We formulate a modeling and control framework aimed at direct liquid cooling of data servers. In our application scenario, the server's heat load is rejected into a liquid cooling circuit that extends to individual chips. We start with a comprehensive discussion of our modeling derivations. We then show how to dynamically provision the coolant, while 1) regulating the temperatures of any self-heating components within the safe operational envelope; 2) minimizing the coolant supply cost; and 3) increasing the server outflow temperature (a key performance objective toward heat recovery systems). We confirm experimentally the benefits of the proposed controlled cooling strategy over several realistic scenarios corresponding to different inlet coolant temperatures and computational loads.

Given a generic Control Configuration Selection (CCS) protocol and a plant uncertainty description isomorph to a unit ball in a finite-dimensional Lp space, we search for the largest perturbation radius for which the nominal configuration remains the preferred one. To this aim, we develop a randomized search algorithm based on sampling the uncertain plants and characterize its statistical performance. By adopting an intuitive accuracy measure that relates to the volume of points in which the preferred configuration differs from the nominal one, we devise a generally applicable strategy that allows for arbitrarily accurate estimates, in a specific probabilistic sense, depending on the number of uncertain plants that are sampled. We benchmark the proposed algorithm using examples from the literature and in a data center flow provisioning problem. In the latter setting, we identify the uncertainty description with the space of controls and sample the “uncertain” plants from an underlying nonlinear model.

This study considers how to efficiently manage the thermal state of data servers by provisioning their cooling airflow dynamically. It introduces a thermal network framework that is tailored to capture the temperature dynamics of the on-board thermal inertiae. On top of this modeling, it devises LeakageCooling, a novel flow provisioning controller that balances the temperature dependent leakage dissipation within the electronic chips and the power consumed by the local fans to produce the cooling airflow. The prediction capabilities of the proposed modeling and the overall control performance are assessed over several relevant experimental scenarios on an Open Compute Windmill V2 server.

We evaluate the existing trade-off between the exhaust flow rates and temperatures on a air-cooled Open Compute Windmill V2 server platform. To this aim, we implement a novel flow provisioning controller that optimizes the server's exhaust air temperature, and compare its performance against that of the default provisioning strategy. We report both experimental and numerical results that link the attainable flow rates and the exhaust temperatures. We demonstrate thus the benefits of tailored control strategies in a heat recovery context, and provide crucial characterizations toward the investigation of data center heat recovery applications.

This work considers how to provision efficiently the cooling airflow in server rooms. We focus on raised plenum and perforated tiles setups and devise ColdSpot: an optimizing thermal supervisor that adaptively regulates the cooling airflow across the floor plane. ColdSpot operates by pairing 1) model-free estimators of the supply flow requirements at each perforated tile and 2) a model-based cost optimizer that actuates the room's Air Conditioning Units (ACUs) to satisfy the above rate requirements. We propose to use an array of Gaussian Processes (GPs) to capture the nonlinear flow model of each tile and present a hierarchical control structure on top of this modeling. We validate in silico the prediction accuracy of GPs in this context and demonstrate the performance of ColdSpot in regulating the cold-isles temperatures in spite of varying heat and airflow transport disturbances within the room.

We design a Newton-like phasor-based Extremum Seeking Control (ESC) aimed at multi-variable nonlinear control systems. We first augment the derivative estimator underlying the phasor approach to capture the Hessian of the plant index. Then we use these estimates to steer the system along a Newton-like direction. We assess the closed-loop stability of the proposed scheme and demonstrate its effectiveness in a data center cooling scenario.

We study the economic operation of a free-cooling setup in which an Indirect Adiabatic Air Handler (IAAH) recovers heat from an array of server racks placed in a contained aisle. For this setting we propose two different control policies: in the first approach, the airflow supply rate to the racks is maintained constant while only the process- side operations of the IAAH are optimized. In the second approach, also the room-side rate is updated adaptively. Building on calibrated models of the IAAH and the servers, we design experimental trials considering different outdoor temperatures and humidity conditions as well as varying computational workloads. The in silico analysis contributes actionable insights on the optimal thermal and cost operations of the system.

Aiming at improving the energy efficiency of air cooled servers in data centers, we devise a novel control oriented, nonlinear, thermal model of the servers that accounts explicitly for both direct and recirculating convective air flows. Instrumental to the optimal co-design of both geometries and cooling policies, we propose an identification methodology based on Computational Fluid Dynamics (CFD) for a generic thermal network of m fans and n electronic components. The performance of the proposed modelling framework is validated against CFD measurements with promising results. We formalize the minimum cooling cost control problem as a polynomially constrained Receding Horizon Control (RHC) and show, in-silico, that the resulting policy is able to efficiently modulate the cooling resources in spite of the unknown future computational and electrical power loads.