Resource Utilization Prediction Model for Cloud Datacentre: Survey

This survey aims to analyze resource prediction models in cloud environments to improve resource allocation strategies. It can be difficult for cloud service providers to maintain the required Quality of Service (QoS) requirements without going against a service level agreement (SLA). Improving cloud performance requires accurate workload prediction. To enhance customer service quality (QoS), cloud computing provides virtualisation, scalability, and on-demand services. Resource provisioning is a major challenge in the cloud environment due to its dynamic nature and the rapid increase in resource demand. Over-provisioning of resources leads to energy waste and increased expenses while under-provisioning can result in SLA breaches and reduced QoS. It is crucial to allocate resources as closely as possible to current demands. Cloud elasticity plays a key role in adapting to workload changes and maintaining performance levels. Predicting future resource demand is essential for effective resource allocation, which is the focus of this survey. Our survey uniquely focuses on comparing univariate and multivariate input cases for cloud resource prediction, a perspective that has not been deeply explored in similar surveys. Unlike existing works that primarily categorize models by methodologies or application characteristics, our study offers a novel analysis of how different input scenarios impact prediction accuracy, resource efficiency, and scalability. By addressing this overlooked aspect, our survey provides unique insights and practical guidance for researchers and practitioners aiming to optimize resource utilization in cloud environments. A thorough analysis of resource prediction models in cloud systems is presented in this research, including a comparison of predicted resources, prediction algorithms, datasets, performance metrics, a prediction summary, and a taxonomy of prediction methods. This survey not only synthesizes current knowledge but also identifies key gaps and future directions for the development of more robust and efficient resource prediction models.