RLGG Datasets

RLGG version of RCAEval's RE1 Datasets: We added anomaly scores computed using and LSTM unsupervised prediction model and plots for each time series. Original Dataset can be found here. Description provided by authors: RE1 Datasets (375 cases). The RE1 datasets, introduced in [1] , contain 375 failure cases collected from three microservice systems (125 cases per system). Each system includes five fault types across five services, with five repetitions per fault-service pair. RE1 exclusively contains metrics data, supporting the development of metric-based RCA methods. The fault types include CPU, MEM, DISK, DELAY, and LOSS. The number of metrics ranges from 49 to 238 depending on the system size. RE1-OB (Online Boutique): 125 cases, 5 services (adservice, cartservice, checkoutservice, currencyservice, productcatalogservice) RE1-SS (Sock Shop): 125 cases, 5 services (carts, catalogue, orders, payment, user) RE1-TT (Train Ticket): 125 cases, 5 services (ts-auth-service, ts-order-service, ts-route-service, ts-train-service, ts-travel-service) File Structure: Each dataset directory follows the naming convention: {benchmark}_{service}_{fault}_{instance} metrics.json: Time-series metrics data inject_time.txt: Fault injection timestamp (Unix timestamp) metrics_dict.pkl: Preprocessed timeseries data with anomaly scores. References:1. Luan Pham, Huong Ha, and Hongyu Zhang. 2024. Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We? In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE '24). Association for Computing Machinery, New York, NY, USA, 706–715. https://doi.org/10.1145/3691620.3695065