Artificial Intelligence Techniques for a Proactive Auto-Scaling and Energy-Efficient VM Allocation Framework Using an Online Multi-Resource Capsule Shuffle Attention Network for Cloud Data Centres: Trends and Challenges

Abstract

Cloud data centres are essential for supporting modern digital services, including artificial intelligence applications, big data analytics, e-commerce platforms, and Internet of Things (IoT) systems. With the rapid expansion of cloud-based services, the demand for computing resources has grown significantly, creating challenges related to efficient resource allocation, energy consumption, and scalability. Cloud providers must dynamically allocate virtual machines (VMs) and scale resources to handle fluctuating workloads while minimizing operational costs and energy usage. Traditional resource management approaches, which rely on static or rule-based mechanisms, often fail to adapt to dynamic environments, resulting in poor resource utilization, service degradation, and increased energy consumption. To overcome these limitations, artificial intelligence-based techniques have gained attention for enabling proactive resource management. Machine learning and deep learning models can analyse historical workload data, predict future demands, and optimize VM allocation decisions. Recent advancements in deep learning, particularly capsule networks and attention mechanisms, have further enhanced these capabilities. Capsule networks effectively capture hierarchical data relationships, while attention mechanisms focus on the most relevant features, leading to improved prediction accuracy. Their integration in advanced architectures enables efficient modelling of complex resource patterns in cloud environments, supporting scalable and energy-efficient cloud operations.