Distributed network management systems in cloud computing environments

A Deep Reinforcement Learning Framework for Scalable Cloud Service Orchestration

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.

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.

Optimizing Cloud-Based Distributed Systems for Real-Time Machine Learning Model Deployment and Scaling

The rapid evolution of machine learning (ML) models and the surge in data volumes necessitate scalable and efficient deployment strategies. Cloud-based distributed systems offer on-demand scalability and resource flexibility, making them ideal for real-time ML model deployment and scaling. This paper explores optimization techniques for cloud-based distributed systems to enhance the deployment and scaling of ML models in real-time applications. We examine the integration of distributed systems and ML within cloud environments, focusing on scalable training and inference mechanisms. Key considerations such as task partitioning, communication overhead, fault tolerance, and resource optimization are discussed. Furthermore, we review auto-scaling techniques, highlighting advancements and challenges in dynamically adjusting resources to meet fluctuating demands. The paper also delves into the application of machine learning for cloud resource provisioning, emphasizing dynamic allocation based on real-time usage patterns. By synthesizing current research and practices, this study provides insights into effectively leveraging cloud-based distributed systems for real-time ML model deployment and scaling.

Optimizing Next-generation Cloud and Data Center Networks: A Review of Routing, Resource Management, and Emerging Technologies

A structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025 reveals that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations.

A Cooperative MADRL Approach for Joint Computing Offloading and Route Optimization in Cloud-Edge Networks

Future 6G networks will integrate communication and computing capabilities to support intelligent, delay-sensitive services. In heterogeneous cloud-edge environments, however, task offloading and routing decisions are strongly coupled, and dynamic workloads, limited computing resources, and constrained link capacity make efficient service provisioning challenging. Existing reinforcement learning-based offloading methods can improve decision efficiency, but many focus on simplified or single-domain settings and do not adequately account for backbone topology and bandwidth constraints. To address this problem, this paper studies joint task offloading and routing optimization in multi-domain cloud-edge networks, explicitly modeling network topology and link capacity. We propose a cooperative multi-agent deep reinforcement learning method that coordinates distributed edge agents through centralized training and decentralized execution. Routing optimization feedback is further incorporated to guide constraint-aware policy learning. Simulation results demonstrate that the proposed method reduces end-to-end latency, mitigates network congestion, and avoids link and node overload in cloud-edge networks.