1. Data Center Power Analytics
Ayse Coskun
Boston University, ECE Department
Contributors:
Ata Turk, Ozan Tuncer, Trishita Tiwari,
Hao Chen, Marina Zapater, Emre Ates, and
other contributors in the MOC Monitoring Team

2. MOC Monitoring Infrastructure

3. Real-time analysis of power data
The data from the physical sensors is stored in a Redis database in an AWS server.
Use Sensu to execute python scripts that will pull data from Redis every minute.
These scripts will also push the data to InfluxDB (the monitoring database).
Now use the data in InfluxDB to calculate per hour totals of power consumption.
REDIS  SENSU  INFLUXDB

4. Results
Grafana enables using data in InfluxDB to create graphs that visualize the data in real time.
E.g. Environmental Weather station temperatures

5. Thermal and power characterization
On-line data monitoring of MGHPCC
Power consumption for chillers, tower, pumps, in-row coolers, IT
Water flow, inlet temperature and other cooling parameters
Off-line thermal and power characterization of servers
CPU, Memory, Fans, Disks, ...

6. Thermal and power optimization
Reducing power consumption of the MIT Engage1 POD
Reducing overall power of MGHPCC by joint workload and cooling management
Current results:
Energy consumption reduction by 4%, saving $60,000/year
On-going work:
Reaching 8% energy reduction, assess impact on CO2 emissions

7. Regulation Service Reserve Provisioning
Power supplier (ISO) provides credit to participants who modulate power consumption dynamically (e.g., regulation service reserves)

8. Diagnosis of Performance Anomalies
Anomalies waste compute resources
Premature job termination
Reduced efficiency due to contention
Analyze monitored data via machine learning methods to diagnose anomalies

9. Monitoring the MOC