OpenTelemetry AIOps Benchmark Dataset: ML-based Anomaly Detection across Traces, Metrics, and Logs (v2, eight-model study)

Companion dataset and analysis artifacts for the paper "Evaluating ML-based anomaly detection on unified OpenTelemetry telemetry: an empirical study across traces, metrics, and logs" (IEEE Access resubmission, v11). Telemetry was collected from microservice testbeds (Weaveworks Sock Shop and the OpenTelemetry Demo) instrumented with OpenTelemetry and deployed on AWS EKS, then used to benchmark semi-supervised anomaly detection across three OTel signals (distributed traces, infrastructure metrics, application logs). Experiment design. A 24-hour normal baseline was followed by a fault-injection campaign (network delay, HTTP 500 errors, CPU stress, memory stress, and four compound service cascades) using Chaos Mesh, with cooldown windows excluded from evaluation to avoid penalizing models during post-fault recovery. Models evaluated (eight semi-supervised, plus a supervised reference). Isolation Forest, One-Class SVM, LSTM Autoencoder, Transformer Autoencoder, 1D-CNN Autoencoder, LSTM-VAE, Deep SVDD, and DAGMM, with a supervised Random Forest baseline. Evaluated with five-fold cross-validation by repetition and 2,550 Optuna hyperparameter-optimization trials. Key findings. Under the primary engineered-feature representation, detection splits primarily by scoring mechanism rather than by signal type: the two density-based deep models (DAGMM, Deep SVDD) discriminate on every signal, whereas the four reconstruction-based autoencoders collapse to predict-all-anomaly on most signals. DAGMM is the strongest semi-supervised model on every signal under that representation (metrics F1 = 0.906, AUC-ROC = 0.960) and, under a matched cooldown-included comparison, equals or exceeds the supervised Random Forest F1 without using anomaly labels in training. The reconstruction collapse is a score-distribution inversion that density-based scoring avoids; because it produces high F1 at near-random AUC-ROC, AUC-ROC is recommended as the metric of record. A log-representation sensitivity study shows the log ranking is representation-dependent: a Drain/TF-IDF/PCA-21 representation lifts the Transformer Autoencoder above DAGMM on logs (the Drain/TF-IDF/PCA representation is fit unsupervised on the full corpus, a transductive caveat disclosed in the paper). An exploratory window-size sensitivity check (10/30/60 s) finds the qualitative conclusions stable across the granularities tested. Contents. otel-aiops-benchmark-dataset.zip (base v1 deposit): processed Parquet feature files, per-model/signal/fold results, raw anomaly scores, fusion, significance, prevalence, episode, feature-ablation, and supervised-baseline result files, plus chaos-experiment configs, pipeline code, and figure scripts. paper5_v11_analysis_supplement.zip (new in v2): v11 analysis scripts and result CSVs for the window-size sensitivity study, the Drain/TF-IDF/PCA log-representation comparison, threshold robustness, metric reconciliation, prevalence-normalized comparison, inference-cost benchmark, the log-aggregate feature ablation, and the Friedman ranking, with a README mapping each artifact to its manuscript table/section. Bulky regenerable intermediates (raw .npy score arrays, SageMaker output archives, feature parquets) are omitted and reproducible from the included scripts plus the base dataset.