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

TL;DR

This paper introduces DiG, a scalable out-of-band monitoring platform for data centers that provides high-resolution, real-time profiling of performance and power consumption, enabling efficient analysis and control.

Contribution

The paper presents a novel out-of-band monitoring system that achieves high-resolution, real-time data collection for data centers, overcoming bandwidth and storage bottlenecks.

Findings

Supports sampling up to 20 microseconds with less than 1% error

Enables node-level and cluster-level analysis in real-time

Successfully deployed in a high-performance compute cluster

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 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).