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How did John Mosquera approach capacity planning for the Kubernetes clusters to ensure they could handle future growth?
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John Mosquera's approach to capacity planning for the Kubernetes clusters likely involved a combination of data analysis, predictive modeling, and scalable architecture design. He would have analyzed historical data on resource usage and traffic patterns to identify trends and project future needs. The implementation of predictive analytics suggests John used forecasting models to anticipate future capacity requirements. He likely established headroom policies to ensure the clusters always had spare capacity to handle unexpected spikes in demand. The use of Horizontal Pod Autoscalers and Cluster Autoscaler provided a foundation for automatic scaling, but John would have needed to regularly review and adjust their configurations based on observed patterns and future projections. He may have implemented capacity testing to validate the system's ability to scale to projected future loads. John's training of the client's SRE team on resource management and cost optimization techniques indicates a focus on ongoing capacity optimization. John approached capacity planning through data analysis, predictive modeling, and scalable architecture design. He likely combined historical analysis with forecasting models, implemented automatic scaling with headroom policies, and conducted regular reviews and capacity testing. This comprehensive approach ensured the Kubernetes clusters were well-prepared to handle future growth while maintaining high performance and cost-efficiency.