Evaluating Kubernetes Pod Scaling Techniques for Event-Driven Applications
Keywords:
Kubernetes, Horizontal Pod AutoscalingAbstract
As cloud-native architectures continue to gain momentum, Kubernetes has become a cornerstone for managing containerized applications, offering flexibility and scalability. Among the various types of workloads, event-driven applications, which respond to asynchronous events, present unique challenges due to their dynamic and unpredictable nature. These applications require high elasticity to efficiently manage fluctuating workloads, scaling up during bursts of activity and scaling down during periods of low demand. Kubernetes provides a range of pod scaling techniques, two of the most prominent being Horizontal Pod Autoscaling (HPA) & custom metrics-based scaling. HPA, which automatically adjusts the number of pods based on resource usage such as CPU and memory, is well-suited for applications with predictable scaling needs. However, event-driven systems often experience irregular load patterns, making HPA’s reliance on standard metrics less ideal for every scenario. In these cases, custom metrics-based scaling offers a more precise solution by allowing users to scale based on specific metrics such as event queue length or application-specific performance indicators, ensuring that the system can handle workloads more efficiently. Amazon Elastic Kubernetes Service (EKS), which provides a fully managed Kubernetes environment, supports both of these scaling methods, enabling users to leverage the full capabilities of Kubernetes for event-driven workloads. The choice between HPA and custom metrics-based scaling depends on the specific requirements of the event-driven application, including factors such as the type of events processed, the frequency of events, and the need for rapid scaling. HPA offers simplicity and ease of use, but its reliance on basic resource metrics can lead to under or over-scaling in more complex use cases. On the other hand, custom metrics scaling provides more granular control but requires additional configuration and monitoring. This paper evaluates the strengths & limitations of these pod scaling techniques within EKS for event-driven applications, giving practical insights into which method is best suited for different workloads.
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