networking

This work addresses the lack of systematic educational resources in high-performance computing (HPC) networking, which poses a significant barrier for researchers entering the field. It presents the first comprehensive integration of the HPC networking stack, covering communication protocol layers, programming interfaces such as MPI, control plane mechanisms, high-speed interconnect technologies, and custom link-layer hardware. The exposition is anchored by a detailed case study of the El Capitan supercomputer architecture at Lawrence Livermore National Laboratory. By offering a well-structured, practice-oriented primer, this contribution fills a critical educational gap and substantially lowers the entry barrier for researchers seeking to master core HPC networking technologies.

Ensuring functional correctness and performance resilience of network protocols under component failures and adversarial attacks remains a significant challenge. Method: This paper proposes a synergistic analysis framework integrating formal verification with attack synthesis. It models protocol behavior using a formal specification language and employs logical predicates, trace analysis, and model checking to achieve closed-loop verification—simultaneously establishing correctness guarantees and automatically generating realistic attack scenarios. Contribution/Results: Diverging from conventional unidirectional verification, our approach innovatively embeds attack-path generation directly into the verification workflow, enabling reproducible and interpretable failure attribution. Experimental evaluation across multiple mainstream network protocols demonstrates substantial improvements in vulnerability detection rates and attack-surface characterization accuracy. The results validate the feasibility and practicality of formal methods for deep, security-critical analysis of complex network protocols.

Novice learners in network science lack systematic, interdisciplinary guidance. Method: We propose the “Network Scientist Training Map” framework, integrating diverse methodological tools—including graph theory, random graph models, spectral graph theory, network embedding, statistical inference, dynamical modeling, and interpretable machine learning—restructuring core paradigms in an accessible, unified manner that emphasizes methodological synthesis over technical accumulation. Contribution/Results: First, we formally establish pure network science as an autonomous academic discipline. Second, we construct a structured, full-stack knowledge graph covering foundational concepts and techniques. Third, by transcending the limitations of single-discipline textbooks, the framework enables learners with no prior background to rapidly develop a coherent, systems-level conceptual framework—thereby advancing network science from an application-oriented auxiliary field toward ontological independence.

In enterprise network engineering, physical topology modifications and device configuration updates have long relied on error-prone, inefficient manual processes; existing automation research predominantly focuses on configuration synthesis while neglecting co-evolution with topology changes. This paper proposes the first intent-driven, closed-loop automation framework tailored for enterprise networks. It integrates multimodal large language models (MLLMs), optical character recognition (OCR), and a graph-structure-aware visual encoder to jointly understand topology diagrams and textual intent specifications. We introduce a novel topology–configuration co-prompting engineering paradigm and a Cisco-certified scenario fine-tuning mechanism. Evaluated on real-world enterprise deployments, our framework achieves significantly improved topology image parsing accuracy, reduces network design cycle time by over 40%, and attains an 89.2% execution accuracy for topology-modification intents—substantially decreasing manual intervention.

Conventional network telemetry frameworks struggle to support fine-grained traffic measurement, performance diagnostics, and attack detection under stringent memory and computational constraints of high-speed network devices. Method: This paper proposes a lightweight, real-time online telemetry framework that systematically integrates compact data structures—including Bloom filter variants, Count-Min Sketch, and HyperLogLog—with streaming algorithms, hierarchical sampling, and P4-programmable data-plane co-design to comply with hardware limitations. Contribution/Results: Evaluated at line rate exceeding 100 Gbps, the framework reduces memory footprint by over 60% compared to state-of-the-art approaches while maintaining sub-1% flow frequency estimation error. It achieves an optimal trade-off among accuracy, throughput, and resource overhead, thereby significantly enhancing the feasibility and practicality of telemetry in high-bandwidth environments.

This study addresses the unclear mechanisms by which configuration parameters influence topology quality and performance in tactical wireless networks. It systematically investigates the sensitivity of three parameter categories—structural constraints, technology choices, and modeling assumptions—by generating optimized topologies using a tabu search metaheuristic and assessing statistical significance through Friedman and Wilcoxon non-parametric tests. The findings reveal a fundamental distinction between parameters that substantially reshape network topology and those that merely modulate performance magnitude. Moreover, the work identifies scale-dependent technological transition phenomena and threshold effects induced by structural constraints. These insights yield actionable design principles for parameter tuning and topology optimization in mission-critical tactical networks.

This work addresses the challenge of accurately assessing the resilience of end-to-end applications in real-world communication networks due to limited transparency from network operators. To overcome this, the authors propose DRACO, a novel framework that enables systematic modeling and quantitative evaluation of application deployments across national-scale networks without relying on proprietary operator data. DRACO integrates network modeling, publicly available datasets, synthetic data generation, and resilience metric computation to construct a scalable end-to-end evaluation pipeline. The framework was successfully applied to nationwide networks in Germany and France, demonstrating its effectiveness in handling heterogeneous data sources and enabling large-scale resilience assessments.

This study addresses the scalability bottleneck in unicast and multicast routing caused by the dual role of IP addresses as both identifiers and locators. It systematically traces the evolution of Internet routing scalability solutions, first articulating the map-and-encap architecture as a unifying paradigm and identifying the essential conditions for its successful deployment. Through historical protocol analysis, architectural comparisons, and conceptual abstraction—encompassing approaches such as BIER and tunnel encapsulation—the work reveals that BGP’s lack of intra-domain egress router topology abstraction is a fundamental limitation. The paper proposes core principles to guide future scalable routing designs, emphasizing the critical roles of locally driven incentives and effective topology abstraction in protocol evolution.

This study addresses the lack of open, scalable, and geographically diverse measurement methodologies for independently assessing security and privacy risks in cellular networks, Voice over Wi-Fi (VoWiFi), and over-the-top (OTT) services. To overcome this limitation, the authors propose and publicly release the first unified and extensible measurement framework that operates without requiring carrier cooperation. By integrating wireless probing, end-to-end service testing, and an automated experimentation platform, the framework enables controlled, reproducible measurements across diverse geographic regions and multiple technological pathways—including eSIM and VoWiFi. The platform has been successfully deployed in real-world scenarios, achieving, for the first time, a coordinated analysis of these three communication paradigms and uncovering critical security and privacy vulnerabilities inherent in current architectures.