**3. The use of Lyapunov optimization for deep learning platforms**

As explained, the Lyapunov optimization theory is a scalable, self-configurable, low-complexity algorithm which can be used in many applications. In this section, the use of Lyapunov optimization for deep learning and computer platforms is discussed in two different ways, i.e., departure process control (refer to Section 3.1) and arrival process control (refer to Section 3.2). Finally, its related performance evaluation results are presented (refer to Section 3.3).
