Thermal influence indices: Causality metrics for efficient exploration of data center cooling

Abstract

Cooling is an important issue in data center design and operation. Accurate evaluation of a design or operational parameter choice for cooling is difficult as it requires several runs of computationally intensive Computational Fluid Dynamics (CFD) based models. Therefore there is need for an exploration method that does not incur enormous computation. In addition, the exploration should also provide insights that enable informed decision making. Given these twin goals of reduced computation and improved insights, we present a novel approach to data center cooling exploration. The key idea is to do a local search around the current design/operation of a data center to obtain better design/operation parameters subject to the desired constraints. To do this, all the microscopic information about airflow and temperature in data center available from a single run of CFD computation is converted into macroscopic metrics called influence indices. The influence indices, which characterize the causal relationship between heat sources and sinks, are used to refine the design/operation of the data center either manually or programmatically. New designs are evaluated with further CFD runs to compute new influence indices and the process is repeated to yield improved designs as per the computation budget available. We have carried out design exploration of a realistic data center using this methodology. Specifically, we considered maximization of the heat load in the data center subject to the constraints that: 1) servers are kept at appropriate temperatures and 2) overloading of CRACs is avoided. Our evaluation shows that the use of influence indices cuts down the exploration time by 80 % for a 1500 sq. ft. data center.