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, big data analytics, e-commerce, and Internet of Things (IoT) applications. The rapid growth of cloud-based systems has significantly increased the demand for computing resources, leading to critical challenges in resource allocation, energy consumption, and service scalability. Efficient virtual machine (VM) allocation and dynamic resource scaling are necessary 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 inefficient utilization, performance degradation, and increased energy consumption. To address these limitations, recent research has focused on artificial intelligence-based solutions for proactive cloud resource management. Machine learning and deep learning techniques enable accurate workload prediction and optimized VM allocation, improving system performance and reducing energy usage. Furthermore, advanced deep learning models, such as capsule networks and attention mechanisms, have demonstrated strong potential in capturing complex resource patterns. Capsule networks enhance feature representation by modeling hierarchical relationships, while attention mechanisms focus on relevant data features for efficient processing. Their integration in architectures like Capsule Shuffle Attention Networks offers a promising approach for developing intelligent, energy-efficient, and scalable cloud resource management frameworks.