At present, the microservice architecture is widely used in modern software development for its flexibility and scalability. However, the huge data scale and complex invocation relationships between services make the root cause localization of faults in microservice systems extremely difficult. Some of the current root cause localization methods based on metrics data use history fault data to effectively improve the localization results, but there are challenges such as not representing history faults effectively and limiting the localization results to repetitive faults that have occurred in history. In this paper, we propose an automatic root cause localization framework MicroHFRCL based on the history fault library to address the above issues. MicroHFRCL constructs an instance causal graph based on metric data for causal analysis. The instance causal graph is weighted by encoding the anomalous subgraph and calculating the similarity of history faults. The PageRank algorithm is used to locate the root cause of faults. Among them, MicroHFRCL learns the structure and feature information of fault anomalous subgraphs through GCN and Transformer models, achieving effective representation of history faults and fast calculation of similarity in history fault codes, improving the efficiency of repetitive fault localization, and effectively solving the problem of the limitation of using history faults for root cause localization results. We implemented MicroHFRCL and tested it on the fault dataset collected by a benchmark microservice test system. Compared with the latest baseline models, MicroHFRCL has significantly improved the localization accuracy, and can also achieve good results in the case of small-scale history faults.

We present the first large-scale database of measured spatially-varying anisotropic reflectance, consisting of 1,000 high-quality near-planar SVBRDFs, spanning 9 material categories such as wood, fabric and metal. Each sample is captured in 15 minutes, and represented as a set of high-resolution texture maps that correspond to spatially-varying BRDF parameters and local frames. To build this database, we develop a novel integrated system for robust, high-quality and -efficiency reflectance acquisition and reconstruction. Our setup consists of 2 cameras and 16,384 LEDs. We train 64 lighting patterns for efficient acquisition, in conjunction with a network that predicts per-point reflectance in a neural representation from carefully aligned two-view measurements captured under the patterns. The intermediate results are further fine-tuned with respect to the photographs acquired under 63 effective linear lights, and finally fitted to a BRDF model. We report various statistics of the database, and demonstrate its value in the applications of material generation, classification as well as sampling. All related data, including future additions to the database, can be downloaded from https://opensvbrdf.github.io/.