Linearized Data Center Workload and Cooling Management

With the current high levels of energy consumption of data centers, reducing power consumption by even a small percentage is beneficial. We propose a framework for thermal-aware workload distribution in a data center to reduce cooling power consumption. The framework includes linearization of the general optimization problem and proposing a heuristic to approximate the solution for the resulting Mixed Integer Linear Programming (MILP) problems. We first define a general nonlinear power optimization problem including several cooling parameters, heat recirculation effects, and constraints on server temperatures. We propose to study a linearized version of the problem, which is easier to analyze. As an energy saving scenario and as a proof of concept for our approach, we also consider the possibility that the red-line temperature for idle servers is higher than that for busy servers. For the resulting MILP problem, we propose a heuristic for intelligent rounding of the fractional solution. Through numerical simulations, we compare our heuristics with several existing algorithms. In addition, we evaluate the performance of the solution of the linearized system on the original system. Finally, the results show that the proposed approach can reduce the cooling power consumption by more than 10 percent compared to the case of continuous utilizations and a single red-line temperature. Note to Practitioners—We present a holistic approach for thermal-aware workload distribution for power consumption reduction in data centers. We suggest that when thermal and power consumption models can be linearized, a model-independent approach can be used for optimization purposes. The standard linear problem that results presents some technical challenges to solve, for which we present intuitive and effective solution heuristics. The heuristics are simple enough that they could be used for real-time calculations. The result is that customized models and problems can be avoided (a linear model could be directly constructed from operational data, if available), allowing for the simplification of operational control problems. Our approach is evaluated for a high-fidelity model of a real data center, where both the linearization and optimization components are validated. Finally, we show how this approach can be used to effectively solve the operational problem of workload distribution in the presence of utilization-dependent server red-line temperatures.