to address the unique requirements of different 5G use cases. Metaheuristic algorithms, specifically the Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), are employed to optimize these parameters. The algorithms are assessed based on their ability to enhance throughput, reduce latency, and minimize jitter within the network slices and the MATLAB simulation model. Each algorithm’s performance is evaluated through iterative testing, with improvements measured against established pre-optimization benchmarks. The results demonstrate significant enhancements in network performance post-optimization. For eMBB, the GA shows the most substantial increase in throughput, while PSO is most effective in reducing latency for URLLC applications. In mMTC scenarios, GA achieves the most notable reduction in jitter, illustrating the potential of metaheuristic algorithms in fine-tuning 5G networks to meet diverse service requirements. The study concludes that the strategic application of these algorithms can significantly improve the efficiency and reliability of 5G network slices, offering a scalable approach to managing the complex dynamics of next-generation wireless networks.

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Virtualized C-RAN Orchestration with Docker, Kubernetes and OpenAirInterface

Cited by 3 publications

References 3 publications

A 5G NTN Emulation Platform for VNF Orchestration: Design, Development, and Evaluation

A 5G NTN Emulation Platform for VNF Orchestration: Design, Development, and Evaluation

Deploying cloud‐native experimental platforms for zero‐touch management 5G and beyond networks

Dynamic Optimization in 5G Network Slices: A Comparative Study of Whale Optimization, Particle Swarm Optimization, and Genetic Algorithm

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