Characterizing Co-Located Workloads in Alibaba Cloud Datacenters
Characterizing Co-Located Workloads in Alibaba Cloud Datacenters
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DOI：
10.1109/tcc.2020.3034500
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发表时间：
2020-10
影响因子：
6.5
通讯作者：
Congfeng Jiang;Yitao Qiu;Weisong Shi;Zhefeng Ge;Jiwei Wang;Shenglei Chen;C. Cérin;Zujie Ren;Guoyao Xu;Jiangbin Lin
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文献类型：
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作者：
Congfeng Jiang;Yitao Qiu;Weisong Shi;Zhefeng Ge;Jiwei Wang;Shenglei Chen;C. Cérin;Zujie Ren;Guoyao Xu;Jiangbin Lin
协同特性对于数据中心运营和协同数据中心中的作业调度都至关重要，在协同数据中心中，在线服务和批处理作业部署在同一生产集群上。在本文中，对阿里巴巴的cluster-trace-v2018进行了全面分析，该分析针对4034台机器的生产集群。研究结果表明：（1）生产集群的CPU和磁盘I/O利用率每天都有周期性波动，内存系统已经成为协同集群的性能瓶颈。(2)批处理作业包括其任务和派生实例可以近似为Zipf分布。然而，对于所有具有有向无环图依赖的批处理作业，由于在线服务具有高度优先级，因此它们与在线服务处于同一位置。(3)容器的资源使用具有与整个集群一致的类似的周期性波动，而它们的内存使用保持近似恒定。(4)与在线服务协同定位的批处理作业的数量取决于在线服务的每公斤指令的误预测。为了保证在线服务的服务质量，当在线服务的MPKI增加时，在同一台机器上协同定位的批作业的数量应该减少。
Workload characteristics are vital for both data center operation and job scheduling in co-located data centers, where online services and batch jobs are deployed on the same production cluster. In this article, a comprehensive analysis is conducted on Alibaba's cluster-trace-v2018 of a production cluster of 4034 machines. The findings and insights are the following: (1) The workload on the production cluster poses a daily cyclical fluctuation, in terms of CPU and disk I/O utilization, and the memory system has become the performance bottleneck of a co-located cluster. (2) Batch jobs including their tasks and derived instances can be approximated as Zipf distribution. However, for all batch jobs with directed acyclic graph dependency, they suffer from co-location with online services since the online services are highly prioritized. (3) The resource usages of containers have similar cyclical fluctuation consistent with the whole cluster, while their memory usages remain approximately constant. (4) The number of batch jobs co-located with online services is dependent on the mispredictions per kilo instructions of online services. In order to guarantee the QoS of online services, when the MPKI of online services rises, the number of batch jobs to be co-located on the same machine should decrease.