Efficiency and Qos in Cloud Data Centers: An Analysis of Dyvofesloremu's Sla Violations

The increasing complexity and energy demands of modern cloud data centers have necessitated intelligent resource management strategies that balance efficiency with Quality of Service (QoS). Central to maintaining this balance are Service Level Agreements (SLAs), which formalize expectations between service providers and clients. This study analyzes DyVoFesLoReMu, a hybrid energy optimization framework that integrates Dynamic Voltage/Frequency Scaling (DVFS) with enhanced Linear Regression Minimum Utilization (LrMu) predictors. The model achieves significant energy savings of up to 90%, while SLA violations are maintained within a tolerable range of 5–9%. A novel contribution of this research is the integration of SLA lifecycle methodologies, supported by a generic SLA map, to contextualize decision-making, monitoring, and violation analysis. Experimental evaluations using CloudSim and real workload traces confirm the trade-offs between aggressive consolidation and SLA stability. The study also presents mitigation strategies including adaptive thresholding, predictive scaling with LSTM models, and brownout mechanisms. The findings underscore the importance of SLA-aware energy management in designing resilient and sustainable cloud infrastructures.