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Open access Jun 2026

DEEP REINFORCEMENT LEARNING-BASED INTELLIGENT TASK SCHEDULING FRAMEWORK FOR CLOUD DISTRIBUTED SYSTEMS

This paper proposes an innovative Deep Reinforcement Learning-based Intelligent Task Scheduling Framework (DRITS) designed to optimize task allocation and resource utilization in cloud distributed systems and establishes DRL-based intelligent scheduling as a promising solution for next-generation cloud computing infrastructure management.

Tileemat Ashour Aletiri · 0 citations
Open access Aug 2026

A Hybrid Deep Reinforcement Learning Framework for Efficient Cloud Resource Scheduling

A hybrid Deep Reinforcement Learning (DRL) framework that combines Deep Q-Network, Proximal Policy Optimization and Advantage Actor-Critic to enable adaptive resource scheduling in cloud environments is proposed.

P. Priya, J. Geetha, E. Naresh et al. · 0 citations
Open access Aug 2026

Adaptive Edge Resource Management Through Deep Reinforcement Learning Techniques

The findings demonstrate that DRL-driven adaptive orchestration can become a central mechanism for autonomous edge intelligence in next-generation AI-native communication infrastructures.

Amit K. Mogal, Rahul A. Patil, Sahebrao N. Shinde et al. · 0 citations
Open access 2021

Reinforcement Learning for Adaptive Resource Management in Cloud Software

Reinforcement learning-based adaptive resource management framework is proposed that enables cloud systems to autonomously learn optimal resource allocation policies through continuous interaction with the environment and significantly outperforms static and reactive baseline strategies in terms of resource utilization efficiency and response time stability.

Rajesh Sharma, Priya Natarajan · 0 citations
Conference Jul 2026

Distributed network management systems in cloud computing environments

The research findings indicate that the key to enhancing real-time cloud network intelligence lies in the architecture based on Deep Reinforcement Learning (DRL), and provide useful guidelines for future engineering projects to ensure that network management infrastructure possesses autonomy, flexibility, and efficiency.

Shuyao Jia · 0 citations