Abstract: A throttling method for a storage device is provided. The throttling method includes: receiving a write command from a host; identifying, using a first machine learning model, a throttling delay time; transmitting a completion message to the host according to the throttling delay time; collecting weights of the first machine learning model and performance information of the storage device corresponding to the weights; learning the weights and the performance information to generate an objective function indicating a relationship between the weights and the performance information using a second machine learning model of a weight learning device; selecting a weight corresponding to a maximum performance using the objective function; and updating the first machine learning model with the weight.

Abstract: A storage controller, including a processor configured to perform a plurality of tasks; and a scheduling module configured to schedule the plurality of tasks through reinforcement learning, and provide a scheduling result to the processor, wherein the scheduling module includes: a resource analysis module configured to analyze a usage history and a usage status of a resource; an access pattern analysis module configured to analyze an access pattern; a queue analysis module configured to analyze information included in a queue; and a performance analysis module configured to analyze a task execution result, wherein the scheduling module is further configured to perform the reinforcement learning using state information and reward information, and wherein the state information and the reward information are determined based on the performing of the plurality of tasks.