Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization

TL;DR

This paper introduces a novel automatic task scheduling scheme for large-scale cloud computing systems that combines deep learning for system monitoring with reinforcement learning for dynamic scheduling optimization, improving resource utilization and efficiency.

Contribution

It presents an integrated approach using deep learning and reinforcement learning to enhance real-time, automated task scheduling in large-scale cloud environments, which is a novel application.

Findings

The proposed scheme effectively predicts system parameters in real time.

Dynamic scheduling improves resource utilization and task efficiency.

Experimental results demonstrate superior performance over traditional methods.

Abstract

With the continuous expansion of the scale of cloud computing applications, artificial intelligence technologies such as Deep Learning and Reinforcement Learning have gradually become the key tools to solve the automated task scheduling of large-scale cloud computing systems. Aiming at the complexity and real-time requirement of task scheduling in large-scale cloud computing system, this paper proposes an automatic task scheduling scheme based on deep learning and reinforcement learning. Firstly, the deep learning technology is used to monitor and predict the parameters in the cloud computing system in real time to obtain the system status information. Then, combined with reinforcement learning algorithm, the task scheduling strategy is dynamically adjusted according to the real-time system state and task characteristics to achieve the optimal utilization of system resources and the maximum of task execution efficiency. This paper verifies the effectiveness and performance advantages of the proposed scheme in experiments, and proves the potential and application prospect of deep learning and reinforcement learning in automatic task scheduling in large-scale cloud computing systems.