---
id: "bizitobs-l2c-5t"
slug: "bizitobs-l2c-5t"
name: "BizITobs Cloud Infrastructure Telemetry"
domain: "Technology & Cloud Infrastructure"
taskType: "time_series_forecasting"
numRows: 31968
numColumns: 9
targetColumn: "target_0"
primaryMetric: "Timestral-123M SQL"
primaryScore: "0.2887"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/bizitobs-l2c-5t/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/19v8yC8Sr9u8YmFlS2YhibjeRT6bkEHT2o_GoDLJAh8w/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/bizitobs-l2c-5t"
---

# Dataset: BizITobs Cloud Infrastructure Telemetry (bizitobs-l2c-5t)

> High-resolution cloud infrastructure telemetry benchmark tracking 7 multivariate operational metrics for a critical enterprise cluster over a 24-hour horizon (288 steps at 5-minute intervals) to preempt outages and optimize autoscaling.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `time_series_forecasting` |
| **Business Domain** | Technology & Cloud Infrastructure |
| **Target Column** | `target_0` |
| **Rows (Instances)** | 31,968 |
| **Features (Columns)** | 9 |
| **Primary Evaluation Metric** | Timestral-123M SQL |
| **Best Benchmark Score** | **0.2887** |
| **Prediction Horizon** | 288 steps |
| **Seasonality** | 288 |
| **Time Frequency** | 5-Minute |
| **Timestamp Column** | `timestamp` |
| **Entity / ID Column** | `id` |

## Business Decision Context
**Context:** Modern cloud-native microservices and mission-critical enterprise clusters generate telemetry at high velocity, requiring infrastructure engineering and Site Reliability Engineering (SRE) teams to make proactive capacity allocation and pod autoscaling decisions across daily diurnal cycles.
**Objective:** Forecast 7 core infrastructure telemetry metrics simultaneously over a 288-step (24-hour) horizon at 5-minute cadence to anticipate system saturation and SLA degradation before outages occur.
**Decision Impact:** Under-forecasting resource demand triggers severe latency spikes, cascading service timeouts, customer SLA penalties, and potential unplanned downtime; over-forecasting results in excessive cloud node provisioning and inflated infrastructure overhead.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `id` | `categorical` | identifier | 1 | 0 | Unique categorical identifier representing the specific monitored cluster or compute node partition |
| `timestamp` | `datetime` | timestamp | 31,968 | 0 | 5-minute interval timestamp tracking continuous telemetry collection across the observation window |
| `target_0` | `numeric` | target | 1,541 | 0 | Normalized CPU compute load across provisioned virtual processor cores |
| `target_1` | `numeric` | target | 57 | 0 | Proportional consumption of allocated system RAM and container memory buffers |
| `target_2` | `numeric` | target | 49 | 0 | Rate of persistent storage input/output throughput and block storage operations |
| `target_3` | `numeric` | target | 27 | 0 | Combined ingress and egress network throughput across cluster interfaces |
| `target_4` | `numeric` | target | 81 | 0 | Mean end-to-end service response duration for routed HTTP/RPC transactions |
| `target_5` | `numeric` | target | 121 | 0 | Volume of completed incoming business requests processed per unit time |
| `target_6` | `numeric` | target | 81 | 0 | Frequency of observed 5xx HTTP response codes and unhandled application runtime exceptions |

## FEV Benchmark Leaderboard (AutoGluon Time-Series Suite)
Empirical probabilistic evaluation across standard foundation models and baselines:

| Rank | Method / Model | Family | Organization | SQL Win Rate (%) | SQL Skill Score | MASE Win Rate (%) | Median Latency | Execution |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | **Timestral-209M** | Foundation Model | — | 87.7% | 50.39 | 86.0% | 0.51s | Server (CUDA / PyTorch) |
| 2 | **Timestral-123M (Carla Engine)** | Foundation Model | — | 86.8% | 50.18 | 85.0% | 0.32s | WebGPU (Browser / Sheets) |
| 3 | **TimeRSI** | Closed-Source API | — | 84.8% | 51.24 | 83.8% | 2512s | Server (CUDA / PyTorch) |
| 4 | **t0-gamma** | Foundation Model | — | 84.4% | 49.42 | 81.8% | 4.38s | Server (CUDA / PyTorch) |
| 5 | **Timestral-28M** | Foundation Model | — | 79.2% | 48.42 | 77.0% | 0.11s | Server (CUDA / PyTorch) |
| 6 | **timesfm-3** | Foundation Model | — | 78.5% | 48.99 | 77.0% | 3.41s | Server (CUDA / PyTorch) |
| 7 | **ChakraTS** | Baseline | — | 78.2% | 48.14 | 79.5% | 14.22s | Server (CUDA / PyTorch) |
| 8 | **Lingjiang2.0** | Baseline | — | 73.9% | 47.02 | 71.3% | 8.89s | Server (CUDA / PyTorch) |
| 9 | **Chronos-2** | Foundation Model | — | 70.4% | 47.28 | 68.4% | 0.84s | Server (CUDA / PyTorch) |
| 10 | **timeh2o-v1-tiny** | Foundation Model | — | 69.9% | 45.49 | 67.0% | 0.19s | Server (CUDA / PyTorch) |
| 11 | **Timestral-5M** | Foundation Model | — | 69.0% | 46.19 | 67.7% | 0.04s | Server (CUDA / PyTorch) |
| 12 | **Toto-2.0-2.5B** | Baseline | — | 67.5% | 44.42 | 65.7% | 5.58s | Server (CUDA / PyTorch) |
| 13 | **t0-beta** | Foundation Model | — | 67.4% | 46.65 | 64.5% | 2.29s | Server (CUDA / PyTorch) |
| 14 | **Toto-2.0-1B** | Baseline | — | 66.4% | 44.43 | 65.5% | 2.48s | Server (CUDA / PyTorch) |
| 15 | **TiRex-2** | Foundation Model | — | 65.8% | 45.50 | 63.4% | 0.27s | Server (CUDA / PyTorch) |

## Provenance & Downloadable Artifacts
- **Dataset Parquet File:** [Direct Parquet Download](https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/bizitobs-l2c-5t/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/19v8yC8Sr9u8YmFlS2YhibjeRT6bkEHT2o_GoDLJAh8w/edit?usp=sharing)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/bizitobs-l2c-5t](https://carlahq.eu/datasets/bizitobs-l2c-5t)