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<p>Emulation-based laboratory instruction has become an indispensable component of computer networking education, providing a strong practical complement to theoretical coursework. However, existing network emulation platforms are constrained in both usability and scalability, struggling to sustain stable operation and scale effectively under multi-host deployments with classroom-scale, high-concurrency workloads. To address these challenges, we propose <i>Klonet</i>, a scalable network emulation platform tailored for educational settings. <i>Klonet</i> integrates a browser–server (B/S) architecture, an intuitive graphical user interface (GUI), and course-oriented templates to streamline cross-host configuration, automated deployment, and centralized management, thereby reducing the operational burden on instructors and students. To support scalability, <i>Klonet</i> introduces a resource-aware virtual network mapping method that jointly considers CPU and bandwidth constraints, partitions experimental topologies when needed, and maps virtual nodes and links across multiple hosts, where a MaxRemain-based selection criterion effectively avoids resource fragmentation. Comprehensive evaluations under static workloads and dynamic operation demonstrate that <i>Klonet</i> can stably support networks with up to thousands of nodes, maintain a high deployment success ratio and efficient resource utilization across a range of task intensities, and outperform comparable schemes. These results indicate that <i>Klonet</i> strikes a practical balance among usability, manageability, and scalability for modern networking courses.</p>

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A scalable and usable network emulation platform for laboratory instruction in networking education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

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  • Jin Zhang,
  • Jiajing Zhou,
  • Jingzhao Xie,
  • Gang Sun,
  • Hongfang Yu

摘要

Emulation-based laboratory instruction has become an indispensable component of computer networking education, providing a strong practical complement to theoretical coursework. However, existing network emulation platforms are constrained in both usability and scalability, struggling to sustain stable operation and scale effectively under multi-host deployments with classroom-scale, high-concurrency workloads. To address these challenges, we propose Klonet, a scalable network emulation platform tailored for educational settings. Klonet integrates a browser–server (B/S) architecture, an intuitive graphical user interface (GUI), and course-oriented templates to streamline cross-host configuration, automated deployment, and centralized management, thereby reducing the operational burden on instructors and students. To support scalability, Klonet introduces a resource-aware virtual network mapping method that jointly considers CPU and bandwidth constraints, partitions experimental topologies when needed, and maps virtual nodes and links across multiple hosts, where a MaxRemain-based selection criterion effectively avoids resource fragmentation. Comprehensive evaluations under static workloads and dynamic operation demonstrate that Klonet can stably support networks with up to thousands of nodes, maintain a high deployment success ratio and efficient resource utilization across a range of task intensities, and outperform comparable schemes. These results indicate that Klonet strikes a practical balance among usability, manageability, and scalability for modern networking courses.