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

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.

AI-Based Dynamic Task Scheduling in Cloud Computing Using Deep Reinforcement Learning

An AI-enabled dynamic task scheduling framework based on Deep Reinforcement Learning (DRL) with a Deep Q-Network (DQN) model to dynamically assign tasks to virtual machines and learn the best scheduling policies by continuously interacting with the cloud environment based on system parameters such as resource availability, task queue length, and virtual machine load is introduced.

A Deep Reinforcement Learning Framework for Scalable Cloud Service Orchestration

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.

AI-Assisted Resource Scheduling in Multi-Cloud Computing Environments

Cloud computing has evolved significantly, with multi-cloud environments becoming popular for improving scalability, reliability, fault tolerance, and cost efficiency. However, resource scheduling in multi-cloud systems remains challenging due to heterogeneous infrastructures, dynamic workloads, varying pricing models, network latency, and SLA requirements. Traditional scheduling methods such as Round Robin and FCFS often fail to adapt effectively to these complexities. Artificial Intelligence (AI) offers an advanced solution through intelligent resource scheduling. By leveraging machine learning, reinforcement learning, and predictive analytics, AI-based schedulers can forecast workloads, optimize resource allocation, and make autonomous scheduling decisions. This research proposes an AI-driven resource scheduling framework that integrates workload prediction, resource classification, intelligent scheduling, and continuous feedback mechanisms. The framework aims to optimize multiple objectives, including cost reduction, execution efficiency, energy consumption, and SLA compliance. Performance evaluation using metrics such as resource utilization, makespan, response time, throughput, and energy consumption demonstrates that AI-assisted scheduling outperforms traditional approaches. The results indicate improved resource utilization, better workload balancing, reduced operational costs, and enhanced service quality, highlighting the potential of AI-driven scheduling for next-generation multi-cloud resource management systems.

Intelligent Task Scheduling in Edge-Cloud Environments Using Double Deep Q-Network Reinforcement Learning

Experimental evaluation on a heterogeneous synthetic benchmark demonstrates that the proposed DDQN scheduler reduces SLA violations by approximately 85% relative to Round Robin and 72% relative to the greedy baseline, while achieving superior energy efficiency.