Dataset description This dataset contains snapshots of host telemetry metrics collected during different workload conditions. It is intended for training and evaluating anomaly detection models (e.g., reconstruction-based autoencoders). The metrics cover: Per-core CPU utilization breakdown by state (percent) Memory metrics (bytes) Files This Zenodo record should include the dataset in CSV format and this README.md. Recommended file naming (adjust to your actual file names): train_no_load.csv, train_medium_load.csv, train_high_load.csv: training CSVs for different workload scenarios test_telemetry.csv: test CSV used for evaluation If you prefer a single file, you can also concatenate all rows into one telemetry_dataset.csv and add two extra columns: scenario in {no_load, medium_load, high_load, ...} split in {train, test} How the dataset was generated A node was instrumented with a telemetry pipeline (e.g., Prometheus + node exporter) to collect CPU and memory metrics at a fixed sampling interval. Multiple workload scenarios were executed (e.g., no load / medium load / high load). Metrics were exported to CSV with a fixed column order. Each row represents one telemetry snapshot. Notes: CPU values are percentages per core and CPU state. Due to sampling/aggregation, values may occasionally slightly exceed 100. node_memory_MemTotal_bytes is constant for a given machine (total installed memory). Columns All CSV files share the same schema (38 columns). Units and meanings are listed below. CPU columns (percent) For each core i in {0,1,2,3}, the following columns represent the percentage of time spent in the given CPU state during the sampling window: cpu_i_idle, cpu_i_iowait, cpu_i_irq, cpu_i_nice, cpu_i_softirq, cpu_i_steal, cpu_i_system, cpu_i_user Memory columns (bytes) memory_used_bytes: used memory in bytes (as exported by the telemetry pipeline) node_memory_Buffers_bytes: memory used for buffers node_memory_Cached_bytes: memory used for page cache node_memory_MemAvailable_bytes: estimate of memory available for starting new applications node_memory_MemFree_bytes: unused memory node_memory_MemTotal_bytes: total installed memory Column descriptions (full list) Column Unit Description cpu_0_idle % Core 0 CPU time in idle state cpu_0_iowait % Core 0 CPU time waiting on I/O cpu_0_irq % Core 0 CPU time servicing interrupts cpu_0_nice % Core 0 CPU time for niced processes cpu_0_softirq % Core 0 CPU time servicing softirqs cpu_0_steal % Core 0 CPU time stolen (virtualization) cpu_0_system % Core 0 CPU time in kernel space cpu_0_user % Core 0 CPU time in user space cpu_1_idle % Core 1 CPU time in idle state cpu_1_iowait % Core 1 CPU time waiting on I/O cpu_1_irq % Core 1 CPU time servicing interrupts cpu_1_nice % Core 1 CPU time for niced processes cpu_1_softirq % Core 1 CPU time servicing softirqs cpu_1_steal % Core 1 CPU time stolen (virtualization) cpu_1_system % Core 1 CPU time in kernel space cpu_1_user % Core 1 CPU time in user space cpu_2_idle % Core 2 CPU time in idle state cpu_2_iowait % Core 2 CPU time waiting on I/O cpu_2_irq % Core 2 CPU time servicing interrupts cpu_2_nice % Core 2 CPU time for niced processes cpu_2_softirq % Core 2 CPU time servicing softirqs cpu_2_steal % Core 2 CPU time stolen (virtualization) cpu_2_system % Core 2 CPU time in kernel space cpu_2_user % Core 2 CPU time in user space cpu_3_idle % Core 3 CPU time in idle state cpu_3_iowait % Core 3 CPU time waiting on I/O cpu_3_irq % Core 3 CPU time servicing interrupts cpu_3_nice % Core 3 CPU time for niced processes cpu_3_softirq % Core 3 CPU time servicing softirqs cpu_3_steal % Core 3 CPU time stolen (virtualization) cpu_3_system % Core 3 CPU time in kernel space cpu_3_user % Core 3 CPU time in user space memory_used_bytes bytes Used memory node_memory_Buffers_bytes bytes Buffers node_memory_Cached_bytes bytes Cached node_memory_MemAvailable_bytes bytes MemAvailable node_memory_MemFree_bytes bytes MemFree node_memory_MemTotal_bytes bytes MemTotal Summary statistics The following table reports per-column data type and summary statistics (min / median / max). This table was computed from the provided file. Column Type Min Median Max cpu_0_idle float64 0 29.03 100.5 cpu_0_iowait float64 0 0.02 16.43 cpu_0_irq float64 0 0 21.77 cpu_0_nice float64 0 0 18.78 cpu_0_softirq float64 0 0 13.74 cpu_0_steal float64 0 0 18.48 cpu_0_system float64 0 0.48 22.55 cpu_0_user float64 0 30.5 63.15 cpu_1_idle float64 0 29 104 cpu_1_iowait float64 0 0.02 22.61 cpu_1_irq float64 0 0 20.17 cpu_1_nice float64 0 0 17 cpu_1_softirq float64 0 0 26.32 cpu_1_steal float64 0 0 17.09 cpu_1_system float64 0 0.43 35.32 cpu_1_user float64 0 30.63 75.72 cpu_2_idle float64 0 28.91 100.4 cpu_2_iowait float64 0 0.01 19.66 cpu_2_irq float64 0 0 14.42 cpu_2_nice float64 0 0 19.61 cpu_2_softirq float64 0 0 16.19 cpu_2_steal float64 0 0 15.58 cpu_2_system float64 0 0.45 33.33 cpu_2_user float64 0 30.7 86.3 cpu_3_idle float64 0 29.01 112.5 cpu_3_iowait float64 0 0.02 14.85 cpu_3_irq float64 0 0 17.67 cpu_3_nice float64 0 0 19.58 cpu_3_softirq float64 0 0 19.25 cpu_3_steal float64 0 0 15.61 cpu_3_system float64 0 0.44 29.21 cpu_3_user float64 0 30.62 70 memory_used_bytes float64 8.89095e+08 1.70806e+09 3.32244e+09 node_memory_Buffers_bytes float64 1.05865e+08 1.16023e+08 1.18623e+08 node_memory_Cached_bytes float64 5.08577e+09 5.39835e+09 5.57918e+09 node_memory_MemAvailable_bytes float64 5.00084e+09 6.61521e+09 7.43418e+09 node_memory_MemFree_bytes float64 0 1.26609e+09 1.96274e+09 node_memory_MemTotal_bytes float64 8.32328e+09 8.32328e+09 8.32328e+09 Reproducing the statistics table To recompute the summary statistics for one or more CSV files (e.g., all training files plus the test file), run the following locally (requires pandas and numpy): python - = 1e6: return f"{v:.6g}" return f"{v:.4g}" print(f"| `{col}` | `{dtype}` | {fmt(mn)} | {fmt(med)} | {fmt(mx)} |") PY