The growing role of optical networks in Artificial Intelligence (AI) infrastructure exposes AI researchers to a field still gated by fluency with vendor-specific Yet Another Next Generation (YANG) models and Network Configuration Protocols (NETCONF). Large Language Models (LLMs) can help bridge this gap if applied properly. Specifically, LLMs do not know the YANG data models adopted by commercial Open Reconfigurable Optical Add-Drop Multiplexer (OpenROADM) equipment, and retrieving YANG as flat text loses the hierarchical structure that gives each constraint its meaning.

Next-Generation Radio Access Networks (NG-RANs) aim to facilitate high data rates, low-latency applications, and dense mobile connectivity — benefit from the integration of Artificial Intelligence and Machine Learning (AI/ML) to enhance performance and efficiency. Nevertheless, the dynamic service demands within NG-RAN (namely Open RAN) lead to AI/ML performance degradation known as drift, resulting in violations of Service Level Agreements (SLA) and issues like over-or under-provisioning of resources. Detecting and adapting to drift becomes crucial to meet the diverse requirements of intelligent networks. Due to frequent retraining, the existing threshold and classifier-based approaches have potential disadvantages such as SLA violations and resource inefficiency. This paper introduces a novel approach that exploits the Generative Adversarial Network (GAN) architecture to determine the drift and anomaly. The proposed approach is evaluated for a throughput prediction use case over a real-time dataset and compared to the threshold and classifier-based approaches. The results show that the proposed approach outperforms the threshold and classifier-based approaches.

The rapid evolution of next-generation mobile networks introduces challenges and opportunities in achieving various use cases with extremely low latency, high data rates, and dense user connectivity. However, these objectives can lead to the higher energy consumption of mobile networks, particularly within the Radio Access Network (RAN), which generally consumes 75% of the mobile networks total energy consumption. The current studies available focus mainly on the energy consumption of the next-generation Core Network (5GC), while this demonstration provides a better understanding of the energy consumption of the RAN components. The demonstration provides energy observability leveraging various open-source software tools to measure and monitor RAN energy consumption trends deployed on the Kubernetes platform — a widely adopted container orchestration platform. Moreover, this demonstration shows energy consumption on different RAN architectures — Monolithic, Disaggregated, and Control Plane and User Plane Separation (CUPS) — utilizing tools such as Kepler and Scaphandre for comprehensive energy measurement.

Recently, container-based solutions have become de facto compute units of modern cloud-native applications. However, the exponential growth in data traffic and the power consumption of these technologies to handle high data traffic alarm the strong need for energy evaluation approaches in containerized clouds. Furthermore, the proliferation of highly distributed edge clouds raises additional concerns regarding the power consumption of future cloud architectures. This article presents a detailed overview of methods and techniques for monitoring power consumption within popular cloud platforms. The study offers an in-depth evaluation of these approaches, demonstrating variations in measured power consumption based on the chosen technique. A well-known container orchestration platform named Kubernetes (K8s) has been applied in our extensive measurements. This work argues that energy-efficient container clouds will play a vital role in building a more sustainable and eco-friendly digital infrastructure by optimizing power consumption and reducing carbon footprint, paving the way for a greener future. The paper also discusses open challenges and future research directions on energy sustainability, leading to the conclusion, offering lessons learned and prospects on potential solutions to foster sustainable practices within the container ecosystem.

Beyond fifth-generation (B5G) networks (namely 6G) aim to support high data rates, low-latency applications, and massive machine communications. Integrating Artificial Intelligence (AI) and Machine Learning (ML) models are essential for addressing the network’s increasing complexity and dynamic nature. However, dynamic service demands of B5G cause the AI/ML models performance degradation, resulting in violations of Service Level Agreements (SLA), over-or under-provisioning of resources, etc. To address the performance degradation of the AI/ML models, retraining is essential. Existing threshold and periodic retraining approaches have potential disadvantages such as SLA violations and inefficient resource utilization for setting a threshold parameter in a dynamic environment. This paper presents a novel algorithm that predicts when to retrain AI/ML models using an unsupervised classifier. The proposed predictive approach is evaluated for a Quality of Service (QoS) prediction use case on the Open RAN Software Community (OSC) platform and compared to the threshold approach. The results show that the proposed predictive approach outperforms the threshold approach.

The advent of Open Radio Access Network (O-RAN) technology enables intelligent edge solutions for base stations in beyond 5G (B5G) networks. O-RAN Working Group 2 (WG2) focuses on the architecture and specifications of AI/ML workflows, allowing AI/ML applications in O-RAN environments to meet different QoS requirements for different use cases over varying time periods. This study shows the technical challenges in mapping AI/ML functionalities at Near-Real Time (RT) RAN Intelligence Controller (RIC) and/or Non-RT RIC for closed loop control-based resource adaptation in O-RAN. We also present a drift-based solution to avoid performance violations if there is decay in prediction accuracy. Results show that drift-based solution outperforms offline models.