Characterizing Job Microarchitectural Profiles at Scale: Dataset and Analysis

Kangjin Wang, Ying Li, Cheng Wang, Tong Jia, K. Chow, Yang Wen, Yaoyong Dou, Guoyao Xu, Chuanjia Hou, Jie Yao, Liping Zhang
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引用次数: 2

Abstract

Understanding the microarchitectural resource characteristics of datacenter jobs has become increasingly critical to guarantee the performance of jobs while improving resource utilization. Prior work studied the resource characteristics of datacenter jobs at the OS level, little reveals the deep and detailed characteristics at the microarchitecture level due to the lack of related open traces. In this paper, we provide a new open trace, AMTrace (Alibaba Microarchitecture Trace) 1, which is profiled from 8,577 high-end physical hosts from Alibaba’s datacenter by a hardware/software co-design monitoring method. AMTrace provides the microarchitectural metrics of 9.8 × 105 Linux containers with ”Per-Container-Per-Logic CPU” granularity. Different from existing open traces, AMTrace provides a new perspective to analyze the microarchitectural resource characteristics of datacenter jobs. Based on AMTrace, we first reveal the uneven resource usage of jobs among multiple logic CPUs. Then, we analyze the impact of resource contention of CPU and memory bandwidth on job performance. Finally, we analyze the job performance under different CPU provisioning modes from microarchitecture perspective. These analyses lead to constructive insights for datacenter resource management and optimization. Furthermore, we discuss possible research opportunities on AMTrace and we believe that AMTrace will inspire more exciting research on microarchitecture and resource management.
大规模作业微架构特征描述:数据集与分析
了解数据中心作业的微体系结构资源特征对于保证作业的性能和提高资源利用率变得越来越重要。先前的工作研究了操作系统级别的数据中心作业的资源特征,由于缺乏相关的开放痕迹,很少揭示微体系结构级别的深入和详细特征。在本文中,我们提供了一种新的开放跟踪,AMTrace(阿里巴巴微架构跟踪)1,它通过硬件/软件协同设计监测方法对阿里巴巴数据中心的8,577台高端物理主机进行了分析。AMTrace以“每个容器每个逻辑CPU”的粒度提供了9.8 × 105个Linux容器的微架构度量。与现有的开放跟踪不同,AMTrace提供了一个新的视角来分析数据中心作业的微架构资源特征。基于AMTrace,我们首先揭示了多个逻辑cpu之间作业资源使用的不均匀性。然后,我们分析了CPU资源争用和内存带宽对作业性能的影响。最后,从微体系结构的角度分析了不同CPU配置模式下的作业性能。这些分析为数据中心资源管理和优化提供了建设性的见解。此外,我们讨论了AMTrace可能的研究机会,我们相信AMTrace将激发更多令人兴奋的微架构和资源管理研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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