Alexandre Huff

Network Function Virtualization (NFV) is changing the way networks are built and maintained by replacing traditional middleboxes with software that runs on commodity hardware. This paradigm shift not only increases flexibility but can also reduce the cost of building and maintaining the network. The ETSI NFV-MANO reference model is currently widely adopted as the de facto NFV standard, with a large number of compliant platforms currently available. In this work, we propose Vines (Vines Is an NFV-MANO Extensible Solution), a CloudStack-based platform that supports NFV technology and is compliant with the ETSI NFV specifications. Vines includes an NFV Orchestrator (NFVO) that enables the creation of complex network services composed of multiple individual functions. Management & orchestration are holistic, in the sense that they provide a comprehensive set of functionalities for heterogeneous network functions and services. Moreover, nearly all NFV developments have been done on the OpenStack cloud platform. Vines opens up the possibility of using NFV solutions on Apache CloudStack, one of the most widely used cloud platforms worldwide. Experimental results are presented showing that Vines has the same performance level as the widely used OpenStack/Tacker.

This paper presents the first ever fully open-source implementation of Load Balancing (LB) in an experimental Fifth Generation (5G) New Radio (NR) Standalone (SA) network using Open Radio Access Network (O-RAN) architecture. The deployment leverages the O-RAN Software Community (SC) Near Real-Time RAN Intelligent Controller (Near-RT RIC), srsRAN stack, Open5GS core, and Software-Defined Radios (SDRs), with Commercial Off-The-Shelf (COTS) User Equipments (UEs). The implementation extends the srsRAN stack to support E2 Service Model for RAN Control (E2SM-RC) Style 3 Action 1 to facilitate Handovers (HOs) and adds Medium Access Control (MAC) downlink (DL) buffer volume reporting to srsRAN's E2 Service Model for Key Performance Measurement (E2SM-KPM). The deployment demonstrates Near-RT RIC closed-loop control where our Mobility Load Balancing (MLB) xApp makes HO decisions based on network load metrics for LB between two Open Distributed Units (O-DUs) operating at different frequencies in the same band.

6G networks demand unparalleled adaptability and energy efficiency. This work presents a dynamic O-RAN-based framework integrating the SMO, Near-RT RIC, and Non-RT RIC for real-time analytics, intelligent policy application, and adaptive energy management. By deploying rApps (e.g., Energy Savings) and xApps, the framework dynamically clusters and manages radio nodes, optimizing resource allocation while reducing power consumption. A prototype implementation shows substantial improvements over traditional static approaches, notably conserving energy during low-demand periods while sustaining high performance at peak times. Key components enable seamless integration across the network, demonstrating the framework’s capability to adapt to varying demands and establishing a new benchmark for 6G network management.

This paper addresses the critical challenge posed by the increasing energy consumption in mobile networks, particularly with the advent of Sixth Generation (6G) technologies. We propose an adaptive network management framework that leverages the Open Radio Access Network (O-RAN) architecture to enhance network adaptability and energy efficiency. By utilizing O-RAN's open interfaces and intelligent controllers, our approach implements dynamic resource management strategies that respond to fluctuating user demands while maintaining the quality of service. We design and implement O-RAN-compliant applications to validate our framework, demonstrating significant improvements in energy efficiency without compromising network performance. Our study offers a comprehensive guide for utilizing O-RAN's open architecture to achieve sustainable and energy-efficient 6G networks, aligning with global efforts to reduce the environmental impact of mobile communication systems.

