Anticipate bandwidth bottlenecks before they impact service quality.
Equipment-level and site-level ranking indicating power reduction opportunity, including centiles and deciles
Identify key patterns driving optimization opportunities (e.g., traffic volume trends, time-of-day usage, geographic demand patterns)
Performance assessment including projected energy cost savings, carbon footprint reduction, and ROI analysis
Implement intelligent power management strategies while maintaining performance
This describes how far ahead in the future this model can be trained to predict. Ideally, the historical data available for training would be 2-3 times longer than the prediction window.
Accurately predict and capitalize on low-utilization periods.
Reduce energy waste with intelligent, automated power management.
A typical modeling engagement with GlorifAI lasts 3-4 weeks once we get access to the data and proceeds in three distinct phases.
