network simulation

Existing discrete-event simulators (e.g., Hypatia) struggle to accurately evaluate real-world protocols and applications in LEO mega-constellation networks, exhibiting a substantial fidelity gap between simulation and empirical measurements. To bridge this gap, we propose a trajectory-driven satellite network path simulation–replay architecture: leveraging Hypatia for offline, high-fidelity path modeling and feature extraction to generate reusable end-to-end trajectory files, which are then replayed in real time on actual hardware and software platforms. This tightly couples simulation with network emulation. The architecture supports multi-constellation scenarios and precisely reproduces dynamic network characteristics—including latency, bandwidth, and topology evolution. Experimental evaluation demonstrates a correlation of 0.96 between simulated and replayed results, significantly enhancing the realism and reproducibility of protocol and application assessments in LEO satellite networks.

Existing network simulation models neglect application-layer behavior, leading to traffic distortion and hindering robustness evaluation of monitoring and anomaly detection systems. To address this, we propose the first framework that models application-layer behavior as learnable and composable probabilistic processes. Specifically, it estimates probability density functions from real-world traffic traces and employs behavioral pattern convolution to generate dynamic, scalable, and realistic traffic. We further design a lightweight simulation engine supporting coexistence of multiple applications on a single machine and real-time behavioral modulation. Experimental results demonstrate that our approach significantly improves test coverage while achieving traffic distributions closely aligned with real-world scenarios. The open-source implementation has been validated on large-scale production networks.

This study addresses the accuracy limitations of network simulation in research on the Differentiated Services (DiffServ) architecture by proposing a hybrid validation methodology that integrates real-world experimentation with simulation. The authors deploy representative scenarios in a physical testbed and replicate identical configurations in a re-engineered simulation environment, enabling systematic comparison of key performance metrics. To enhance fidelity, they substantially refactor the DiffServ modules of widely used network simulators, significantly improving their modeling capabilities. Their findings demonstrate that uncalibrated simulations can yield misleading conclusions in advanced networking studies. The proposed approach not only exposes critical shortcomings in current simulation practices but also offers a practical, actionable pathway toward higher simulation accuracy for DiffServ-based research.

Existing network path simulators struggle to cope with the growing heterogeneity, scale, and dynamism of Internet paths. To address this, we propose a modular and scalable network path simulation platform built upon a novel “Unit”-based architecture: simulation functionalities are decomposed into lightweight, asynchronously interfaced modules that support hierarchical composition and user-defined extensions. Integrated with multi-level linking mechanisms and a distributed communication design, the platform enables concurrent simulation of hundreds of gigabit-speed paths on a single machine and scales seamlessly to multi-node clusters. This design significantly enhances simulation flexibility, maintainability, and experimental scale. The platform has already facilitated efficient validation of multiple modern transport protocols and serves as a high-fidelity, accessible infrastructure for large-scale network research and systems development.

To address the challenges of assessing non-shortest-path diversity in large-scale interconnection networks and the poor scalability of conventional packet-level simulators, this paper proposes a lightweight simulation framework tailored for extreme-scale networks. By identifying memory and event-scheduling bottlenecks in mainstream simulators, we introduce three core techniques: compact data structures, lazily bound event queues, and lock-free memory pools—significantly reducing both memory footprint and synchronization overhead. Our framework enables fine-grained, packet-level simulation of data center and HPC networks with over one million endpoints on a single commodity laptop, achieving a throughput of 10 million packets per second—three orders of magnitude higher than state-of-the-art shared-memory simulators. The open-source framework supports rapid prototyping and validation of novel interconnect protocols, providing a reproducible, high-fidelity foundation for path diversity analysis and performance optimization in ultra-large-scale networks.

This work addresses the limitations of existing cybersecurity datasets, which are predominantly static and ill-suited for enabling controllable replay and traceability in heterogeneous, multi-protocol environments. To overcome this, the authors propose a scenario-oriented, container-native testing platform that leverages declarative configuration to parameterize the generation of both adversarial and benign network traffic, log collection, and dataset integration. The platform encapsulates 60 attack scenarios, nine target services, and benign traffic generators within single-purpose containers and integrates them into an automated pipeline for feature extraction and experimental execution. Designed with reproducibility, auditability, and extensibility in mind, the framework significantly reduces operational bias and supports fully traceable, reproducible experiments in complex settings such as IoT and IIoT networks.

Existing simulation platforms struggle to efficiently support high-fidelity, scalable evaluation of protocols and applications for large-scale low Earth orbit (LEO) satellite constellations. This work proposes a distributed simulation architecture based on declarative workflows, leveraging Linux containers to emulate satellite nodes and employing VXLAN overlay networks to model inter-satellite links. The system integrates Etcd for coordination, IS-IS for intra-constellation routing, and ideal time-varying routing to construct dynamic topologies, while decoupling physical-layer modeling from routing logic. This design enables end-to-end protocol experimentation and is particularly well-suited for validating emerging architectures such as SRv6. Experimental results demonstrate that jointly optimizing handover strategies for both user and gateway service satellites significantly enhances overall network performance.

Traditional callback-based discrete-event network simulators often suffer from fragmented logic, leading to “callback hell” and stack tearing, which result in complex and poorly maintainable code. This work proposes CoDES—a novel coroutine-based paradigm for discrete-event simulation—that reimagines coroutines not merely as a performance optimization but as a foundational mechanism to reconstruct the network simulation development workflow. By enabling developers to express network events in a natural, sequential manner, CoDES significantly enhances code clarity. Integrated into NS-3, CoDES supports sequential modeling of protocols such as MPI, HPCC, and RIP, achieving up to a 62.3% reduction in code volume and an 82.6% decrease in structural complexity, all while preserving simulation accuracy, runtime performance, and memory overhead.

This work proposes a semantic-aware, intent-based wireless resource orchestration approach tailored for Open RAN architecture and evaluates its performance under realistic observability constraints. To this end, the authors develop a scalable ns-3-based simulation framework integrating a RAN Intelligent Controller (RIC) with distributed applications (dApps), enabling intent-driven orchestration across multiple time scales, validated through a wireless resource management use case. A novel Intent Satisfaction Score (ISS) metric is introduced, combining distortion and perception-oriented measures to achieve, for the first time in Open RAN simulations, semantic-aware closed-loop control. Experimental results demonstrate that the proposed method significantly reduces radio resource consumption and computational overhead while effectively improving intent satisfaction, at the cost of only moderate degradation in packet delivery ratio and throughput.

This work proposes a novel robustness-centered evaluation paradigm for network protocol assessment by introducing automated adversarial testing. Traditional approaches, which rely on manual testing or real-world traces, often fail to cover unanticipated network conditions and may overlook critical flaws. In contrast, the proposed method leverages machine learning to generate adversarial network environments and integrates adversarial optimization with noise-robustness techniques to systematically stress-test both single-path and multipath congestion control protocols. Applied to the Linux kernel, this approach successfully produced effective adversarial scenarios for 27 congestion control algorithms, uncovering previously unknown kernel vulnerabilities and protocol limitations. The results demonstrate a significant enhancement in the depth and breadth of robustness evaluation for network protocols.