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.

Container networks have become crucial to the overall performance and health of network systems due to the increasing adoption of container technologies. Passive per-packet and per-hop one-way delay (p4 h-OWD) measurement is essential for prompt and accurate detection and localization of network issues in container environments. Existing measurement methods for generic virtual networks require intrusive packet modification to uniquely match packets during p4 h-OWD measurements. However, the overhead and ensuing performance impact induced by high-frequency packet operations during the measurement process have been largely overlooked in the context of lightweight and weak-isolation container networks. This paper demonstrates that even state-of-the-art technologies leveraging the efficient extended Berkeley Packet Filter (eBPF) can introduce significant overhead, impacting both the networking and computing performance of container networks during p4 h-OWD measurements. To effectively reduce the p4 h-OWD measurement tax, which encompasses the measurement overhead and its consequent impact, we propose a non-intrusive method to obtain unique packet identifiers in container networks, thereby avoiding the significant operational overhead associated with intrusive packet matching. Building on this foundation, we present CNDMeas, an efficient eBPF -based technology designed for p4 h-OWD mea-surement within container networks. Evaluation results show that CNDMeas effectively limits the increase in CPU time dedicated to handling software interrupts to within 3 % and reduces the impact on both networking performance by up to 71 % in terms of delay, and computing performance during the measurement process compared to state-of-the-art technologies.

Moderate-scale datacenters are increasingly deployed at the network edge to support low-latency and high-bandwidth internet of Things (IoT) and 5G applications. These applications usually have bulk data to transfer from one datacenter to several datacenters, which can cause a significant amount of bandwidth costs. Traffic engineering (TE) systems at the network edge must minimize the bandwidth costs of multicast transfers across edge datacenters. To avoid service quality degradation, TE should also guarantee transfer deadlines, which have received little attention in existing work. Due to the dynamic bandwidth pricing schemes, it is challenging to strike a good balance between minimizing costs and guaranteeing transfer deadlines. In this paper, we present CDScheduler a cost-efficient scheduling solution for multicast transfers with deadline guarantees. To reduce bandwidth costs, CDScheduler uses Steiner trees for forwarding and adaptive routing that considers bandwidth price variation and transfer demands. We formulate the cost-efficient multicast transfer scheduling problem and propose an algorithm based on linear program relaxation and randomized rounding to find a solution that guarantees timely completion and reduces cost. Extensive evaluations show that CDScheduler can reduce bandwidth cost significantly and outperforms the state-of-the-art solutions by cutting down up to 78% bandwidth cost.