In-band Network Telemetry(INT) enables fine-grained telemetry by editing the packet header with the capability of programmable data plane to carry network status. However, INT could cause significant telemetry overhead without an effective system design. Existing measurement systems attempt to reduce this overhead by employing fixed-frequency INT sampling. Nonetheless, these methods lead to frequent measurements of network ports with large flows while neglecting ports with small flows for extended periods. In this paper, we introduce a lightweight passive telemetry system based on INT, called INTSelection. The core idea is to use a flow selection algorithm at the centralized controller so as to measure all active ports. Compared with the current method, INT-Selection reduces the bandwidth overhead by 58.2% and 2.7%.

Heterogeneous networks utilize devices from multiple vendors to enhance failure prevention, but also pose challenges in network device management. Network configuration translation, as a critical aspect of management, facilitates the upgrading of pairwise nodes that back up each other and device replacements across different vendors. Manually translating configuration requires a significant amount of time and demands that administrators have ample experience. To address this, we introduce the Network Configuration Translation MultiAgent System (NetMAS), designed to manage heterogeneous manuals and streamline configuration translation. By efficiently handling diverse manuals, NetMAS simplifies the translation of network configurations across different devices and vendors. NetMAS consists of three core components: a hierarchical manual retriever, a detachable network configuration translator, and a memory module. Leveraging the advanced function-calling capabilities of the Large Language Models, NetMAS eliminates the need for external training, significantly enhancing overall efficiency. Our experimental results demonstrate that NetMAS can effectively retrieves configuration manuals. The retrieval accuracy reaches 94.11% when considering the top-5 results. Furthermore, it supports manuals from various vendors and devices. When translating from Cisco to Huawei, H3C, and Juniper, NetMAS improves accuracy by an average of$25.48 \times$and$12.38 \times$, respectively, compared to directly using GPT-4o and Deepseek.

Segment Routing over IPv6 (SRv6) is an innovative and adaptable source routing technique that enhances interconnection services. It plays a pivotal role in next-generation networking technologies, providing crucial support for network telemetry, computing power networks, and related technologies. The end-to-end connectivity capability of SRv6 is highly regarded by ISPs, driving its widespread deployment in networks. However, configuring an SRv6 network can be challenging and prone to errors due to the complexity of low-level configuration languages and numerous protocol parameters. To address this issue, we present AutoSRv6, a system designed to synthesize SRv6 configurations for large, evolving networks using high-level abstractions of network topology and policies. AutoSRv6 leverages formal constraint-solving techniques and SMT solvers to compute protocol parameters and generate configuration files that align with network policies. Furthermore, AutoSRv6 incorporates a mechanism to overcome the constraints imposed by hardware, mapping the end-to-end path to a SID (Segment Identifier) sequence. We have developed a prototype of AutoSRv6 and conducted experiments on diverse network topologies, evaluating its performance with various network policies. The results show that autoSRv6 can generate the network configuration satisfying the policy, the time cost of IGP synthesis is better than the existing method, and the length of the segment list is optimized by more than 2 times.

Damage or relocation of network devices necessitates the replacement of devices. Additionally, manufacturers of devices may also change due to factors such as price, policies, and functional limitations. This brings great needs of converting existing configurations to fix new devices and ensuring consistent network policy behaviors, which is configuration translation. Manually translating requires network administrators to possess significant configuration expertise and to spend extreme time translating thousands of commands for a core device. Therefore, we propose ConfigTrans, a novel configuration translation method for commands with and without parameters based on constraint solving and large language models (LLMs), respectively. For commands with parameters, we explore constraints about parameters, commands, and format limitations, and design a heuristic algorithm to solve them. For commands without parameters, we utilize an LLM to find the translation. The most likely several commands are selected based on semantic similarity, and the final result and keywords in it are chosen by the LLM. To meet the requirements of the view, the result commands are arranged while supplementing missing higherview commands. Experimental results show that our method achieves an accuracy rate of 82.47 % for translating commands with parameters and 75.5 % for commands without parameters.