Integrating Multi-Agent Reinforcement Learning and XGBoost for Efficient Large-Scale Dynamic Workflow Scheduling in Cloud Environments

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

Effective task scheduling is essential in cloud computing to maximize resource utilization and satisfy Service Level Agreement (SLA) requirements in highly dynamic and heterogeneous environments. Conventional scheduling algorithms, like Round Robin, Min-Min, and metaheuristic-based methods, rely on static or preset rules that are unable to respond to changes in the workload in real time. This results in longer execution times, higher energy consumption, and less-than-ideal system performance. In order to overcome these constraints, this paper introduces 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. To balance makespan, energy consumption, throughput, and SLA violation rate, a multi-objective reward function is developed. The proposed DRL-based scheduler for intelligent and adaptive scheduling in present cloud computing settings is deployed and tested in CloudSim Plus simulator platform for diverse workload situations to affirm the usefulness of the framework. The results demonstrate that the DRL-based scheduler outperforms the conventional scheduling schemes by 23.6% in makespan, 27% in energy efficiency, 49% in SLA violations, and 33% in throughput.

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

Cloud computing environments face increasingly complex challenges in task scheduling due to dynamic workloads, heterogeneous resources, and multi-objective optimization requirements. This paper proposes an innovative Deep Reinforcement Learning (DRL)-based Intelligent Task Scheduling Framework (DRITS) designed to optimize task allocation and resource utilization in cloud distributed systems. The proposed framework leverages advanced Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) algorithms to enable dynamic, adaptive scheduling that continuously learns optimal policies through interaction with the cloud environment. Our comprehensive evaluation demonstrates that DRITS achieves significant performance improvements, including 32.4% reduction in makespan, 48.7% lower energy consumption, and 22.6% improvement in resource utilization compared to traditional heuristic algorithms [1]. Extensive simulations using real-world Google Cluster workloads and diverse benchmark datasets validate the robustness and scalability of the proposed approach across varying workload conditions. The framework demonstrates strong adaptability to dynamic environments, fault tolerance capabilities, and superior performance in multi-objective optimization scenarios. These results establish DRL-based intelligent scheduling as a promising solution for next-generation cloud computing infrastructure management.

Deep reinforcement learning for cost-efficient resource management in hybrid cloud environments

Sensitivity and ablation studies confirm stable learning and controllable latency-cost trade-offs, demonstrating that lightweight RL can effectively deliver cost-efficient, adaptive autoscaling in hybrid cloud environments.

A Deep Reinforcement Learning Framework for Scalable Cloud Service Orchestration

Reinforcement Learning for Adaptive Resource Management in Cloud Software

Cloud software systems operate under highly dynamic and unpredictable workloads, requiring efficient and adaptive resource management strategies to maintain performance, reliability, and cost efficiency. Traditional rule-based and heuristic resource allocation approaches often fail to respond optimally to rapid workload fluctuations and complex system interactions. This paper proposes a reinforcement learning-based adaptive resource management framework that enables cloud systems to autonomously learn optimal resource allocation policies through continuous interaction with the environment. By modeling cloud resource management as a sequential decision-making problem, the framework leverages reinforcement learning algorithms such as Q-learning, Deep Q-Networks (DQN), and policy-gradient methods to dynamically adjust computing resources including CPU, memory, and virtual machine instances. The proposed approach aims to optimize multiple objectives such as performance, cost, and service-level agreement (SLA) compliance. Experimental evaluation using simulated and real-world cloud workloads demonstrates that reinforcement learning significantly outperforms static and reactive baseline strategies in terms of resource utilization efficiency and response time stability. The results highlight the potential of reinforcement learning to enable intelligent, self-adaptive cloud resource management systems.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application

Time-sensitive networking (TSN) is increasingly integrated into mobile edge computing (MEC) to support applications with stringent latency requirements, such as extended reality (XR). However, existing TSN scheduling solutions predominantly rely on static optimization techniques or centralized learning models that are based on fixed traffic patterns, limiting their effectiveness in dynamic environments. In practice, MEC environments often host multiple co-located XR traffic flows whose characteristics evolve over time, creating complex inter-queue dependencies that current schedulers fail to capture. Addressing these challenges requires adaptive, decentralized scheduling mechanisms capable of coordinating multiple TSN queues under varying traffic conditions. To this end, this paper proposes a multi-agent reinforcement learning (MARL) framework for TSN scheduling, where each TSN queue is modeled as an autonomous agent. The Heterogeneous-Agent Proximal Policy Optimization (HAPPO) algorithm is employed to explicitly model inter-agent dependencies and jointly optimize service delivery across queues. The simulation results demonstrate that the proposed approach reduces average frame waiting times by up to 26.8% and worst-case delays by approximately 16.8%, highlighting its effectiveness in dynamic XR-driven MEC scenarios.