A Survey on Stochastic Regression Models for Forecasting Cloud Workload

Cloud computing has become the backbone of modern IT infrastructure, enabling elastic resource provisioning and pay-as-you-go models. As enterprises migrate more workloads to the cloud, the challenge of predicting workload demand and managing resource utilization effectively has grown. Predictive models—especially those leveraging machine learning (ML) and deep learning (DL)—play a crucial role in ensuring that resources are allocated efficiently, costs are minimized, and performance meets Service Level Agreements (SLAs) Since cloud data is large and complex at the same time, hence it is necessary to use artificial intelligence based techniques for the estimation of cloud workload so as to improve upon the accuracy of conventional techniques. This paper presents a review on the contemporary techniques for cloud workload prediction. The performance evaluation parameters have also been discussed. Future research directions in terms of machine learning and deep learning algorithms for cloud workload prediction have been presented.