Approximate dynamic programming using fluid and diffusion approximations with applications to power management
This work addresses a specific computational bottleneck in dynamic programming for power management, representing an incremental advancement in the field.
The paper tackles the problem of selecting a function class for neuro-dynamic programming approximations by proposing an approach using fluid and diffusion approximations, applied to dynamic speed scaling for power management in computer processors, resulting in a method to improve computational efficiency in this context.
Neuro-dynamic programming is a class of powerful techniques for approximating the solution to dynamic programming equations. In their most computationally attractive formulations, these techniques provide the approximate solution only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? The goal of this paper is to propose an approach using the solutions to associated fluid and diffusion approximations. In order to illustrate this approach, the paper focuses on an application to dynamic speed scaling for power management in computer processors.