# Azure/AzurePublicDataset

Microsoft Azure Traces

Repository: https://github.com/Azure/AzurePublicDataset
Canonical: https://ross.abutalabs.com/products/azurepublicdataset
Language: Jupyter Notebook
License: CC-BY-4.0
License Family: other
Last push: 2026-06-03T12:53:37+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 85, release rhythm 86, longevity 100
- inputs: {"age_days": 3302, "days_push": 91, "days_rel": 92, "gap_med": 0, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1184, forks 186 (observed 2026-08-28T04:03:54.755691+00:00)

## What it is
A repository of public Microsoft Azure workload traces released for the research community, including VM workloads, Azure Functions invocations and blob accesses, LLM inference traces, and VM benchmark noise data. Trace files are hosted as GitHub release assets with documentation pages per dataset.

## Use cases
- study cloud VM workload patterns from Azure production traces
- research serverless function invocation behavior
- model LLM inference request workloads with token counts
- evaluate VM packing and allocation algorithms
- analyze performance variability of Azure VM SKUs
- benchmark cloud resource management research

## When to choose
- you need real-world cloud workload data for academic research
- you are studying serverless, VM scheduling, or LLM inference capacity planning
- you want reproducible traces tied to published systems papers

## When to avoid
- you need live or streaming cloud telemetry
- you need a tool or library rather than raw trace data
- your research requires non-Azure or non-cloud workloads

## Facets
- artifact type: dataset
- maturity: active
- function: data-science, benchmarking, analytics
- domain: cloud-computing, data-science, large-language-models, microservices
- platform: cross-platform, python
- tags: azure-traces, cloud-workloads, vm-traces, serverless, llm-inference-traces, research-dataset, jupyter-notebook, research

## Member repositories
- Azure/AzurePublicDataset (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:54.755691+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:24:13.588450+00:00, confidence not recorded.
  - readme: https://github.com/Azure/AzurePublicDataset (fetched 2026-08-28T04:03:54.755691+00:00, sha a95a1c9af2f0)
- Data as of 2026-08-30T08:39:29.467469+00:00.