Efficient Task Scheduling in Cloud using Double Deep QNetwork

Abstract

Cloud computing has transformed data management with its scale and flexibility. Cloud resources are transient and diversified, making task scheduling difficult. This paper proposes Double Deep Q-Network (DDQN) reinforcement learning model to solve the cloud computing task scheduling problem. Double Deep Q-Network (DDQN) is a powerful reinforcement learning system that improves on Deep Q-Networks (DQN). The target network and the online network are the two distinct neural networks that DDQN presents. To create a more consistent and less unpredictable learning process, the target network is updated on a regular basis to imitate the Q-value estimations of the online network. Traditional DQN can have problems with overestimation bias, which is something that this dual-network architecture helps to alleviate. DDQN is a reliable and efficient tool for solving complex reinforcement learning problems. It excels in learning optimal strategies through iteratively improving its Q-value estimations. DDQN presents a robust framework for addressing the challenges inherent in cloud computing task scheduling. Its dual-network architecture and iterative learning process offer a promising avenue for enhancing the efficiency and effectiveness of resource allocation in cloud environments. Through its continuous refinement of Q-value estimations, DDQN emerges as a valuable asset in navigating the complexities of modern data management within cloud infrastructures.