Automatic and Dynamic Scaling refers to the ability of a system or infrastructure to adjust its capacity, resources, or performance levels automatically and in real-time based on the current demand or workload. This capability is particularly valuable in cloud computing environments and distributed systems where workloads can vary over time.

Both automatic and dynamic scaling are essential features in modern cloud-based and distributed systems, contributing to their flexibility, efficiency, and ability to handle varying levels of demand seamlessly. Many cloud service providers offer tools and services that enable organizations to implement automatic and dynamic scaling strategies for their applications and services.

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Content-aware auto-scaling of stream processing applications on container orchestration platforms

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Modern applications are designed as an interacting set of microservices, and these applications are typically deployed on container orchestration platforms like Kubernetes. Several attractive features in Kubernetes make it a popular choice for deploying applications, and automatic scaling is one such feature. The default horizontal scaling technique in Kubernetes is the Horizontal Pod Autoscaler (HPA). It scales each microservice independently while ignoring the interactions among the microservices in an application. In this paper, we show that ignoring such interactions by HPA leads to inefficient scaling, and the optimal scaling of different microservices in the application varies as the stream content changes. To automatically adapt to variations in stream content, we present a novel system called DataX AutoScaler that leverages knowledge of the entire stream processing application pipeline to efficiently auto-scale different microservices by taking into account their complex interactions. Through experiments on real-world video analytics applications, such as face recognition and pose classification, we show that DataX AutoScaler adapts to variations in stream content and achieves up to 43% improvement in overall application performance compared to a baseline system that uses HPA.

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