AUTOSCALING BASED ON RESPONSE TIME PREDICTION FOR MICROSERVICE APPLICATION IN KUBERNETES

• Main Author: Ayu Pramesti, Annisa
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/66596
• Institution: Institut Teknologi Bandung

Containerized applications are evolving along with the microservice architectures in distributed application development. This trend shows the importance of managing and orchestrating containerized applications thus applications can operate properly. One of the aspects of container orchestration is scaling or increasing the application's ability to handle more requests. In this study, an autoscaler based on response time prediction is developed for microservice applications in Kubernetes environment. The prediction function is developed using a machine learning model that features performance metrics at the microservice and node levels. The response time prediction is then used to calculate the number of replicas required by the application to meet the target response time. The results obtained are that the proposed autoscaler can serve requests with smaller error between response time and target than Kubernetes Horizontal Pod Autoscaler with a target CPU usage. However, the proposed autoscaler still cannot serve as many requests as Kubernetes Horizontal Pod Autoscaler with the target CPU usage. The proposed autoscaler also consumes more node resources than Kubernetes Horizontal Pod Autoscaler with targeted CPU usage because it scales out more frequently.