Optimizing Resource Allocation: Artificial Intelligence Techniques for Dynamic Task Scheduling in Cloud Computing Environments

Abstract

Efficient resource allocation and dynamic task scheduling are critical challenges in cloud computing environments, where diverse workloads and fluctuating demand patterns necessitate adaptive and responsive allocation strategies. This paper explores the application of artificial intelligence (AI) techniques for optimizing resource allocation and task scheduling in cloud environments. Leveraging the capabilities of AI, including machine learning, optimization algorithms, and reinforcement learning, this study aims to address the complexities of resource management and scheduling in dynamic and heterogeneous cloud infrastructures. By analyzing workload characteristics, resource availability, and performance objectives, AI-based approaches enable intelligent decision-making to allocate resources effectively, minimize response times, and optimize resource utilization. This paper provides a comprehensive overview of AI-driven techniques for dynamic task scheduling, including genetic algorithms, particle swarm optimization, and deep reinforcement learning, highlighting their strengths, limitations, and practical considerations in cloud environments. Furthermore, the study investigates the impact of workload variability, resource contention, and QoS requirements on the performance of AI-based scheduling algorithms, offering insights into their scalability, adaptability, and robustness in real-world deployment scenarios. Through empirical evaluations and case studies, the paper demonstrates the efficacy of AI-driven approaches in improving the efficiency and agility of cloud resource management, paving the way for cost-effective, scalable, and responsive cloud services. Moreover, the study discusses emerging research directions and challenges in AI-based resource allocation, such as federated learning, edge computing, and fairness-aware scheduling, to address evolving demands and emerging paradigms in cloud computing. By advancing the state-of-the-art in AI-driven resource management, this research contributes to the development of intelligent cloud platforms capable of meeting the evolving needs of modern applications and enabling transformative advancements in cloud computing technologies.