A Comprehensive Analysis of Dynamic Task Scheduling for Cloud Computing Environment

Abstract

Cloud computing has revolutionized the way computational resources are made available through scalable, flexible, and on-demand services. As promising as it is, efficient task scheduling remains an important challenge because of varied workloads, heterogeneous infrastructures, and multi-objective optimization requirements for cost, execution time, and power efficiency. Legacy scheduling approaches frequently fail to meet these demands, and thus the idea of hybrid frameworks fusing artificial intelligence (AI) and optimization techniques has been developed. In this paper, dynamic task scheduling techniques that use deep learning, metaheuristic optimization, and heuristic algorithms to be more efficient are discussed. Specific focus has been placed on energy-efficient models like adaptive Particle Swarm Optimization (PSO) and multi-objective scheduling models with a balance between performance and sustainability. The developed AI-based model utilizes deep learning to predict the workload, optimization techniques to achieve multi-objective trade-offs, and reinforcement learning to adapt in real-time. The research helps in the development of multi-objective, adaptive, and sustainable task scheduling for cloud platforms with future prospects on federated learning, edge/fog integration, and security-conscious scheduling techniques.