Deep Learning and Optimization Approaches in Resource Allocation with Efficient Task Scheduling in Cloud Computing Using Hierarchical Auto-Associative Polynomial Convolutional Neural Network: A Review

Abstract

Cloud computing has emerged as a dominant paradigm for delivering scalable and on-demand computing resources. Efficient resource allocation and task scheduling are critical challenges in cloud environments due to the dynamic nature of workloads and heterogeneous resource availability. Traditional scheduling algorithms often fail to achieve optimal performance in terms of resource utilization, energy efficiency, and Quality of Service (QoS). Recent advancements in artificial intelligence, particularly deep learning and optimization techniques, have significantly improved cloud resource management. Deep neural networks, including convolutional neural networks (CNNs) and hybrid models, enable intelligent decision-making by learning complex patterns from workload data. Moreover, optimization algorithms such as genetic algorithms, particle swarm optimization, and reinforcement learning have been widely applied to enhance scheduling efficiency. A recent approach integrates hierarchical auto-associative polynomial convolutional neural networks (HAPCNN) with optimization techniques for efficient resource allocation. This model leverages hierarchical feature extraction and adaptive learning to improve scheduling decisions, leading to better resource utilization and reduced execution time. This review presents a comprehensive analysis of deep learning and optimization approaches for cloud resource allocation and task scheduling, comparing methodologies, identifying challenges, and highlighting future research directions.