Machine Learning Optimization for Cloud Resource Utilization and Capacity Planning Strategies

Background: Due to an unstable workload and a change in demand, cloud computing environments experience great difficulties with resource allocation and capacity planning. Conventional capacity planning techniques use fixed thresholds and past-based data analysing, which in most cases cannot accommodate the dynamic, non-stationary characteristics of resource usages patterns in contemporary cloud infrastructures.

Materials and Methods: This work was conducted under the framework of broad systematic literature review, based on PRISMA principles, taking the 44 peer-reviewed journal articles in Google Scholar, ResearchGate, ScienceDirect, and other academic repositories. The secondary data collection measures contained bibliometric analysis, content analysis, and comparison of machine learning algorithms, linear regression, polynomial regression, neural networks, and random forest models to project various CPU, memory, and storage usage in cloud computing.

Results: The systematic review noted that the Random Forest models will be better predictors of storage and memory consumption with R 2 of greater than 0.90. Neural networks also depicted favorable outcomes in the prediction of CPU utilization with the R2 of 0.87 to explain 87% of explanation of variance of CPU used behavior. The result showed that a high resource allocation efficiency of 15-93% cost savings is achieved against standard methods of a traditional static resource allocation.

Discussion: The techniques based on machine learning demonstrated much better performance than traditional statistical approaches to capture the sequence of the dependencies and intricate patterns of the usage. The deep learning and the LSTM & GRU models showed an extraordinary superiority in the non-stationary time series data of the cloud resources utilization. Predictive models can be integrated with cloud management systems, allowing scale-and capacity optimisation decision making to occur proactively.

Conclusion: Machine learning can be applied to optimize prediction and capacity planning strategies of cloud resources that could considerably improve performance. Development of ML-based predictive models will provide the cloud service providers with more efficient allocation of their resources, achieves lower-operating costs, and better service delivery quality utilizing proactive capacity management strategies.