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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

Cloud computing enables on-demand access to scalable virtualized resources. However, efficient task scheduling in cloud computing remains a challenge because of the dynamic and heterogeneous nature of workloads. This paper proposes a hybrid Deep Reinforcement Learning (DRL) framework that combines Deep Q-Network (DQN), Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) to enable adaptive resource scheduling in cloud environments. The proposed model is implemented using PyTorch and evaluated in a CloudSim based simulation environment Experimental results show that the proposed approach achieves a consistent improvement in terms of makespan reduction and VM utilization compared to individual DRL approaches and classical scheduling algorithms. Experiments were reiterated with multiple runs to ensure reliability and statistical measures are reported. Under the evaluated conditions, the proposed approach shows more efficient scheduling performance, but it has higher computational overhead and is only validated in a simulated environment for now. These results suggest that hybrid DRL-based scheduling is a promising approach for adaptive cloud resource management, with potential for further validation in real-world deployments and energy-aware scenarios.

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

Adaptive Edge Resource Management Through Deep Reinforcement Learning Techniques

Edge computing has emerged as a foundational paradigm for intelligent digital infrastructure because it reduces latency, improves bandwidth utilization, and enables real-time analytics close to data sources. Yet modern edge environments remain highly volatile. Resource availability changes continuously. IoT traffic fluctuates unpredictably. Mobile users migrate across heterogeneous networks. Conventional heuristic-based schedulers struggle to maintain stable Quality of Service (QoS) under such conditions. Deep Reinforcement Learning (DRL) offers an adaptive decision-making framework capable of learning dynamic resource allocation strategies directly from complex environments. This paper investigates adaptive edge resource management through DRL-driven optimization models for computation offloading, task scheduling, bandwidth allocation, energy efficiency, and autonomous orchestration in distributed edge ecosystems. The study synthesizes recent advances between 2020 and 2025 across edge intelligence, federated learning, multi-agent reinforcement learning, and AI-driven autonomous networking. A layered DRL-enabled edge orchestration framework is proposed to optimize latency, throughput, energy consumption, and load balancing simultaneously. The research also formulates two research questions focused on scalability and adaptive scheduling under heterogeneous workloads. The proposed methodology integrates Proximal Policy Optimization (PPO), Deep Q-Networks (DQN), Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and federated reinforcement learning within a cloud-edge continuum. Comparative analysis indicates that DRL-based adaptive management substantially improves response latency, energy utilization, and computational efficiency compared with static and rule-based schedulers. The paper identifies unresolved challenges involving reward engineering, explainability, convergence stability, privacy preservation, and large-scale deployment in 6G-enabled edge systems. 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 K Sharma, Priya Natarajan · 0 citations
Conference Jul 2026

Distributed network management systems in cloud computing environments

Deep reinforcement learning is rapidly emerging as a transformative approach for distributed network management in dynamic and heterogeneous cloud environments. This paper presents a novel intelligent framework that embeds advanced DRL agents with hierarchical feature extraction into cloud-based Distributed Network Management Systems, enabling precise and adaptive control over network resources, topologies, cand service isolation. By constructing a high-fidelity simulation environment based on NS-3, the effectiveness of the framework has been fully validated in both threshold driven and static methods. Research shows that DRL-based systems can still maintain throughput stability in the face of topology changes, node failures, tenant traffic fluctuations, or throughput delays. The framework ensures network performance during large-scale failures and rapid expansions thru robustness and scalability analysis. Despite these advantages, computational overhead and policy convergence are issues during large-scale deployment. The research findings indicate that the key to enhancing real-time cloud network intelligence lies in the architecture based on Deep Reinforcement Learning (DRL). These findings provide useful guidelines for future engineering projects to ensure that network management infrastructure possesses autonomy, flexibility, and efficiency.

Shuyao Jia · 0 citations