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Mampage, A., Karunasekera, S. and Buyya, R. (2023) Deep Reinforcement Learning for Application Scheduling in Resource-Constrained, Multi-Tenant Serverless Computing Environments. Future Generation Computer Systems, 143, 277-292.
https://doi.org/10.1016/j.future.2023.02.006

has been cited by the following article:

• TITLE: Reinforcement Learning for Dynamic and Predictive CPU Resource Management in Cloud Computing

  AUTHORS: Yihan Wang, Suchuan Xing

  KEYWORDS: Reinforcement Learning, CPU Resource Management, Adaptive Systems, Cloud Computing, Resource Allocation, Machine Learning, Dynamic Resource Management, AI, Automation, Virtualization

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.13 No.3, July 28, 2025

  ABSTRACT: As cloud computing continues to evolve, managing CPU resources effectively has become a critical task for ensuring system performance and efficiency. Traditional CPU resource management methods, such as static allocation and manual optimization, are increasingly inadequate in handling dynamic, fluctuating workloads characteristic of modern cloud environments. This paper explores the use of Reinforcement Learning (RL) for adaptive CPU resource management, offering a dynamic, data-driven approach to optimizing resource allocation in real-time. Reinforcement learning, particularly Q-learning and Deep Q Networks (DQNs), enables cloud systems to autonomously adjust CPU resources based on workload demands, improving system efficiency and minimizing resource wastage. This paper discusses the key principles of reinforcement learning, its applications in CPU resource management, the benefits of its implementation, and the challenges that need to be addressed for broader adoption. Finally, the paper highlights future directions for integrating RL with other machine learning techniques and its potential impact on cloud infrastructure optimization.