Optimizing resource allocation in cloud-native applications through proactive autoscaling with the InformerAutoScale model

Optimizing resource allocation in cloud-native applications through proactive autoscaling with the InformerAutoScale model

연구 분야: Software Development

학회: The Journal of Supercomputing

Cloud-native applications are designed to utilize cloud computing resources efficiently. These applications automatically scale resources by managing containerized copies of files and creating containers, which are handled through pods in Kubernetes. However, they face challenges due to the dynamic workload associated with automatic scaling and de-scaling in cloud environments. This makes it difficult to obtain accurate monitoring information, particularly with reactive autoscaling. This research presents a proactive autoscaling approach through the proposed InformerAutoScale model, which predicts resource requirements for long sequences in cloud-native applications to enable accurate pod scaling and descaling. Experimental results demonstrate that the InformerAutoScale approach effectively reduces resource waste and manages issues such as under and over-provisioning. The real-world implementation was carried out using Docker Desktop and Kubernetes, with scale or scaled pods allocated based on application requests. Proactive autoscaling achieved a 90.66% improvement in scaling efficiency compared to reactive methods.