Core Concepts
The author proposes an automatic task scheduling scheme based on deep learning and reinforcement learning to optimize large-scale cloud computing systems efficiently.
Abstract
The content discusses the integration of deep learning and reinforcement learning in Kubernetes automated scheduling for large-scale cloud computing optimization. It emphasizes the importance of efficient task scheduling in cloud computing systems, highlighting the benefits of utilizing AI technologies. The proposed methodology includes gang scheduling, task scheduling, advanced preemption strategies, topology-aware scheduling, and GPU topology considerations. Practical applications involve capacity expansion, optimizing Pod scheduling policies, advanced scheduling features, performance monitoring, disaster recovery planning, testing, and optimization. The conclusion reflects on the effectiveness of the proposed scheme in improving system efficiency and resource utilization while providing insights for future research directions.
Stats
"The Big Data Expert Committee of the China Computer Society pointed out in the 2019 Big Data development trend survey report that artificial intelligence, big data and cloud computing will be highly integrated into an integrated system."
"In this context, this paper aims to deeply analyze the optimization strategy of big data storage and processing in a cloud computing environment."
"Both big data and AI computing are typical examples of distributed computing models."
"As a result, big data provides rich data resources while cloud computing platforms provide high-performance computing resources."
"This combination offers endless possibilities for innovative intelligent applications that help improve user experience."
Quotes
"Kubernetes automated scheduling has many advantages on its own..."
"Through this research...the combination with Kubernetes automated scheduling brings new opportunities..."