Drainakis, G., Pantazopoulos, P., Katsaros, K.V., Sourlas, V., Amditis, A. and Kaklamani, D.I. (2023) From Centralized to Federated Learning: Exploring Performance and End-to-End Resource Consumption. Computer Networks, 225, Article ID: 109657.
https://doi.org/10.1016/j.comnet.2023.109657

has been cited by the following article:

• TITLE: AI-Driven CPU Resource Management in Cloud Operating Systems

  AUTHORS: Yihan Wang, Suchuan Xing

  KEYWORDS: CPU Resource Management, Cloud Computing, Machine Learning, Cloud Operating Systems, Resource Allocation

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.6, June 24, 2025

  ABSTRACT: As cloud computing continues to grow, efficient resource management remains a critical aspect of maintaining high-performance and cost-effective cloud infrastructures. CPU resource management in cloud operating systems is a key component that influences the scalability, efficiency, and responsiveness of cloud services. Traditional methods of resource allocation often struggle to adapt to the dynamic nature of cloud environments, where workloads can fluctuate in real time. This paper explores the integration of Artificial Intelligence (AI) in optimizing CPU resource management in cloud systems. By leveraging AI techniques such as machine learning and reinforcement learning, cloud operating systems can automate resource allocation, predict demand, and optimize system performance. The paper discusses the role of AI in enhancing resource efficiency, scalability, and fault tolerance, while also addressing the challenges of implementing AI-driven solutions. The potential of AI to transform the way cloud operating systems manage CPU resources offers exciting possibilities for future developments in cloud technology.