QUEUEING THEORY BASED KUBERNETES AUTOSCALER

Abstract

The microservices architecture is emerging as a new architectural style for designing and developing applications by composing loosely coupled services that exchange standard messages using standard interfaces and protocols. Docker provides a platform to automate microservices deployment into isolated containers. Kubernetes automates the deployment, scaling and management of Docker containers. Unlike current virtual machines (VM) based deployment, containerization allows more effective scaling of resources to meet the requirements of varying workloads. Benefiting from the research advances in VMs consolidation, placement and auto-scaling approaches, as well as the queueing theory, our work provides a custom queueing theory based auto-scaler for Kubernetes, which dynamically make vertical and horizontal scaling decisions. The auto-scaler goal is to achieve the desired Quality of Service (QoS) while optimizing the cloud resources usage.