arXiv subjects

Larissa F. Rodrigues Moreira

Publications and source records attributed to Larissa F. Rodrigues Moreira.

11 recordsLinked to original sources

arXiv2026

Energy-Aware Agentic Orchestration for Multi-Site Service Deployment

Energy-aware orchestration across geographically distributed infrastructures is increasingly important as data centers absorb growing Artificial Intelligence (AI) workloads. However, existing approaches often exclude the energy cost of automated decision-making from system-level sustainability assessments. This paper presents an energy-aware agentic orchestration framework that treats Large Language Model (LLM) reasoning as a measurable component of the control loop. We compare single-shot tool calling, ReAct, and plan-and-execute strategies on a multi-site Kubernetes testbed spanning two European data centers and instrumented at layers ranging from power distribution units to per-pod energy attribution. Using ten natural-language orchestration intents with varying decision complexity, we show that reasoning strategy and intent complexity strongly influence graphics processing unit (GPU) energy consumption during orchestration decisions. ReAct and plan-and-execute generated executable tool calls across all evaluated intents but required multiple LLM inferences, whereas the single-shot configuration was less costly but non-actionable. Prefix caching reduced mean ReAct GPU energy consumption by 29.4%. For the evaluated lightweight workload, the infrastructure-energy changes observed within the one-second measurement window were insufficient to offset the measured GPU decision energy. These findings demonstrate the importance of accounting for reasoning overhead when assessing the sustainability of AI-native orchestration and motivate energy-budgeted selection of agent strategies.

cs.NI↗

arXiv2026

Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic

Unsupervised anomaly detection is widely used to detect Distributed Denial-of-Service (DDoS) attacks in cloud-native 5G networks, yet most studies assume a fixed traffic representation, either temporal or structural, without validating which feature space best matches the data. We propose a lightweight decision framework that prioritizes temporal or structural features before training, using two diagnostics: lag-1 autocorrelation of an aggregated flow signal and PCA cumulative explained variance. When the probes are inconclusive, the framework reserves a hybrid option as a future fallback rather than an empirically validated branch. Experiments on two statistically distinct datasets with Isolation Forest, One-Class SVM, and KMeans show that structural features consistently match or outperform temporal ones, with the performance gap widening as temporal dependence weakens.

cs.LG↗

arXiv2026

TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning

Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study introduces TRACE, a MachineLearning (ML)pipeline designed to identify route changes using only traceroute latency data, thereby ensuring independence from control plane information. We propose a robust feature engineering strategy that captures temporal dynamics using rolling statistics and aggregated context patterns. The architecture leverages a stacked ensemble of Gradient Boosted Decision Trees refined by a hyperparameter-optimized meta-learner. By strictly calibrating decision thresholds to address the inherent class imbalance of rare routing events, TRACE achieves a superior F1-score performance, significantly outperforming traditional baseline models and demonstrating strong effective ness in detecting routing changes on the Internet.

cs.NI↗

arXiv2026

NeuroScaler: Towards Energy-Optimal Autoscaling for Container-Based Services

Future networks must meet stringent requirements while operating within tight energy and carbon constraints. Current autoscaling mechanisms remain workload-centric and infrastructure-siloed, and are largely unaware of their environmental impact. We present NeuroScaler, an AI-native, energy-efficient, and carbon-aware orchestrator for green cloud and edge networks. NeuroScaler aggregates multi-tier telemetry, from Power Distribution Units (PDUs) through bare-metal servers to virtualized infrastructure with containers managed by Kubernetes, using distinct energy and computing metrics at each tier. It supports several machine learning pipelines that link load, performance, and power. Within this unified observability layer, a model-predictive control policy optimizes energy use while meeting service-level objectives. In a real testbed with production-grade servers supporting real services, NeuroScaler reduces energy consumption by 34.68% compared to the Horizontal Pod Autoscaler (HPA) while maintaining target latency.

cs.NI↗

arXiv2025

Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment

The deployment of large-scale software-based 5G core functions presents significant challenges due to their reliance on optimized and intelligent resource provisioning for their services. Many studies have focused on analyzing the impact of resource allocation for complex deployments using mathematical models, queue theories, or even Artificial Intelligence (AI). This paper elucidates the effects of chaotic workloads, generated by Distributed Denial of Service (DDoS) on different Network Functions (NFs) on User Equipment registration performance. Our findings highlight the necessity of diverse resource profiles to ensure Service-Level Agreement (SLA) compliance in large-scale 5G core deployments. Additionally, our analysis of packet capture approaches demonstrates the potential of kernel-based monitoring for scalable security threat defense. Finally, our empirical evaluation provides insights into the effective deployment of 5G NFs in complex scenarios.

cs.CR↗

arXiv2025

Programming in Brazilian Higher Education and High School: A Systematic Literature Review

