Network specification, which describes what an existing network is designed for, can help operators better understand and manage their networks, and is a critical pre-condition for network verification and synthesis tools to work. Existing tools for specification mining either cannot scale to large networks, or scale by sacrificing fidelity. Moreover, the specification contains a huge number of low-level intents (e.g., tens of thousands of pairwise reachability), making it hard for operators to read. To this end, this paper presentsNetMiner, which can mine specification from network configurations, with high scalability, fidelity, and easier to read. The key idea ofNetMineris to faithfully simulate the network routing and forwarding behaviors with control plane simulators and data plane verifiers, so as to achieve high fidelity. Meanwhile,NetMinerimproves the scalability by identifying relevant failure scenarios, and aggregating them to significantly reduce the number of needed simulations. Moreover,NetMinerclusters similar low-level intents into a high-level intent, to make the specification more concise and easier to read. Experiments using real configurations from a large cloud service provider and synthetic configurations show thatNetMinercan mine specification$10\times $faster, and reduce the number of intents by$100\times $, compared to state-of-the-art tools.

As the number of data center servers grows into the millions and due to the demand for more accurate, rapid and powerful network fault detection and location, the existing Pingmesh-centric monitoring and diagnostic system is not efficient enough. In this paper, we propose ByteTracker, the first agentless probing and diagnostic system for large-scale data center networks. It does not need to deploy probe processes or make any configurations on end hosts, and all probes are launched by a small number of centralized Probers. ByteTracker achieves accurate, real-time probe path tracking with packet mirroring on switches. By reducing end-host probe noise, precisely identifying network timeout probes, accurately tracking probe paths, and marking the failed switch with multiple network timeout probes, ByteTracker can locate network failures with nearly 100% accuracy. We have deployed ByteTracker in all of our data centers for over half a year. During deployment, ByteTracker can detect almost all network anomalies and locate them within 5 seconds with 100% accuracy.

The data plane verification of networks in hyperscale environments is challenging due to the complexity and size of modern networks. In this paper, we introduce Medusa, a novel verifier that efficiently analyzes large data plane models using parallel processing on multi-core CPUs. First, we propose a new data structure called RANGESET, which overcomes the parallelism limitations of existing popular data structures such as Binary Decision Diagrams (BDD) used in data plane verifiers. Next, we leverage multi-core processing by dividing the network into distinct groups and assigning each group to a separate thread for computation. The results are then integrated for comprehensive verification. By optimizing the use of multi-core systems, we enhance computational efficiency and accelerate the verification process. Experimental results demonstrate that Medusa outperforms existing tools in terms of speed and memory. For instance, in a network with O(10K) devices and O(1M) forwarding rules, Medusa can detect loops in approximately 5 seconds, outperforming other Data Plane Verifiers (DPVs) where some cannot model and analyze the network. Moreover, in networks that we could compare with other state-of-the-art DPVs, Medusa provides a substantial improvement, with speedups up to 600X, 4000X, and 800X compared to alternatives like Flash, APKeep, and Tulkun, respectively.

Network specification, which describes what an existing network is designed for, can help operators better understand and manage their networks, and is a critical pre-condition for network verification and synthesis tools to work. Mining specification with existing tools either cannot scale to large networks, or scale better at a cost of sacrificing fidelity. This paper presents NetMiner, which can simultaneously achieve high scalability and fidelity. The key idea of NetMiner is to use off-the-shelf network simulators to compute routes, and then check properties with data plane verifiers, so as to achieve a high fidelity. At the same time, NetMiner improves the scalability by identifying relevant failure scenarios, and aggregating them to significantly reduce the number of needed simulations. This process is solely based on the routes returned by the simulators and therefore preserving fidelity. Experiments using real configurations from a large cloud service provider and synthetic configurations show that NetMiner is about 10 times faster than the state-of-the-art.