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.
Abstract
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.
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.
Vishakha Makode, Taresh Ayaspure· Journal of Advances in Devel...· 0 citations
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· مجلة العلوم الشاملة· 0 citations
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.
Michael Anderson· International Journal of App...· 0 citations
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· International Journal of Mac...· 0 citations
Energy efficiency in cloud computing has become a critical concern due to the growing energy demands of large-scale datacenters, operational costs and environmental impacts. This research proposes Deep Learning-Enhanced Energy-Aware Task Scheduling (DL-EATS), a novel solution that combines LSTM-based workload prediction with a hybrid multi-objective optimization model integrating Genetic Algorithm and Particle Swarm refinement. DL-EATS intelligently schedules tasks to virtual machines or containers by anticipating workload variations, thereby minimizing energy consumption, ensuring SLA compliance and maximizing system throughput. The method was evaluated against state-of-the-art algorithms, including Reinforcement Learning-based Virtual Machine Placement (RLVMP), Enhanced Adaptive Moth-Flame Optimization (EA-MFO), Q-Learning-based Resource Dynamic Optimization (Q-RDO) and Task Scheduling using Grey Wolf Optimizer (TS-GWO), under identical cloud workload scenarios. Experimental results demonstrate that DL-EATS achieves the lowest energy consumption (480 kWh), shortest makespan (38 s), minimal SLA violation rate (1.2%) and highest resource utilization (92%), representing an 18.5% improvement in energy efficiency over the next best method and substantial gains across all performance metrics. These findings confirm that integrating predictive deep learning with hybrid heuristic optimization provides a scalable, reliable and energy-efficient solution for modern cloud datacenter management.
Abdulmumini Adamu, A. A. Abdulwasiu· Journal of Science Research...· 0 citations
Battery life remains a major constraint on the continuous operation of mobile IoT devices represented by drones. Most existing computation offloading strategies rely on static heuristic rules, which fail to deliver stable performance in dynamically changing wireless mobile environments. Targeting this problem, this paper develops a multi-agent deep reinforcement learning approach for Spark job scheduling across local devices, edge servers and cloud resources. The scheduling task is modeled as a Markov decision process: six real-time system and network metrics, including CPU utilization, memory load, handover delay, signal strength, transmission latency and congestion level, form the state space, and the reward function is built directly on actual measured energy consumption data. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, the proposed method adopts a centralized training and decentralized execution paradigm to realize distributed collaborative decision-making. Validation on a public MEC dataset shows that the trained policy finally converges to a hybrid scheduling mode: 57% of tasks are processed locally, 39% are offloaded to edge nodes, and less than 4% are assigned to cloud resources for specific application scenarios. Comparative tests with PPO, FIFO, FAIR and HAS baselines confirm that multi-agent reinforcement learning can well capture the intrinsic scheduling patterns of complex mobile environments, providing an adaptive and energy-efficient scheduling solution for practical IoT deployments.
Haoyu Gu· Scientific Journal of Intell...· 0 citations