Optimizing Cloud Computing Resource Allocation Through Intelligent Strategies

In the realm of cloud data centers, accurately anticipating and managing future resource supply proves challenging due to the dynamic nature of the cloud and its reliance on real-time, ever-changing usage needs. Real-time data analysis is pivotal in predicting cloud resource utilization, enabling proactive resource provisioning, estimating server capacity, and implementing automated scaling of virtual machines to optimize resource usage. This article focuses on a proactive approach to addressing this intricate challenge. While current research primarily concentrates on predicting workloads with evident seasonality or trends, or irregular workload patterns, our study introduces a novel perspective on both seasonal and non-seasonal resource demand forecasting. To achieve this, we propose a prediction model that combines statistical techniques with machine learning. Leveraging the assumption of seasonality in workload patterns, we employ the Seasonal Autoregressive Integrated Moving Average (SARIMA) model for prediction. For non-seasonal workloads, the choice between a Long Short-Term Memory (LSTM) network and an Autoregressive Integrated Moving Average (ARIMA) model is determined based on the results of a normality test. The SARIMA model demonstrates accurate forecasts of resource usage, empowering Cloud Service Providers (CSPs) to analyze workloads and make informed predictions, thereby avoiding the pitfalls of over- or under-provisioning cloud resources.