Recent Advances 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 Systematic Review

Abstract

Cloud data centres have become the backbone of modern digital infrastructure, supporting a wide range of services including cloud computing, artificial intelligence applications, big data analytics, Internet of Things (IoT) platforms, and large-scale web services. These data centres host thousands of physical servers and virtual machines (VMs) that provide scalable computing resources to users on demand. However, the continuous growth of cloud-based applications has significantly increased computational workloads, resulting in high energy consumption and rising operational costs. Excessive power usage in cloud environments also contributes to environmental concerns through increased carbon emissions, making energy-efficient resource management a critical research challenge. Auto-scaling mechanisms are widely used to dynamically adjust computing resources according to workload variations, thereby ensuring optimal system performance and minimizing resource wastage. Traditional reactive auto-scaling techniques allocate resources only after workload spikes occur, which may cause delayed responses, service level agreement (SLA) violations, and inefficient resource utilization. To overcome these limitations, proactive auto-scaling approaches employ predictive models that analyse historical workload patterns to forecast future resource demands and provision resources in advance. In addition, energy-efficient VM allocation strategies aim to optimize virtual machine placement and workload consolidation across physical servers, reducing power consumption while maintaining performance, reliability, and service availability in large-scale cloud data centres.

Kaoru Uppalapati

Associate Professor, Department of Electronics and Communication Engineering, Chiang Thon College of Management, Thailand

Email: kaoru.uppalapati@ctcm-th.org