Reinforcement learning-based system and method for adaptively adjusting computing capacity

Abstract

A reinforcement learning-based system for adaptively adjusting computing capacity is provided. The system includes an environment module and an agent module. The environment module is configured to collect environment information, including the actual power consumption and one or more power-related metrics, from an application environment. The environment module is further configured to determine a reward value based on the actual power consumption and the expected power consumption, and determine state data based on the one or more power-related metrics. The agent module is configured to receive the reward value and the state data from the environment module, and determine an adjustment action based on the reward value and the state data. The adjustment action involves adjusting the computing capacity and is dynamically executed by the application environment.

Dependencies are inferred from the claim text.