Tag Archives: resource optimization

Yet More Google Compute Cluster Trace Data



Google’s Borg cluster management system supports our computational fleet, and underpins almost every Google service. For example, the machines that host the Google Doc used for drafting this post are managed by Borg, as are those that run Google’s cloud computing products. That makes the Borg system, as well as its workload, of great interest to researchers and practitioners.

Eight years ago Google published a 29-day cluster trace — a record of every job submission, scheduling decision, and resource usage data for all the jobs in a Google Borg compute cluster, from May 2011. That trace has enabled a wide range of research on advancing the state of the art for cluster schedulers and cloud computing, and has been used to generate hundreds of analyses and studies. But in the years since the 2011 trace was made available, machines and software have evolved, workloads have changed, and the importance of workload variance has become even clearer.

To help researchers explore these changes themselves, we have released a new trace dataset for the month of May 2019 covering eight Google compute clusters. This new dataset is both larger and more extensive than the 2011 one, and now includes:
  • CPU usage information histograms for each 5 minute period, not just a point sample;
  • information about alloc sets (shared resource reservations used by jobs);
  • job-parent information for master/worker relationships such as MapReduce jobs.
Just like the last trace, the new one focuses on resource requests and usage, and contains no information about end users, their data, or patterns of access to storage systems and other services.

At this time, we are making the trace data available via Google BigQuery so that sophisticated analyses can be performed without requiring local resources. This site provides access instructions and a detailed description of what the traces contain.

A first analysis of differences between the 2011 and 2019 traces appears in this paper.

We hope this data will facilitate even more research into cluster management. Do let us know if you find it useful, publish papers that use it, develop tools that analyze it, or have suggestions for how to improve it.

Acknowledgements
I’d especially like to thank our intern Muhammad Tirmazi, and my colleagues Nan Deng, Md Ehtesam Haque, Zhijing Gene Qin, Steve Hand and Visiting Researcher Adam Barker for doing the heavy lifting of preparing the new trace set.

Source: Google AI Blog