Improving Mobile Computing Performance with AI-Powered Dynamic Task Scheduling Algorithms

They play a huge role in the interaction and collaboration in the society, business, economy, communication and even in entertainment, calendar, calculator etc. Nevertheless, mobile environments are dynamic and often characterised by limited resources making performance optimisation a hurdlesome task. This research aims to determine how AI, specifically, can be employed to improve the rate of dynamic task scheduling in mobile computing. The devised scheduling algorithms incorporate principles of machine learning to study characteristics of the execution of tasks and needed resources, plus energy consumption, in real time. It adapts to mobile devices' resources, reduces response time, manages computational loads, and optimistically and fault-tolerantly prioritizes tasks across mobile and cloud computing environments. Scheduling innovations include selfadaptive processes which adjust the scheduling decisions based on the users and other systems' actions and adaptive models to prevent resource overloads. The analysis compares the performance of AI-based scheduling with other approaches based on parameters like computational speed, energy consumption, and QoS parameters. This work proves that there are large enhancements in resource efficiency and quality of use, especially in conditions with massive levels of task parallelism and differentiation. By providing a feasible and effective solution for the problem attributed to mobile computing, this work presents the path to achieving smarter, more effective mobile systems to support the prompt execution of tasks which are indispensable in an advanced society and economy.