GITNUX MARKETDATA REPORT 2023

Must-Know Data Center Metrics

Last Updated: October 31, 2023

Written & Summarized by: Jannik Lindner

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Highlights: The Most Important Data Center Metrics

1. Power Usage Effectiveness (PUE)
2. Data Center Infrastructure Efficiency (DCiE)
3. Carbon Usage Effectiveness (CUE)
4. Water Usage Effectiveness (WUE)
5. IT Load
6. Data Center Capacity Utilization
7. Server Utilization
8. Storage Utilization
9. Network Utilization
10. Mean Time Between Failures (MTBF)
11. Mean Time To Repair (MTTR)
12. Data Center Downtime
13. Data Center Availability
14. Incident Response Time
15. First-Time Fix Rate
16. Annualized Failure Rate (AFR)
17. Service Level Agreement (SLA) Compliance
18. Cost per Transaction
19. Energy Cost per Transaction
20. Asset Lifecycle Management

Data Center Metrics: Our Guide

Data centers are paramount in this digital era where nearly all operations are inextricably linked to technology. However, comprehending the efficiency of these data centers goes beyond the physical architecture and rests heavily on vital metrics. This blog post unveils must-know data center metrics that empower businesses to optimize operations, streamline resources and augment performance.

Power Usage Effectiveness

The ratio of total energy consumed by the data center to the energy consumed by IT equipment. A lower PUE indicates better efficiency.

Data Center Infrastructure Efficiency

The inverse of PUE, showing the proportion of energy used by IT equipment relative to total energy consumption. A higher DCiE indicates better efficiency.

Carbon Usage Effectiveness

Measures the carbon emissions associated with the data center’s energy consumption, showing the data center’s environmental impact.

Water Usage Effectiveness

Measures the amount of water used in cooling equipment and other systems in the data center, highlighting the water efficiency of the facility.

IT Load

The total power consumed by IT equipment, including servers, storage, and networking devices.

Data Center Capacity Utilization

The percentage of used computing resources compared to the maximum available resources, indicating how effectively the data center resources are being utilized.

Server Utilization

The average percentage of server resources being used at any given time. Higher server utilization can result in better overall efficiency.

Storage Utilization

The total amount of storage capacity being used as a percentage of the total available storage capacity.

Network Utilization

Measures the percentage of available network bandwidth being used, indicating how effective the data center’s network resources are being utilized.

Mean Time Between Failures

The average time between hardware failures within the data center, indicating the reliability of the infrastructure.

Mean Time To Repair

The average time it takes to fix a failure or issue within the data center, showing the effectiveness of the maintenance and support processes.

Data Center Downtime

The total amount of time that the data center is unavailable due to planned or unplanned outages.

Data Center Availability

The percentage of time that the data center is operational and available for use.

Incident Response Time

The average time taken to respond to issues or incidents within the data center.

First-Time Fix Rate

The percentage of incidents resolved without needing further assistance or support tickets.

Frequently Asked Questions

What are the primary metrics used to measure data center performance?

The primary data center metrics include Power Usage Effectiveness (PUE), Data Center Infrastructure Efficiency (DCiE), Data Center Energy Productivity (DCeP), Data Center Performance Per Energy (DPPE), and Carbon Usage Effectiveness (CUE).

What is Power Usage Effectiveness (PUE), and why is it important?

Power Usage Effectiveness (PUE) is a metric that measures the energy efficiency of a data center. It is calculated by dividing the total data center energy consumption by the IT equipment’s energy consumption. A lower PUE indicates higher efficiency, as it means that more power is being used to run the IT equipment rather than support infrastructure.

Can you explain Data Center Infrastructure Efficiency (DCiE)?

Data Center Infrastructure Efficiency (DCiE) is a metric that demonstrates the efficiency of a data center’s supporting infrastructure. It is calculated by dividing the IT equipment’s energy consumption by the total data center energy consumption and then multiplying by 100 to obtain a percentage. A higher percentage is desirable, as it indicates more efficient use of the power consumed by the infrastructure.

How does Data Center Energy Productivity (DCeP) help assess data center efficiency?

Data Center Energy Productivity (DCeP) is an important metric that measures the amount of useful work, such as computations or data transactions, performed by a data center per unit of energy consumed. DCeP typically uses metrics like Work Powered by Energy (WPE), which measures the number of tasks completed per kWh of energy consumed. A higher DCeP score indicates a more efficient data center, as it means that more useful work is being completed with the same energy consumption.

What is Carbon Usage Effectiveness (CUE), and how does it relate to data center sustainability?

Carbon Usage Effectiveness (CUE) is a metric that measures the carbon emissions associated with a data center’s energy consumption. It is calculated by dividing the total carbon dioxide emissions by the IT equipment’s energy consumption. A lower CUE score indicates a more environmentally sustainable data center, as it signifies a reduced carbon footprint for the given energy usage.

How we write these articles

We have not conducted any studies ourselves. Our article provides a summary of all the statistics and studies available at the time of writing. We are solely presenting a summary, not expressing our own opinion. We have collected all statistics within our internal database. In some cases, we use Artificial Intelligence for formulating the statistics. The articles are updated regularly. See our Editorial Guidelines.

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