{"url":"/dataset/alibaba-cluster-trace","name":"Alibaba Cluster Trace","full_name":null,"description_markdown":"**Alibaba Cluster Trace** captures detailed statistics for the co-located workloads of long-running and batch jobs over a course of 24 hours. The trace consists of three parts: (1) statistics of the studied homogeneous cluster of 1,313 machines, including each machine’s hardware configuration, and the runtime {CPU, Memory, Disk} resource usage for a duration of 12 hours (the 2nd half of the 24-hour period); (2) long-running job workloads, including a trace of all container deployment requests and actions, and a resource usage trace for 12 hours; (3) co-located batch job workloads, including a trace of all batch job requests and actions, and a trace of per-instance resource usage over 24 hours.\r\n\r\nIt also has a second version of traces `cluster-trace-v2018` that includes about 4,000 machines in a period of 8 days. Besides having larger scaler than trace-v2017, this piece trace also contains the DAG information of the production batch workloads.","description_withheld":null,"homepage":"https://github.com/alibaba/clusterdata","introduced_date":"2018-08-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/characterizing-co-located-datacenter","title":"Characterizing Co-located Datacenter Workloads: An Alibaba Case Study","first_author":null,"url":null},"license":{"name":"Unknown","url":null},"modalities":[],"tasks":[{"name":"Time Series Analysis","url":"/task/time-series","datasets_with_task":"/datasets/task/time-series"},{"name":"2D Human Pose Estimation","url":"/task/2d-human-pose-estimation","datasets_with_task":"/datasets/task/2d-human-pose-estimation"}],"languages":[],"variants":["Alibaba Cluster Trace"],"data_loaders":[{"repo":"https://github.com/alibaba/clusterdata","url":"https://github.com/alibaba/clusterdata","frameworks":[]}],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/2d-human-pose-estimation-on-alibaba-cluster","task":"2D Human Pose Estimation","dataset_variant":"Alibaba Cluster Trace","rows":1,"metrics":["10-20% Mask PSNR"],"first_row_in_archive_order":{"model":"mitsimpo","paper":"/paper/alibaba-at-ijcnlp-2017-task-1-embedding","metrics":{"10-20% Mask PSNR":"12"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/alibaba-at-ijcnlp-2017-task-1-embedding","title":"Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task","date":"2017-12-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+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."}