DiG：为数据中心分析、自动化和控制实现带外可扩展高分辨率监控（扩展版）
DiG: Enabling Out-of-Band Scalable High-Resolution Monitoring for Data-Center Analytics, Automation and Control (Extended)
摘要：
数据中心的规模和复杂性不断增加，我们需要可扩展的方法来支持其自动化分析和控制。性能计数器和功耗是它们的关键“生命体征”。最先进的（SoA）监控系统提供内置工具来收集性能测量结果，并提供定制解决方案来了解其功耗。然而，随着测量分辨率（在时间和空间上）的增加以及随之而来的要处理的大量测量数据，出现了新的挑战，例如网络带宽上的瓶颈、监控单元上的存储和软件开销。为了应对这些挑战，我们提出了一种用于数据中心的新型监控平台，该平台能够实现实时高分辨率分析（即，所有可用的性能计数器和插头处功耗的整个信号带宽-采样高达20 μ s-误差低于1%），以及边缘处（节点级分析）和集中单元上（集群级分析）的分析。监控基础设施完全是带外的、可扩展的、技术无关的和低成本的，并且它已经安装在SoA高性能计算集群中（即，D.A.V.I.D.E. - 2017年11月在Green 500上排名第18位）。
英文摘要：
Data centers are increasing in size and complexity, and we need scalable approaches to support their automated analysis and control. Performance counters and power consumption are their key "vital signs". State-of-the-Art (SoA) monitoring systems provide built-in tools to collect performance measurements, and custom solutions to get insight on their power consumption. However, with the increase in measurement resolution (in time and space) and the ensuing huge amount of measurement data to handle, new challenges arise, such as bottlenecks on the network bandwidth, storage and software overhead on the monitoring units. To face these challenges we propose a novel monitoring platform for data centers, which enables real-time high-resolution profiling (i.e., all available performance counters and the entire signal bandwidth of the power consumption at the plug - sampling up to 20us - with an error below 1%) and analytics, both at the edge (node-level analysis) and on a centralized unit (cluster-level analysis). The monitoring infrastructure is completely out-of-band, scalable, technology agnostic and low cost, and it is already installed in a SoA high-performance compute cluster (i.e., D.A.V.I.D.E. - 18th in Green500 November 2017).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
