Understanding and optimizing pod capacity is crucial for autoscaling, ensuring your Kubernetes microservices can handle peak loads without performance degradation.
Setting appropriate resource requests and limits for CPU and memory is essential. Use metrics from load testing to inform these settings, maintaining system stability.
Implementing autoscaling based on calculated target utilization percentages helps maintain application performance, especially under varying loads, by providing necessary resources dynamically.
Utilizing additional metrics like event-based performance indicators can enhance autoscaling decisions beyond traditional CPU and memory usage.
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