{"url":"/dataset/archival-bundle-of-the-data-used-for","name":"Archival bundle of the data used for \"Predictive Auto-scaling with OpenStack Monasca\" (UCC 2021)","full_name":null,"description_markdown":"Follow the instructions provided in the [companion repo](https://github.com/giacomolanciano/UCC2021-predictive-auto-scaling-openstack) to automatically download and decompress the archive. The following files are included:\r\n\r\n| File                                                    | Description                                                    |\r\n| :------------------------------------------------------ | :------------------------------------------------------------- |\r\n| `amphora-x64-haproxy.qcow2`                             | Image used to create Octavia amphorae                          |\r\n| `distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>.log`       | `distwalk` run log                                             |\r\n| `distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-pred.json` | Predictive metric data exported from Monasca DB                |\r\n| `distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-real.json` | Actual metric data exported from Monasca DB                    |\r\n| `distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-times.csv` | Client-side response time for each request sent during a run   |\r\n| `model_dumps/*`                                         | Dumps of the models and data scalers used for the validation   |\r\n| `predictor.log`                                         | `monasca-predictor` log                                        |\r\n| `predictor-times.log`                                   | `monasca-predictor` log (timing info only)                     |\r\n| `predictor-times-{lin,mlp,rnn}.{csv,log}`               | `monasca-predictor` log (timing info only, group by predictor) |\r\n| `super_steep_behavior.csv`                              | Dataset used to train MLP and RNN models                       |\r\n| `test_behavior_02_distwalk-6t_last100.dat`              | `distwalk` load trace                                          |\r\n| `ubuntu-20.04-min-distwalk.img`                         | Image used to create Nova instances for the scaling group      |","description_withheld":null,"homepage":"https://doi.org/10.5281/zenodo.5618881","introduced_date":"2021-11-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/predictive-auto-scaling-with-openstack","title":"Predictive Auto-scaling with OpenStack Monasca","first_author":"Giacomo Lanciano","url":null},"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Archival bundle of the data used for \"Predictive Auto-scaling with OpenStack Monasca\" (UCC 2021)"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}