Unverified Commit 31339285 authored by haiyangding's avatar haiyangding Committed by GitHub
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Merge pull request #117 from kmmelcher/master

Fix README typos
parents 357481b1 42ce2df5
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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:
So far, four 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](./cluster-trace-v2017/trace_201708.md)). Download link is available after a short survey ([survey link](https://goo.gl/forms/eOoe6DwZQpd2H5n53)).
* *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](./cluster-trace-v2018/trace_2018.md)). Download link is available after a survey (less than a minute, [survey link](http://alibabadeveloper.mikecrm.com/BdJtacN)).
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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.
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 (Quality 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](mailto:alibaba-clusterdata@list.alibaba-inc.com) or [Haiyang Ding](mailto:haiyang.dhy@alibaba-inc.com) (Haiyang maintains this cluster trace and works for Alibaba's resource management & scheduling group).
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### 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](mailto:alibaba-clusterdata@list.alibaba-inc.com)).
The fundamental 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](mailto:alibaba-clusterdata@list.alibaba-inc.com)).
* microservices trace v2021
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