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    README.md

    Alibaba Cluster Trace Program

    Overview

    The Alibaba Cluster Trace Program is published by Alibaba Group. By providing cluster trace from real production, the program helps the researchers, students and people who are interested in the field to get better understanding of the characterastics of modern internet data centers (IDC's) and the workloads.

    So far, three versions of traces have been released:

    • cluster-trace-v2017 includes about 1300 machines in a period of 12 hours. The trace-v2017 firstly introduces the collocation of online services (aka long running applications) and batch workloads. To see more about this trace, see related documents (trace_2017). Download link is available after a short survey (survey link).
    • cluster-trace-v2018 includes about 4000 machines in a period of 8 days. Besides having larger scaler than trace-v2017, this piece trace also contains the DAG information of our production batch workloads. See related documents for more details (trace_2018). Download link is available after a survey (less than a minute, survey link).
    • cluster-trace-gpu-v2020 includes about 6500 machines in a period of 2 months. It describe the AI/ML workloads in the MLaaS (Machine-Learning-as-a-Service) provided by the Alibaba PAI (Platform for Artificial Intelligence) on GPU clusters. See subdirectory for the released data, data schema, and data processing script and Jupyter notebook (pai_gpu_trace_2020).

    We will release the cluster-trace-v2021 related to microservices in the following months. These traces contain the details of all complete invocation processes, including call dependencies between microservices, respond time, interfaces, and communication paradigms.

    We encourage anyone to use the traces for study or research purposes, and if you had any question when using the trace, please contact us via email: alibaba-clusterdata, or file an issue on Github. Filing an issue is recommanded as the discussion would help all the community. Note that the more clearly you ask the question, the more likely you would get a clear answer.

    It would be much appreciated if you could tell us once any publication using our trace is available, as we are maintaining a list of related publicatioins for more researchers to better communicate with each other.

    In future, we will try to release new traces at a regular pace, please stay tuned.

    Our motivation

    As said at the beginning, our motivation on publishing the data is to help people in related field get a better understanding of modern data centers and provide production data for researchers to varify their ideas. You may use trace however you want as long as it is for reseach or study purpose.

    From our perspective, the data is provided to address the challenges Alibaba face in IDC's where online services and batch jobs are collocated. We distill the challenges as the following topics:

    1. Workload characterizations. How to characterize Alibaba workloads in a way that we can simulate various production workload in a representative way for scheduling and resource management strategy studies.
    2. New algorithms to assign workload to machines. How to assign and reschedule workloads to machines for better resource utilization and ensuring the performance SLA for different applications (e.g. by reducing resource contention and defining proper proirities).
    3. Collaboration between online service scheduler (Sigma) and batch jobs scheduler (Fuxi). How to adjust resource allocation between online service and batch jobs to improve throughput of batch jobs while maintain acceptable QoS (Quolity of Service) and fast failure recovery for online service. As the scale of collocation (workloads managed by different schedulers) keeps growing, the design of collaboration mechanism is becoming more and more critical.

    Last but not least, we are always open to work together with researchers to improve the efficiency of our clusters, and there are positions open for research interns. If you had any idea in your mind, please contact us via aliababa-clusterdata or Haiyang Ding (Haiyang maintains this cluster trace and works for Alibaba's resource management & scheduling group).

    Outcomes from the trace

    Papers using Alibaba cluster trace

    The fundemental idea of our releasing cluster data is to enable researchers & practitioners doing resaerch, simulation with more realistic data and thus making the result closer to industry adoption. It is a huge encouragement to us to see more works using our data. Here is a list of existing works using Alibaba cluster data. If your paper uses our trace, it would be great if you let us know by sending us email (aliababa-clusterdata).

    Tech reports and projects on analysing the trace

    So far this session is empty. In future, we are going to link some reports and open source repo on how to anaylsis the trace here, with the permission of the owner.

    The purpose of this is to help more beginners to get start on learning either basic data analysis or how to inspect cluster from statistics perspective.

    项目简介

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/alibaba/clusterdata

    发行版本

    当前项目没有发行版本

    贡献者 9

    开发语言

    • Jupyter Notebook 99.0 %
    • Python 1.0 %