Data Center Cooling Efficiency Improvement Through Localized and Optimized Cooling Resources Delivery

Abstract

Data centers are large computing facilities that can house tens of thousands of computer servers, storage and networking devices. They can consume megawatts of power and, as a result, reject megawatts of heat. For more than a decade, researchers have been investigating methods to improve the efficiency by which these facilities are cooled. One of the key challenges to maintain highly efficient cooling is to provide on demand cooling resources to each server rack, which may vary with time and rack location within the larger data center. In common practice today, chilled water or refrigerant cooled computer room air conditioning (CRAC) units are used to reject the waste heat outside the data center, and they also work together with the fans in the IT equipment to circulate air within the data center for heat transport. In a raised floor data center, the cool air exiting the multiple CRAC units enters the underfloor plenum before it is distributed through the vent tiles in the cold aisles to the IT equipment. The vent tiles usually have fixed openings and are not adapted to accommodate the flow demand that can vary from cold aisle to cold aisle or rack to rack. In this configuration, CRAC units have the extra responsibilities of cooling resources distribution as well as provisioning. The CRAC unit, however, does not have the fine control granularity to adjust air delivery to individual racks since it normally affects a larger thermal zone, which consists of a multiplicity of racks arranged into rows. To better match cool air demand on a per cold aisle or rack basis, floor-mounted adaptive vent tiles (AVT) can be used to replace CRAC units for air delivery adjustment. In this arrangement, each adaptive vent tile can be remotely commanded from fully open to fully close for finer local air flow regulation. The optimal configuration for a multitude of AVTs in a data center, however, can be far from intuitive because of the air flow complexity. To unleash the full potential of the AVTs for improved air flow distribution and hence higher cooling efficiency, we propose a two-step approach that involves both steady-state and dynamic optimization to optimize the cooling resource provisioning and distribution within raised-floor air cooled data centers with rigid or partial containment. We first perform a model-based steady-state optimization to optimize whole data center air flow distribution. Within each cold aisle, all AVTs are configured to a uniform opening setting, although AVT opening may vary from cold aisle to cold aisle. We then use decentralized dynamic controllers to optimize the settings of each CRAC unit such that the IT equipment thermal requirement is satisfied with the least cooling power. This two-step optimization approach simplifies the large scale dynamic control problem, and its effectiveness in cooling efficiency improvement is demonstrated through experiments in a research data center.

Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy

“…The airflow of the inlet air is a mix of recirculated hot air exhausted from other servers and supplied cool air from CRACs. The time-discrete form of rack inlet temperature is as follows [9], [28]:…”

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

“…However, the approach failed to minimize the overall energy consumption of the systems and fans. Zhou et al studied the improvement of cooling efficiency through localized and optimized cooling resources [26]. Zhou et al suggested to use adaptive vent tiles mounted on floor and control cooling provisions to reduce costs [26].…”

“…Zhou et al studied the improvement of cooling efficiency through localized and optimized cooling resources [26]. Zhou et al suggested to use adaptive vent tiles mounted on floor and control cooling provisions to reduce costs [26]. However, the suggestion did not consider the power consumption of cooling fans.…”

“…Zhou et al [26] Localized and optimized cooling resources. Adaptive vent tiles mounted on floor and control cooling provisions to reduce costs.…”

“…They proposed a regression energy model with support vectors. Because the thermal distribution of was a slow process, the selected parameters in the regression model would be too sensitive for thermal management [26]. Tarutani et al proposed a method to predict the temperatures of sensors and the outlet temperature of an air conditioner by using regression models [33].…”