arXiv subjects

Djamel Sadok

Publications and source records attributed to Djamel Sadok.

6 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↗

arXiv2025

Deep Learning-Based Intrusion Detection for Automotive Ethernet: Evaluating & Optimizing Fast Inference Techniques for Deployment on Low-Cost Platform

Modern vehicles are increasingly connected, and in this context, automotive Ethernet is one of the technologies that promise to provide the necessary infrastructure for intra-vehicle communication. However, these systems are subject to attacks that can compromise safety, including flow injection attacks. Deep Learning-based Intrusion Detection Systems (IDS) are often designed to combat this problem, but they require expensive hardware to run in real time. In this work, we propose to evaluate and apply fast neural network inference techniques like Distilling and Prunning for deploying IDS models on low-cost platforms in real time. The results show that these techniques can achieve intrusion detection times of up to 727 {\mu}s using a Raspberry Pi 4, with AUCROC values of 0.9890.

cs.LG↗

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↗