Azure/AzurePublicDataset

Microsoft Azure Traces

What it solves

This project provides researchers and academics with real-world data from Microsoft Azure to help them study, model, and optimize large-scale cloud systems. By sharing sanitized, production-scale traces, it allows the community to analyze how cloud resources, serverless functions, AI models, and coding agents behave in the wild.

How it works

The repository hosts various datasets collected from Azure infrastructure over several years. These datasets are provided as downloadable files via GitHub Releases. Each dataset corresponds to specific research papers and includes documentation detailing the collection period, the type of data (such as VM utilization, function invocations, or LLM token usage), and the relevant academic context.

Who it’s for

This project is designed for researchers, students, and system engineers who need authentic, large-scale data to test cloud resource management algorithms, evaluate serverless performance, study AI inference patterns, or analyze agentic coding behavior.

Highlights

  • Includes historical VM workload traces from 2017 and 2019.
  • Provides serverless data covering Azure Functions invocations and blob access patterns.
  • Features modern AI datasets, including LLM and multimodal inference traces.
  • Contains specialized data like VM performance noise benchmarks and GitHub Copilot agentic coding sessions.
  • Offers Jupyter notebooks to help users verify and understand the characteristics of the VM datasets.

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