LEVERAGING PREDICTIVE MODELING FOR DYNAMIC POWER CONTROL IN MOBILE CPUS

Dynamic power control strategies for mobile CPUs have been developed to meet the growing demand for performance and energy efficiency of mobile devices.Current approaches, however, still resort to static heuristics or real-time feedback mechanisms and frequently do not fully exploit the dynamic nature of workload variations and thermal constraints.In this paper, we propose a predictive modeling framework based on machine learning techniques for workload demand prediction and the proactive adjustment of CPU power settings.Our model dynamically balances power consumption and processing speed, which depends on the historical performance data, and it also follows other factors like the type of application and user behavior.The framework uses lightweight predictive algorithms with minimal computational overhead to respond in resource-constrained environments.Real-world experiments with significant energy efficiency and thermal management improvements are demonstrated while maintaining or improving user-perceived performance.The proposed approach is compared with existing power control mechanisms regarding prediction accuracy, system stability, and adaptability to workload dynamics.The unique contribution of this research to the emerging field of energy-efficient computing is a power management solution that is scalable, intelligent, and specifically designed for modern mobile devices.This study provides valuable insights towards future