Data plane verification (DPV) can be critical in ensuring the network operates correctly. To be useful in practice, they need to be: (1) fast so as to prevent significant packet loss or security violations; (2) scalable so as to accommodate today's large-scale network architecture. Current DPV tools struggle to meet these requirements due to their centralized architecture. To be concrete, there is a bottleneck for a single-point server to perform real-time DPV tasks. Furthermore, a single-point server makes it hard to collect real-time data plane updates from every device in large-scale networks.

There is an increasing interest in data plane verification, which is designed to automatically verify network correctness through directly analyzing the data plane. Recent data plane verifiers have been able to do real-time sub-millisecond per rule verification. However, we observe that in real-world networks, individual data plane updates rarely occur. On the contrary, there are always a certain number of updates generated in a short period of time, called asburst updates, due to high-level user intend or uncertain network events. When it comes to this real-life scenario, yet, the current equivalence class (EC) based methods are unable to solve themodel-wide changesproblem caused by the EC itself, which significantly slows down the verification speed of burst updates. To overcome this limitation, we present EPVerifier, a fast, scalable data plane verifier accelerating burst updates verification with edge-predicate (EP). Instead of classifying packets into ECs according to global forwarding behavior, the EPVerifier uses one EP per edge to represent all packets that can pass through. Furthermore, with EPs that clearly have localized properties, we introduce a rule type extension that does not require a change in the granularity of the network model to support ACLs and NATs that are prevalent in real devices, and obtain better-performing parallelism by dividing the verification task based on switches. Experiments on both dataset simulations and real-life deployments show that EPVerifier achieves 2-$10\times $faster data plane verification than the state-of-the-art and such advantage expand with the data plane’s complexity and update scale growth.

Laser metal deposition (LMD) is an additive manufacturing method for metal parts by using focused thermal energy to fuse materials as they are deposited. During LMD, transient thermal signatures such as the in-situ thermal images of melt pool, contain rich information about process performance. Early prediction of such transient thermal signatures provides opportunities for process monitoring and defect prevention. While physics-based models of LMD have been conventionally used for thermal signature prediction, they have limitations and are computationally expensive for real-time prediction. A scalable, efficient data-science-based model is therefore needed. This paper develops a deep-learning-based surrogate model, called LMD-cGAN, to predict and emulate the transient thermal signatures in LMD. The model generates images for the thermal dynamics of melt pool conditionally on the deposition layer. It enables early prediction of future-layer thermal signatures for an in-process part based on its early-layer thermal signatures. To respect the physics in LMD, a physics-guided image selection (PGIS) mechanism is integrated with LMD-cGAN to calibrate the predictions against physical benchmarks of transient melt pool for the process. The effectiveness and efficiency of the proposed method are demonstrated in a case study on the LMD of Ti-4Al-6V thin-walled structures. Note to Practitioners—With online sensing, many LMD applications have real-time process data that convey valuable information about the process status and part quality. The proposed method leverages these data for thermal signature prediction. LMD-cGAN is a deep-learning-based surrogate model that learns the population profile of real thermal signatures and generates thermal signatures from there. The proposed PGIS mechanism in LMD-cGAN ensures the physical validity of these predictions by benchmarking them against physical insights about the process. LMD-cGAN can be applied to predict thermal signatures in future layers based on early-layer thermal signatures of an in-process part (an implicit assumption here is that the in-process part to be predicted for is the same type). LMD-cGAN can also be applied to emulate thermal signatures in specific layers. To generate thermal signatures for generic, non-defect parts, the training data should be selected with caution – the part where the data were collected should have no obvious defects, so the thermal signatures generated by LMD-cGAN show the regular thermal dynamics. Compared with pure physical models, the proposed method incorporates process uncertainties captured from the early-layer data, hence “on-the-fly” emulation of the melt pool, while characterizing the inherent relationship between the LMD process and thermal signatures.