Implementation of the Fremer Model to Optimize Kubernetes Configuration in Concert Ticketing Application Deployment

Abstract

As digital services evolve, the need for scalable and robust cloud-native applications increases. Kubernetes has become one of the most widely used container orchestration platforms. However, Kubernetes takes a while to react to unexpected surges in workload because its built-in autoscaling method is still reactive. This has been identified as one of the challenges for extreme traffic applications, such as selling concert tickets, which can generate millions of requests within seconds. This research work presents the implementation of the Fremer model, which is a lightweight frequency domain transformer that can be used to predict workloads and support proactive autoscaling for environments using Kubernetes. A dummy concert ticketing application was used on Google Cloud Kubernetes Engine and was monitored using Prometheus for Central Processing Unit (CPU) usage. A user behavior survey was conducted to determine realistic workload patterns in the concert ticket sales application. To create a dataset with realistic workload patterns, JMeter-based stress tests were run using the obtained survey data. The collected dataset was used to train the Fremer model to predict CPU usage ahead of time and provide early scaling decisions. Experimental results show that this model can capture general CPU usage trends and predict spikes well in advance of actual spikes. This allows for proactive provisioning of pods before peak load occurs. Although performance degrades under highly volatile spike conditions, the results show that Fremer can improve predictive autoscaling for moderately volatile scenarios. Overall, the implementation shows that Fremer can improve the efficiency and responsiveness of Kubernetes resources by shifting autoscaling from reactive to predictive.