Computational Research Facility (CRF) Data Efficiency Analysis
Heather Anderson
Mentor: Charles Liles
Summer 2023 Internship
Langley Research Center (LaRC)
Agenda
Introduction
What is the Challenge?
About LaRC CRF
Project Background
About Dataset
Data Construction
Equipment Terms
About Looker Studio
Data Visualization
Dashboard Benefits
Future Work
Conclusion
Heather Anderson�Introduction
What is the Challenge ?
Previous lack of data collection and visualization for the Computational Research Facility
Over-complicated power usage efficiency reports that are difficult to analyze over time
Inability to analyze trends in the data in order to improve facility efficiency
Focus of the project:
Analyze, engineer, and visualize the given data into a more usable format
About the �Langley Computational Research Facility
Project Background
Initialized during Spring 2022 to collect data to calculate CRF power usage efficiency (PUE)
3-month process to tag equipment for recording purposes, in order to collect a full year of data to detect changes in PUE
After this data was collected the next step was to find a platform to host the data, PowerBI was not an option because it couldn’t set up automatic data gateways, Tableau was also not an option with the timeline. In the end Looker Studio was selected as it automatically connects to BigQuery and requires low mantinence
Data Pipeline
BAS train ensemble
Aveva PI Data Historian
Python Processing
Big Query
Looker Studio
About the Dataset
Previous Reports
Previous Reports
Manual Reports were both error prone and time consuming
Data Construction
Original Dataset
Reconstructed Data
About Power Usage Effectiveness (PUE)
Power usage effectiveness (PUE) is a metric used to determine the energy efficiency of a data center. PUE is determined by dividing the total amount of power entering a data center by the power used to run the IT equipment within it. PUE is expressed as a ratio, with overall efficiency improving as the quotient decreases toward 1.0 (1.10-1.40 is preferable)
Data center infrastructure and the processing power within it require a lot of energy, and data centers that do not operate efficiently will use more energy. Monitoring a metric like PUE is useful for benchmarking data center efficiency. Organizations and data center managers can use this metric once to measure their data center efficiency and then again to measure the effect of any changes made to the data center. This helps reduce power consumption and energy costs
Calculated by Total Facility Power/IT Equipment Energy = PUE
How to Lower PUE
Virtualize servers
Virtual machines can run their own workloads, which reduces energy consumption and frees up more floor space.
Improve cooling systems
To prevent overheating, data centers require a cooling system. However, refrigerant-based cooling systems use a lot of power. Improving these systems or reducing the data center's reliance on them can help lower PUE.
Optimize cool air production
This can be done, for example, by using naturally cool outside air or heat exchangers.
Replace inefficient hardware
The quality and performance of some hardware may degrade over time, so if servers or storage systems are not performing properly, they should be replaced.
Use an energy-efficient uninterruptible power supply (UPS)
Power distribution should be designed with a UPS to be more efficient. More efficient equipment and making power run a shorter distance increase efficiency.
Use energy-efficient lighting
Although lighting generally makes up a smaller portion of power consumption, it is still an easy way to reduce power and heat production. Replacing fluorescent lighting with LEDs on motion sensors and lighting controls can help reduce power consumption and heat production.
Facility Equipment Terms
RPP’s – Remote Power Panels
AHU – Air Handling Unit
Power Meters
IT Load
Facility Load
Facility Equipment Terms
About
Connect to Data From:
Chart Types and Filters
Additional Development Tools
1
2
3
Data Aggregation
Aggregation is the process of reducing and summarizing tabular data
Aggregation in Looker Studio
There are several ways to apply an aggregation method to your data in Looker Studio:
Visualizing Main 3 Metrics�Page 1
AHU & Real Power�Page 1
Scorecards for Main Dashboard�Page 1
Main Dashboard�Page 1
Facility Load vs IT Load�Page 2
Facility Load vs IT Load�Page 2
Facility Load and Power Meters�Page 3
Facility Load and Power Meters�Page 3
Facility Load & Power Meters�Page 3
IT Load and Power Meters�Page 4
Facility Load & Power Meters�Page 4
Cooling Tower Fan & �Chiller Plant Cooling�Page 5
Condenser & Chilled Water Pumps�Page 5
Chillers, Pumps, & Fans�Page 5
RPP’s�Page 6
RPP’s & IT Load�Page 6
RPP’s�Page 7
RPP’s & IT Load�Page 7
Power Meters�Page 8
Power Meters�Page 8
Generating Reports
Example: Monthly Report for July 2023 in an Adobe PDF format
Report View
Example: Monthly Report for July 2023 in an Adobe PDF format
Alternate Light Theme
This copy was generated for those who prefer a lighter theme for potentially easier data visibility
Benefits of PUE Dashboards
Fully automated system, replacing previous time intensive manual reports
Low cost to run data (~4 cents throughout internship)
Minimal dashboard maintenance needed; all versions saved to the cloud
Increased ability to analyze data trends over time, and compare metrics over larger periods
Future Work
Utilize this data to improve future power usage efficiency for the data center
Utilize
Potentially integrate this data into the GIS system to make it more accessible
Integrate
Continue to generate reports and edit dashboards according to need
Continue
Conclusion
Any Questions?