DOLCESCU Constantin-ValentinBOTEZ RobertZINCA DanielDOBROTA Virgil2025-07-142025-06-25ISSN 1221 – 6542https://oasis.utcluj.app/handle/123456789/700The paper presents a decision tree–based scheduler for intelligent cloud process allocation that evaluates features such as source area, instruction count, payload size, priority, throughput, and delay to guide real-time placement decisions. The model was trained and validated on a diverse, scenario-driven synthetic dataset covering four controlled workload conditions plus randomized fallback cases. For the training dataset, the classifier achieved 93% accuracy, while for the validation and test set, an accuracy of 92% was obtained. A Kubernetes-inspired simulation framework further visualizes and confirms the scheduler’s allocation logic under dynamic conditions. These results underscore the approach’s effectiveness, interpretability, and suitability for production-grade cloud orchestration.encloud computingDecision TreesMachine Learningprocess allocationresource optimization.dataset