arXiv subjects

Judith Kelner

Publications and source records attributed to Judith Kelner.

9 recordsLinked to original sources

arXiv2026

DualPI2 Active Queue Management in ns-3: Implementation And Validation

The demand for ultra-low latency applications necessitates advanced network architectures like the Low Latency, Low Loss, and Scalable Throughput (L4S) standard. A core component of L4S is the DualPI2 Active Queue Management (AQM), which ensures the safe coexistence of scalable and classic traffic. Despite L4S's growing adoption, the ns-3 network simulator lacks a high-fidelity, up-to-date DualPI2 model. This paper presents a comprehensive implementation of the DualPI2 AQM in ns-3, while also mirroring the official Linux Kernel architecture. Our model incorporates representative mechanisms previously absent in simulation, such as credit-based Weighted Round Robin (WRR) scheduling, step-marking, and overload protection. To guarantee simulation accuracy, we validate our implementation against a physical Linux testbed across 25 diverse Bandwidth-Delay Product (BDP) scenarios. Results demonstrate that our ns-3 model replicates real-world behavior, ensuring strict throughput fairness and queue delay isolation. Ultimately, this validated model equips the research community with a robust tool to evaluate and advance L4S performance across diverse network topologies, ranging from data centers to wireless home and office environments.

cs.NI↗

arXiv2026

Implementing the L4S Architecture in the ns-3 Simulator

The demand for ultra-low latency in modern applications, such as cloud gaming and augmented reality, has exposed the limitations of traditional congestion control algorithms regarding bufferbloat. The Low Latency, Low Loss, and Scalable Throughput (L4S) architecture addresses this challenge by combining scalable congestion controls, such as TCP Prague, low-latency queue management with prioritization, and Accurate ECN (AccECN) feedback. Although Linux kernel implementations exist, the research community lacks a complete, high-fidelity model within the Network Simulator 3 (ns-3) for reproducible experiments. This paper presents an implementation of end-host protocols for the L4S architecture in ns-3, focusing on the porting of TCP Prague from the Linux kernel (v6.12) and the integration of AccECN signaling. Significant engineering challenges regarding the adaptation of kernel logic are detailed, particularly the reconciliation of Linux's packet-based arithmetic with ns-3's byte-based architecture for window management and pacing. Simulation results demonstrate that the proposed model faithfully reproduces the congestion response behaviors observed in real-world testbed scenarios, validating the platform's accuracy. Consequently, this work provides the community with a validated toolset for complex L4S performance evaluations in controlled environments.

cs.NI↗

arXiv2026

Data analysis of cloud virtualization experiments

The cloud computing paradigm underlines data center and telecommunication infrastructure design. Heavily leveraging virtualization, it slices hardware and software resources into smaller software units for greater flexibility of manipulation. Given the considerable benefits, several virtualization forms, with varying processing and communication overheads, emerged, including Full Virtualization and OS Virtualization. As a result, predicting packet throughput at the data plane turns out to be more challenging due to the additional virtualization overhead located at CPU, I/O, and network resources. This research presents a dataset of active network measurements data collected while varying various network parameters, including CPU affinity, frequency of echo packet injection, type of virtual network driver, use of CPU, I/O, or network load, and the number of concurrent VMs. The virtualization technologies used in the study include KVM, LXC, and Docker. The work examines their impact on a key network metric, namely, end-to-end latency. Also, it builds data models to evaluate the impact of a cloud computing environment on packet round-trip time. To explore data visualization, the dataset was submitted to pre-processing, correlation analysis, dimensionality reduction, and clustering. In addition, this paper provides a brief analysis of the dataset, demonstrating its use in developing machine learning-based systems for administrator decision-making.

cs.NI↗

arXiv2026

Digital Twin Synchronization: towards a data-centric architecture

Digital Twin (DT) technology revolutionizes industrial processes by enabling the representation of physical entities and their dynamics to enhance productivity and operational efficiency. It has emerged as a vital enabling technology in the Industry 4.0 context. The present article examines the particular issue of synchronizing a digital twin while ensuring an accurate reflection of its physical counterpart. Despite the reported recent advances in the design of middleware and low delay communication technologies, effective synchronization between both worlds remains challenging. This paper reviews currently adopted synchronization technologies and architectures, identifies vital outstanding technical challenges, and proposes a unified synchronization architecture for use by various industrial applications while addressing security and interoperability requirements. As such, this study aims to bridges gaps and advance robust synchronization in DT environments, emphasizing the need for a standardized architecture to ensure seamless operation and continuous improvement of industrial systems.

