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DiG: enabling out-of-band scalable high-resolution monitoring for data-center analytics, automation and control (extended)
- Source :
- Cluster Computing, 24 (4)
- Publication Year :
- 2021
- Publisher :
- Springer, 2021.
-
Abstract
- 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 20 $$\upmu {\hbox {s}}$$ —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).
- Subjects :
- FOS: Computer and information sciences
Computer Networks and Communications
Computer science
Data centers
HPC
High-resolution monitoring
Edge analytics
Machine learning
Deep neural networks
Real-time computing
02 engineering and technology
Deep neural network
Computer cluster
0202 electrical engineering, electronic engineering, information engineering
Overhead (computing)
Data center
business.industry
Edge analytic
020206 networking & telecommunications
Automation
Computer Science - Distributed, Parallel, and Cluster Computing
Analytics
Scalability
020201 artificial intelligence & image processing
Distributed, Parallel, and Cluster Computing (cs.DC)
business
Software
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Cluster Computing, 24 (4)
- Accession number :
- edsair.doi.dedup.....5b8bb4fe412a4d3e0ec1dc29358ca7fc