Optimizing Real-Time Task Scheduling in Cloud-based AI Systems using Genetic Algorithms

Artificial Intelligence (AI) has also started to be regularly introduced in cloud-based systems as it can help improve performance and skyrocket efficiency. It is a challenge for such systems to schedule tasks in real time, and computing sources must be allocated promptly and efficiently. The current scheduling mechanisms, being relatively static under varying system conditions, lead to inefficient allocation with a slight (too severe) degradation in performance. This project suggests that Genetic Algorithms (GA) be employed for real-time task scheduling optimization in AI services on the cloud. GAs is robust optimization methods based on fundamental natural selection and survival of the fittest principles, thus well suited for solving complex issues like task scheduling in dynamic environments. The proposed scheduling mechanism uses a GA-based approach to generate a population of candidate task schedules. The fittest solution is then selected using genetic operators such as selection, crossover, and mutation. It allows the system to adjust resources more effectively for its current state, leading to better performance and scalability.