cs.NI↗

arXiv2022

A framework for robotic arm pose estimation and movement prediction based on deep and extreme learning models

Human-robot collaboration has gained a notable prominence in Industry 4.0, as the use of collaborative robots increases efficiency and productivity in the automation process. However, it is necessary to consider the use of mechanisms that increase security in these environments, as the literature reports that risk situations may exist in the context of human-robot collaboration. One of the strategies that can be adopted is the visual recognition of the collaboration environment using machine learning techniques, which can automatically identify what is happening in the scene and what may happen in the future. In this work, we are proposing a new framework that is capable of detecting robotic arm keypoints commonly used in Industry 4.0. In addition to detecting, the proposed framework is able to predict the future movement of these robotic arms, thus providing relevant information that can be considered in the recognition of the human-robot collaboration scenario. The proposed framework is based on deep and extreme learning machine techniques. Results show that the proposed framework is capable of detecting and predicting with low error, contributing to the mitigation of risks in human-robot collaboration.

cs.RO↗

arXiv2022

FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices

IoT devices suffer from resource limitations, such as processor, RAM, and disc storage. These limitations become more evident when handling demanding applications, such as deep learning, well-known for their heavy computational requirements. A case in point is robot pose estimation, an application that predicts the critical points of the desired image object. One way to mitigate processing and storage problems is compressing that deep learning application. This paper proposes a new CNN for the pose estimation while applying the compression techniques of pruning and quantization to reduce his demands and improve the response time. While the pruning process reduces the total number of parameters required for inference, quantization decreases the precision of the floating-point. We run the approach using a pose estimation task for a robotic arm and compare the results in a high-end device and a constrained device. As metrics, we consider the number of Floating-point Operations Per Second(FLOPS), the total of mathematical computations, the calculation of parameters, the inference time, and the number of video frames processed per second. In addition, we undertake a qualitative evaluation where we compare the output image predicted for each pruned network with the corresponding original one. We reduce the originally proposed network to a 70% pruning rate, implying an 88.86% reduction in parameters, 94.45% reduction in FLOPS, and for the disc storage, we reduced the requirement in 70% while increasing error by a mere $1\%$. With regard input image processing, this metric increases from 11.71 FPS to 41.9 FPS for the Desktop case. When using the constrained device, image processing augmented from 2.86 FPS to 10.04 FPS. The higher processing rate of image frames achieved by the proposed approach allows a much shorter response time.

cs.CV↗

arXiv2020

The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption

Software defined networking (SDN) and network functions virtualisation (NFV) are making networks programmable and consequently much more flexible and agile. To meet service level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO2), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa (RNP) Network, Brazil's National Teaching and Research Network backbone. The simulation results showed that PPO2 generally outperformed A2C and a greedy approach both in terms of acceptance rate and energy consumption. A2C outperformed PPO2 only in the scenario where network servers had a greater number of computing resources.

cs.NI↗

arXiv2020

Predicting Short-term Mobile Internet Traffic from Internet Activity using Recurrent Neural Networks

Mobile network traffic prediction is an important input in to network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non-linear patterns or other important correlations in time series mobile network data. We compare the performance of two deep learning architectures - Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) - for predicting mobile Internet traffic using two months of Telecom Italia data for the metropolitan area of Milan. K-Means clustering was used a priori to group cells based on Internet activity and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error. Both Deep Learning algorithms were effective in modeling Internet activity and seasonality, both within days and across two months. We find variations in performance across clusters within the city. Overall, the LSTM outperformed the GRU in our experiments.

cs.NI↗

arXiv2009

A Standalone Markerless 3D Tracker for Handheld Augmented Reality

This paper presents an implementation of a markerless tracking technique targeted to the Windows Mobile Pocket PC platform. The primary aim of this work is to allow the development of standalone augmented reality applications for handheld devices based on natural feature tracking. In order to achieve this goal, a subset of two computer vision libraries was ported to the Pocket PC platform. They were also adapted to use fixed point math, with the purpose of improving the overall performance of the routines. The port of these libraries opens up the possibility of having other computer vision tasks being executed on mobile platforms. A model based tracking approach that relies on edge information was adopted. Since it does not require a high processing power, it is suitable for constrained devices such as handhelds. The OpenGL ES graphics library was used to perform computer vision tasks, taking advantage of existing graphics hardware acceleration. An augmented reality application was created using the implemented technique and evaluations were done regarding tracking performance and accuracy

cs.CV↗