The Open Radio Access Network (O-RAN) Alliance proposes an open architecture that disaggregates the RAN and supports executing custom control logic in near-real time from third-party applications, the xApps. Despite O-RAN’s efforts, the creation of xApps remains a complex and time-consuming endeavor, aggravated by the sometimes fragmented, outdated, or deprecated documentation from the O-RAN Software Community (OSC). These challenges hinder academia and industry from developing and validating solutions and algorithms on ORAN networks. This tutorial addresses this gap by providing the first comprehensive guide for developing xApps to manage the O-RAN ecosystem from theory to practice. We provide a thorough theoretical foundation of the O-RAN architecture and detail the functionality offered by Near Real-Time RAN Intelligent Controller (Near-RT RIC) components. We examine the xApp design and configuration. We explore the xApp lifecycle and demonstrate how to deploy and manage xApps on a Near-RT RIC. We address the xApps’ interfaces and capabilities, accompanied by practical examples. We provide comprehensive details on how xApps can control the RAN. We discuss debugging strategies and good practices to aid the xApp developers in testing their xApps. Finally, we review the current landscape and open challenges for creating xApps.

Os padrões 3rd Generation Partnership Project (3GPP) e Open Radio Access Network Alliance (O-RAN) definem interfaces abertas para que componentes de software e hardware da RAN e núcleo possam interoperar entre diferentes fornecedores. A interoperabilidade é uma das principais características desejadas pelos operadores de redes de telecomunicações móveis e órgãos padronizadores, permitindo a entrada de novos fornecedores e acelerando a inovação da indústria de telecomunicações. Entretanto, poucos trabalhos têm investigado a interoperabilidade de projetos O-RAN com os núcleos de redes móveis de quinta geração. Este trabalho analisa a interoperabilidade entre projetos de código aberto utilizando as interfaces abertas da RAN e do núcleo. O trabalho também realiza uma avaliação de desempenho dos projetos que apresentam interoperabilidade entre si. A avaliação de desempenho inclui uma discussão acerca dos projetos OpenAirInterface e Software Radio Systems RAN, na qual o primeiro apresenta um consumo de memória três vezes menor do que o segundo, enquanto que ambos apresentam taxas de transferências similares.

Uma Service Function Chain (SFC) define um serviço virtualizado de rede por meio do encadeamento de múltiplas funções virtualizadas. Nesse contexto, a solução Multi-SFC possibilita a composição de serviços de rede distribuídos em várias nuvens, domínios e orquestradores NFV (Network Function Virtualization). A Multi-SFC elimina limitações das implantações tradicionais, em particular a restrição de que todas as funções de uma SFC sejam alocadas em um único domínio ou ponto de presença (PoP). No entanto, alocar recursos no cenário da Multi-SFC para implantar SFCs de maneira otimizada é uma tarefa desafiadora. Este trabalho propõe uma estratégia bioinspirada para o mapeamento de SFCs no contexto da Multi-SFC. Os resultados dos testes de convergência e tempo de execução demonstram a viabilidade da proposta, enquanto os testes de variação de requisições de mapeamento evidenciam a versatilidade de atuação da solução.

This article investigates the deployment of a Near-Real-Time Radio Access Network (RAN) Intelligent Controller (near-RT RIC) on a distributed cloud infrastructure composed of multiple physical sites with different amounts of resources and associated costs. The challenge is dynamically adapting the near-RT RIC deployment to the most cost-effective arrangement while meeting the latency requirements between the near-RT RIC and the controlled nodes. We introduce an optimization model to solve the disaggregated near-RT RIC placement problem, considering a cloud-native infrastructure to minimize the placement cost while satisfying the latency-sensitive control loop requirements across the cloud-edge continuum. Moreover, we describe an experimental environment we created using geographically disparate cloud sites. We present data detailing the latencies of the communication links among these sites and the costs incurred in using this real-world infrastructure. We conduct a performance evaluation of the near-RT RIC deployment, comparing the distributed approach versus a traditional monolithic strategy and evaluating positioning costs, deployment, setup and registration times, and the control loop latency considering three scenarios. Our results show that in a cloud-native environment, the disaggregated near-RT RIC allows cost savings of up to 60% in comparison to a monolithic near-RT RIC while satisfying the control loop latency and achieving time efficiency in terms of deployment and registration of xApps and near-RT RIC components.