Resource Scheduling Using AI in Cloud Environments
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P122Keywords:
Cloud Computing, Resource Scheduling, Artificial Intelligence (AI), Machine Learning, Task Scheduling, Dynamic Resource Allocation, Virtual Machine (VM) Allocation, Container Orchestration, Load Balancing, Workflow Scheduling, Auto-Scaling, Quality of Service (QoS), Service Level Agreement (SLA), Energy-Efficient Computing, Cost Optimization, Predictive Analytics, Reinforcement Learning, Heuristic Optimization, Metaheuristic Algorithms, Distributed SystemsAbstract
Efficient resource scheduling is a foundational challenge in cloud computing environments, where dynamic workloads, heterogeneous resources, and stringent service-level agreements (SLAs) converge to complicate optimal task allocation. Traditional scheduling algorithms such as Round-Robin, Min-Min, and other heuristic/metaheuristic approaches often struggle to adapt in real time to fluctuations in demand, resource availability, and cost-energy trade-offs [1],[2]. In contrast, artificial intelligence (AI) techniques including supervised learning, reinforcement learning (RL), and hybrid optimisation models offer adaptive and predictive capabilities that significantly enhance scheduling performance by learning from past workload patterns and system feedback [3],[4]. This paper investigates the application of AI-driven resource scheduling in cloud environments, proposing a framework that integrates workload forecasting, dynamic resource allocation, and SLA compliance optimisation. Experimental evaluation, based on simulated and real-trace datasets, demonstrates that the proposed AI-based scheduling approach can improve resource utilisation, reduce energy consumption, and lower SLA violation rates compared to baseline methods. The findings highlight the potential of AI to reshape scheduling in modern cloud infrastructures and underscore the need for further research into scalability, transparency of AI decisions, and hybrid edge-cloud deployment scenarios.
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