cloud network architecture design

本文针对云数据中心虚拟网络请求资源分配问题，提出基于模式匹配的在线虚拟网络嵌入方法，通过构建匹配规则最大化资源利用。

Manual network configuration is error-prone and often violates standards, leading to misconfigurations and inconsistency. This paper proposes a UML-based model-driven approach: first, an extensible, semantically precise network configuration metamodel is defined to formally capture device capabilities, topological constraints, and change requirements; second, declarative model-to-command mapping rules and a code generator are developed to automatically translate high-level configuration models into vendor-specific CLI commands. To our knowledge, this is the first academically rigorous end-to-end automation framework that bridges UML modeling and production-grade network configuration. Evaluated in a real-world OSPF migration project at Shinshu University’s campus network, the approach fully automated the generation of all device configurations; post-deployment validation confirmed that network behavior strictly conformed to specifications—demonstrating correctness, practicality, and engineering feasibility.

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.

该研究使用软件定义网络（SDN）技术设计校园网的核心层，通过RouteFlow平台和OSPF协议提高网络的可用性和路由效率。

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.

This work addresses the core challenge in network automation: automatically generating deployable network topologies from natural language requirements while satisfying structural and resilience constraints. We propose a large language model (LLM)-based, constraint-driven framework that translates natural language into compliant topologies through hierarchical intent parsing and systematic validation. To facilitate evaluation, we introduce the first benchmark for this task, releasing a public dataset encompassing four real-world scenarios and characterizing common generation error patterns. Extensive experiments across multiple proprietary and open-source LLMs demonstrate the framework’s effectiveness, with performance quantified using metrics including topological correctness, node/edge F1 scores, and server-content connectivity. Our results provide actionable guidance for model selection in AI-driven network design.

This work addresses the challenges of configuration fragility and limited adaptability in software-defined networking (SDN) under dynamically changing topologies, particularly in edge environments where stringent requirements for low latency, privacy preservation, and local execution prevail. To this end, the authors propose an end-to-end framework tailored for edge deployment, featuring a novel topology embedding–driven retrieval-augmented generation mechanism (TopoRAG). This framework integrates graph neural networks (GNNs) with a multi-agent collaborative architecture, wherein planner, generator, and verifier agents operate in a closed loop to achieve topology-aware configuration synthesis and localized repair. Experimental results demonstrate that the approach enables highly consistent automatic configuration generation and precise repair under topological dynamics, significantly enhancing the reliability and deployment efficiency of SDN configurations in edge settings.

This study addresses the unpredictable end-to-end latency in cloud virtualized environments, which stems from virtualization overheads in CPU, I/O, and network resources. Through systematic network measurement experiments across diverse virtualization platforms—including KVM, LXC, and Docker—under multidimensional workload conditions, the authors collect packet round-trip time data to construct a high-quality dataset suitable for machine learning–based network performance modeling. By integrating data preprocessing, correlation analysis, dimensionality reduction, and clustering techniques, this work presents the first quantitative evaluation of latency impacts across multiple virtualization technologies. The resulting dataset effectively supports network performance prediction and intelligent resource scheduling, providing an empirical foundation for performance optimization in cloud environments.

Traditional data center networks struggle to balance cost and fault tolerance, while random graph topologies have long remained impractical due to the lack of scalable routing and cabling solutions. This work presents RNG, the first successful deployment of a random graph–based architecture in large-scale production environments. RNG introduces a novel distributed routing protocol that supports a large number of edge-disjoint paths and leverages passive optical components to implement endpoint shuffling, significantly simplifying cabling complexity. Under diverse traffic patterns, RNG matches or exceeds the performance of fat-tree topologies while reducing deployment costs by up to 45%. It has since become the default data center network architecture for the majority of Amazon’s workloads.

This study addresses the lack of systematic, large-scale analyses of structural properties in software feature models, which has hindered the understanding and evolution of variability models. For the first time, it systematically applies large-scale network analysis to 5,709 variability models drawn from 20 repositories. By constructing graphs capturing transitive dependencies and conflicts among features, and integrating graph modeling with network-theoretic and statistical analyses, the work uncovers cross-domain structural commonalities—such as dependency dominance, high centralization, and characteristic degree distributions—as well as domain-specific deviations. These findings provide novel empirical insights and a foundation for identifying pivotal features, guiding modular decomposition, and assessing structural fragility in variability-intensive systems.