Optimizing Kubernetes Scheduling for Web Applications Using Machine Learning

Machine learning (ML) has significantly enhanced computing and optimization, offering solutions to complex challenges. This paper investigates the development of a custom Kubernetes scheduler employing ML to optimize web application placement. A cluster of five nodes was established for evaluation, utilizing Python and TensorFlow to create and train a neural network that forecasts scheduling times for various configurations. The dataset, generated via scripts, encompassed multiple scenarios to ensure thorough model training. The results indicate that the custom scheduler with ML consistently surpasses the default Kubernetes scheduler in scheduling time by 1–18%, depending on the scenario. As expected, the difference between the built-in and ML-based scheduler becomes more evident with higher loads, underscoring opportunities for future research by using other ML algorithms and considering energy efficiency.