Programming, which is both economically significant and mentally stimulating, has been found to benefit the aging brain and to enhance cognitive function at various educational levels. Despite its advantages, challenges persist in standardizing and implementing programming education effectively across both the higher and secondary education levels in Brazil. To shed light on these issues, we carried out a systematic review of programming teaching methods in the Brazilian context, examining gaps, common techniques, approaches, and action opportunities in programming education. Our findings provide valuable recommendations for educational policymakers and educators to develop effective and updated national policies to teach programming.

cs.CY↗

arXiv2024

VINEVI: A Virtualized Network Vision Architecture for Smart Monitoring of Heterogeneous Applications and Infrastructures

Monitoring heterogeneous infrastructures and applications is essential to cope with user requirements properly, but it still lacks enhancements. The well-known state-of-the-art methods and tools do not support seamless monitoring of bare-metal, low-cost infrastructures, neither hosted nor virtualized services with fine-grained details. This work proposes VIrtualized NEtwork VIsion architecture (VINEVI), an intelligent method for seamless monitoring heterogeneous infrastructures and applications. The VINEVI architecture advances state of the art with a node-embedded traffic classification agent placing physical and virtualized infrastructures enabling real-time traffic classification. VINEVI combines this real-time traffic classification with well-known tools such as Prometheus and Victoria Metrics to monitor the entire stack from the hardware to the virtualized applications. Experimental results showcased that VINEVI architecture allowed seamless heterogeneous infrastructure monitoring with a higher level of detail beyond literature. Also, our node-embedded real-time Internet traffic classifier evolved with flexibility the methods with monitoring heterogeneous infrastructures seamlessly.

cs.NI↗

arXiv2024

Towards Cognitive Service Delivery on B5G through AIaaS Architecture

Artificial Intelligence (AI) is pivotal in advancing mobile network systems by facilitating smart capabilities and automation. The transition from 4G to 5G has substantial implications for AI in consolidating a network predominantly geared towards business verticals. In this context, 3GPP has specified and introduced the Network Data Analytics Function (NWDAF) entity at the network's core to provide insights based on AI algorithms to benefit network orchestration. This paper proposes a framework for evolving NWDAF that presents the interfaces necessary to further empower the core network with AI capabilities B5G and 6G. In addition, we identify a set of research directions for realizing a distributed e-NWDAF.

cs.NI↗

arXiv2024

Inspiring Women in Technology: Educational Pathways and Impact

This paper presents initiatives aimed at fostering female involvement in the realm of computing and endeavoring to inspire more women to pursue careers in these fields. The Meninas++ Project coordinates activities at both the high school and higher education levels, facilitating dialogue between young women and computing professionals, and promoting female role models within the field. Our study demonstrated the significant impact of these activities on inspiring, empowering, and retaining female students in computing. Furthermore, higher education initiatives have fostered engagement among both women and men, promoting inclusivity, entrepreneurship, and collaboration to enhance women's representation in the computing field.

cs.CY↗

arXiv2024

Resource Allocation Influence on Application Performance in Sliced Testbeds

Modern network architectures have shaped market segments, governments, and communities with intelligent and pervasive applications. Ongoing digital transformation through technologies such as softwarization, network slicing, and AI drives this evolution, along with research into Beyond 5G (B5G) and 6G architectures. Network slices require seamless management, observability, and intelligent-native resource allocation, considering user satisfaction, cost efficiency, security, and energy. Slicing orchestration architectures have been extensively studied to accommodate these requirements, particularly in resource allocation for network slices. This study explored the observability of resource allocation regarding network slice performance in two nationwide testbeds. We examined their allocation effects on slicing connectivity latency using a partial factorial experimental method with Central Processing Unit (CPU) and memory combinations. The results reveal different resource impacts across the testbeds, indicating a non-uniform influence on the CPU and memory within the same network slice.

cs.PF↗

arXiv2024

Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction

The diagnosis of diseases in food crops based on machine learning seemed satisfactory and suitable for use on a large scale. The Convolutional Neural Networks (CNNs) perform accurately in the disease prediction considering the image capture of the crop leaf, being extensively enhanced in the literature. These machine learning techniques fall short in data privacy, as they require sharing the data in the training process with a central server, disregarding competitive or regulatory concerns. Thus, Federated Learning (FL) aims to support distributed training to address recognized gaps in centralized training. As far as we know, this paper inaugurates the use and evaluation of FL applied in maize leaf diseases. We evaluated the performance of five CNNs trained under the distributed paradigm and measured their training time compared to the classification performance. In addition, we consider the suitability of distributed training considering the volume of network traffic and the number of parameters of each CNN. Our results indicate that FL potentially enhances data privacy in heterogeneous domains.

cs.LG↗