DiG: Enabling Out-of-Band Scalable High-Resolution Monitoring for Data-Center Analytics, Automation and Control

Open access
Date
2018Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Data centers are increasing in size and complexity, and we need scalable approaches to support their automated analysis and control. Performance, power consumption and reliability are their key "vital signs". State-of-the-Art 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) and analytics, both on 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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000306925Publication status
publishedExternal links
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Publisher
Data-center Automation, Analytics, and Control (DAAC)Event
Subject
Data Centers (DC); HPC; High-Resolution Monitoring; Edge Analytics; Machine Learning; Deep neural networks (DNNs); Deep neural networksOrganisational unit
03996 - Benini, Luca / Benini, Luca
Related publications and datasets
Is part of: https://daac-general.nsfcac.org/daac-2018
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ETH Bibliography
yes
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