Deep Learning and Optimization Approaches in A Proactive Auto-scaling and Energy-Efficient VM Allocation Framework Using an Online Multi-Resource Capsule Shuffle Attention Network for Cloud Data Centres: A Review

Abstract

Cloud computing has become a fundamental component of modern infrastructures, providing scalable and flexible access to computing resources through distributed data centres. As digital services continue to expand, cloud systems must efficiently handle large and dynamic workloads while ensuring high performance, energy efficiency, and sustainability. A major challenge in such environments is the optimal allocation of virtual machines (VMs) to physical servers, as inefficient allocation can lead to resource underutilization, excessive energy consumption, and violations of service level agreements (SLAs). To address these issues, proactive auto-scaling and energy-efficient VM allocation frameworks have gained significant attention. Unlike traditional reactive approaches that respond after workload changes, proactive methods use predictive models to anticipate demand and allocate resources in advance, improving system responsiveness and efficiency. Recent advancements highlight the integration of deep learning and optimization techniques to enhance prediction accuracy and allocation strategies. Models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and capsule networks effectively capture complex workload patterns, while attention mechanisms help focus on critical resource features, leading to improved performance and efficient resource management in cloud data centres.