Thermal-Aware Server Load Balancing in Data Centers: Real-Time Temperature and Energy Optimization Using OpenTelemetry Logs

As data center infrastructures scale to meet rising computational demands, thermal regulation and energy optimization have become key to operational efficiency. This study explores the application of real-time server telemetry—specifically OpenTelemetry logs—for thermal-aware load balancing. By analyzing CPU temperature and energy metrics from open telemetry datasets, we designed and evaluated a predictive task scheduler that reduces server hotspots and improves cooling efficiency. Simulation results show a 23% improvement in thermal distribution uniformity and a 15% reduction in energy consumption over static scheduling baselines. These findings suggest that telemetry-driven scheduling strategies can significantly extend hardware lifespan and contribute to greener data center operations.