Enhancing Kubernetes Automated Scheduling with Deep Learning and
  Reinforcement Techniques for Large-Scale Cloud Computing Optimization
Zheng Xu Yulu Gong Yanlin Zhou Qiaozhi Bao Wenpin Qian
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.