The encapsulated route images provided by vendors hide detailed implementation codes of routing protocol, making it challenging to implement the same routing protocol on heterogeneous devices, as well as to locate and repair the configuration errors. While some works attempt to address the above issues, they fail in large-scale deployment, manifested in insufficiency in complex routing behaviors, lack of clear mapping between routing behavior and protocol standards, and difficulty in obtaining an open-source license. To solve all the problems, we propose RTB, a novel tool to mine the causality between network configurations and routing behaviors, in order to assist operations personnel in better pinpointing configuration errors. First, RTB generates test cases based on configuration features using a coverage-guided combinatorial testing algorithm. Then, RTB employs a differential network model to obtain differential datasets storing the correspondence between configurations and routing behaviors. Finally, RTB utilizes the differential datasets to create multilayer causal graphs, so as to reason about the implementation details and differences in routing protocols, which assists network operation and maintenance personnel in locating configuration errors. Experimental results show that RTB only needs 35.3 % test cases, and improves code coverage by 10 % over Metha. Moreover, RTB can mine the causality with 98 % accuracy, which is 5 % more efficient than existing tools.

Network device misconfigurations can lead to security vulnerabilities and operational failures. Existing validation methods primarily rely on program analysis or machine learning, which are often labor-intensive and data-sensitive. While Large Language Models (LLMs) excel in text analysis, their direct application to configuration validation faces challenges such as limited domain knowledge, hallucinations, and non-interpretability. To address these issues, we propose ConfigChecker, an automated network configuration validation system that integrates LLMassisted structured knowledge extraction with knowledge graphbased reasoning. ConfigChecker involves two key phases: (1) Knowledge Graph Construction - We construct three instructiontuning datasets (EV, PG, and RE) to fine-tune LLMs for structured command information extraction, including view transitions, parameter constraints, and inter-command dependencies. The extracted knowledge is stored in a knowledge graph, providing a foundation for configuration validation. (2) Configuration Validation - A graph-based command validator ensures syntax and semantic correctness, while knowledge graph reasoning and indexing mechanism are used to detect inter-command dependency violations. We evaluate six instruction-tuned LLMs for structured knowledge extraction, achieving up to 90% accuracy on the EV and RE datasets. Additionally, our system shows a 9%-20% improvement in validation accuracy compared to three mainstream large models. Furthermore, the average response time per configuration line remains below 15 milliseconds. Results demonstrate that ConfigChecker significantly enhances validation accuracy and efficiency, providing a novel integration of LLMs and knowledge graphs for automated configuration validation.

Advanced router configuration synthesizers aim to prevent network outages by automatically synthesizing configurations that implement routing protocols. However, the lack of interpretability makes operators uncertain about how low-level configurations are synthesized and whether the automatically generated configurations correctly align with routing intents. This limitation restricts the practical deployment of synthesizers.In this paper, we present NetKG, an interpretable configuration synthesis tool.(i) NetKG leverages a knowledge graph as the intermediate representation for configurations, reformulating the configuration synthesis problem as a configuration knowledge completion task; (ii) NetKG regards network intents as query tasks that need to be satisfied in the current configuration space, achieving this through knowledge reasoning and completion; (iii) NetKG explains the synthesis process and the consistency between configuration and intent through the configuration knowledge involved in reasoning and completion.We show that NetKG can scale to realistic networks and automatically synthesize intent-compliant configurations for static routes, OSPF, and BGP. It can explain the consistency between configuration and intent at different granularities through a visual interface. Experimental results indicate that NetKG synthesizes configurations in 2 minutes for a network with up to 197 routers, which is 7.37x faster than the SMT-based synthesizer.

Configurations deployed in routers govern the routing and addressing of IP-based networks. Automated tools for synthesizing and verifying configurations have been deployed to replace manual configurations, preventing network outages from misconfigurations. However, these tools offer limited assistance in real production networks as manual configuration remains the primary and preferred approach for daily operations. To address this issue, we present NetGenius, a configuration recommendation tool that assists operators in manually editing network configurations like a code editor. NetGenius employs a graph neural network to estimate the importance of neighboring commands to the center command, providing recommendations based on importance scores when the center command is used as input. First, NetGenius leverages a generic graph model named Knowledge Graph to model existing configurations and derives configuration features for each center command by composing it with its neighbors. Subsequently, NetGenius conducts a general approach based on the graph neural network to estimate and calculate corresponding feature importance scores. Finally, NetGenius uses a recommendation mechanism to recommend configurations based on importance scores while adhering to specific constraints. We extensively evaluate the performance of NetGenius using real-world configurations from different vendors. The experimental results demonstrate that NetGenius achieves the highest recommendation accuracy of 98.59% across various datasets. Moreover, when applied to a large-scale network comprising$152,475$configuration commands, NetGenius completes its training within 6 seconds and generates recommendations in 5 milliseconds.

In-band Network Telemetry (INT) technology enables fine-grained network monitoring by encapsulating intra-switch network status into INT probes, which is essential in data center networks for ensuring Quality of Service (QoS). Existing INT-based telemetry systems leverage centralized controllers to compute non-overlapping probe paths, thereby facilitating lightweight and network-wide measurements. However, these systems fail to adapt effectively to network topology changes caused by link or device failures, primarily due to inflexible path planning under dynamic conditions. To address this problem, we propose INT-Source, a unified policy-based network-wide telemetry system for probing and forwarding. First, we design a data plane forwarding mechanism for INT probes to ensure telemetry coverage during topology changes and reduce telemetry overhead. Second, we design a probe packet structure and introduce a switch-based probe verification and discard mechanism to prevent redundant link probing. Third, we introduce two algorithms for INT-Source: a Single-Source algorithm to facilitate deployment and a Multi-Source algorithm to enable lightweight and scalable telemetry. Our evaluation shows that INT-Source reduces bandwidth overhead to 12.5% compared to existing methods across three network topologies. Even with a 10% link failure rate, INT-Source is able to monitor 93.1% of network ports, demonstrating strong robustness.

Network configuration plays a vital role in quality assurance of network services, requiring considerable effort and time. Automatic network configuration approaches are promising due to their capacity to automatically generate and verify configurations. However, these methods suffer from drawbacks, such as generated configuration content being largely unknown to network operators and inefficient for large-scale networks. Manual configuration is thus still the primary way of managing networks. To facilitate editing processes of manual configuration, a network-wide tool for recommending custom keywords is in urgent need. In this article, we propose a keyword recommendation tool that recommends custom keywords across various network devices. We observe that network devices of the same type and role tend to have a unified template and similar configurations, which enables recommending custom configurations between them. However, the vision entails the following three challenges. First, configurations need to be modeled accurately. Second, a wide variety of network protocols need to be supported. Third, relationships between custom keywords might be implicit and difficult to find. To address the challenges, we first built a configuration knowledge graph that could accurately model configurations, extract latent relationships between keywords, and generate explainable recommendations. Then we applied a recommendation framework to the graph for appropriate keyword recommendations. Lastly, to validate the performance, we conduct recommendations on real configurations over 26 000 times. Experimental results indicate that the overall coverage rate for matching expected configurations reaches 79.396%, and the redundancy rate is less than 20%.