Mamushiane, LMwangama, JLysko, Albert AKobo, Hlabishi I2026-02-212026-02-212025-12979-8-4007-2158-8https://doi.org/10.1145/3774791.3774806http://hdl.handle.net/10204/14702Network slicing has emerged as a key enabler for delivering diverse services in 5G and beyond networks, where each slice must be provisioned with sufficient resources while maintaining operator profitability. Traditional admission control strategies are often conservative, leading to underutilization, while aggressive allocation risks violating service-level agreements (SLAs). This paper proposes a machine learning-based forecasting and admission control framework that leverages overprovisioning to balance utilization, revenue, and reliability. The framework combines CNN-LSTM forecasting for predicting slice resource demand with a policy that admits slice requests based on forecasted aggregate utilization scaled by an overprovisioning factor. To evaluate the approach, we use over 500 VM workload traces from the Materna dataset, grouping VMs into slices of ten. Results demonstrate that CNN-LSTM forecasting achieves consistent accuracy across diverse workloads, and that overprovisioning-based admission control improves net profit while reducing SLA violations compared to conservative baselines. These findings validate the feasibility of ML-driven overprovisioning for efficient and reliable slice admission in 5G networks.Fulltexten5GNetwork slicingArtificial IntelligenceAdmission controlMachine learningQ-learningResource over-provisioningReinforcement learningArticleN/A