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

MOC Monitoring Infrastructure

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

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

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, ...

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

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

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

Monitoring the MOC https://info.massopencloud.org/blog/monitoring-massachusetts-open-cloud-mocmon/