Data Center Optimization Using Performance Metrics

Abstract

Data centers are rapidly growing in size and number and consume an increasing and also significant proportion of energy production. Yet, their mission critical nature means they are constructed and operated at almost any cost. The data center industry is becoming more aware of the need to manage energy alongside managing capacity and availability. A vital element — and one that cannot be ignored — is how well cooling is delivered to IT equipment. Monitoring allows you to understand how well you are currently operating and whether you are within acceptable bounds. Meanwhile, simulation of the airflow and heat transfer allows you to predict future performance and understand current and future cooling issues. The challenge with both approaches is that they provide large volumes of data and interpretation can become a challenging task. Consider two scenarios: i. One configuration results in a lot of ‘hot spots’ in the data center, resulting in equipment permanently operating in the ASHRAE Allowable range for intake air temperature. ii. A second configuration has one ‘hot spot’ where equipment is operating above the ASHRAE Allowable range, but the remainder is within the ASHRAE Recommended range. Which is better? One solution is to use metrics to help understand the performance. However, existing metrics are not always well known and understood. The only metric that is currently in common use is Power Usage Effectiveness (PUE). While PUE is useful as a measure of data center cooling efficiency, it does not address the cool air delivery effectiveness within the IT space. However, high-level cooling delivery metrics such as Rack Cooling Index (RCI), Return Temperature Index (RTI), Supply Heat Index (SHI), and Return Heat Index (RHI) have been recognized for producing key information but are not as widely used as they could be. Other metrics have been developed that give more detailed understanding of delivery performance. Capture Index, or more specifically Cold Aisle Capture Index (CACI) and Hot Aisle Capture Index (HACI) provide a measure of whether cooling systems targeting specific equipment work effectively. Simulation also allows diagnostic performance ‘measurement’ with detailed indices such as Rack and Room Recirculation. Since data center airflow is complex, this paper uses case studies to show how using metrics provides a rapid insight into both performance and what might need to be addressed to improve and optimize performance.