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

arXiv: https://arxiv.org/abs/2403.07905 · HTML: https://mlsanity.com/paper/2403.07905/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms](https://mlsanity.com/paper/2312.12872/) (2023)
- [Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method](https://mlsanity.com/paper/2401.06782/) (2024)

## Built on by (most cited first, 3 of 3)

- [Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency](https://mlsanity.com/paper/2509.17695/) (2025)
- [The intelligent prediction and assessment of financial information risk in the cloud computing model](https://mlsanity.com/paper/2404.09322/) (2024)
- [Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Scheduling](https://mlsanity.com/paper/2603.12031/) (2026)

## Abstract (arXiv)

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.