A Machine Learning Model for Detection of Docker-based APP Overbooking on Kubernetes

Resource allocation overbooking is an approach used by cloud providers that allocates more virtual resources than available on physical hardware, which may imply service quality degradation. Docker in cloud computing environments is being increasingly used due to their fast provisioning and deployment, while the impact of overbooking of resources allocation due to multi-tenancy remains overlooked. This paper proposes a machine learning model to detect overbooking in Kubernetes environments within the docker container. The proposed model continuously monitors distributed container OS usage and application performance metrics. The collected metrics are used as input to a machine learning model that identifies multi-tenancy interference incurring in application performance degradation. Experiments performed on a Kubernetes cluster with a Docker-based Big Data processing application showed that our proposed model could detect resource overbooking with up to 98% accuracy. This implies an overbooking on a resource of up to 1.2 in the client’s domain.