Abstract
With the continuous expansion of data centers, the problem of large energy consumption has become increasingly prominent. Distributed data centers can enable power transfer through the distribution of computing tasks among multiple data centers and realize the balance between power consumption and computing delay through the power control of a single data center. Scheduling of computing tasks and power control of data center interact with each other, and their control effects are affected by multiple uncertainties. Therefore, a fast and reliable control method is required for realizing the collaborative optimization of the information and energy layers of the data center. First, a distributed data center collaborative optimization architecture is constructed. Then, the dynamic characteristics of multiple data center computing task allocation and single data center power optimization are analyzed based on the dynamic differential equation, and a unified adjustment model of the coupling optimization problem is constructed. Given the system operating cost and computing delay in constructing the objective function, the optimal control theory is introduced to solve the problem and realize the second-level collaborative optimal control of the information energy of the data center. Simulation results show that the high-frequency control based on the proposed algorithm can better track the fluctuation of renewable energy output and calculation tasks than the minute-level control and effectively improve the economic benefits of the system and the local consumption rate of renewable energy.
