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 need to be allocated promptly and efficiently. The current scheduling mechanisms being relatively static under varying system conditions leads to inefficient allocation with a slight (to 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 are 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 LC scheduling uses a GA-based approach to form a group of shuttle schedules (candidate emergency transportation solution) where the fittest one is selected and executed using genetic operators like selection, crossover, mutation, etc. It allows the system to adjust resources more effectively for its current state, leading to better performance and scalability.