Failure-aware application placement modeling and optimization in high turnover devops environment

Abstract

DevOps (software DEVelopment and information technology OPerationS) has established a culture and environment, in which building, testing, and releasing software happen more rapidly, frequently, and reliably through automated pipelines. To support this high turnover application cycle, the cluster orchestration frameworks such as Kubernetes or Docker Swarm have evolved to provide high flexibility and reliability. However, while cluster orchestrators run on the cloud infrastructure, any failure incurred from the infrastructure can directly impact the nodes of a cluster, so infrastructure failures can disrupt applications running on the cluster. In this paper, we propose proactive application placement algorithms with prediction of infrastructure failures. The proposed algorithms utilize failure-risk measurements, Failure-Index, determined from turnover rate of applications and prediction of infrastructure failures. We build stochastic models for application turnover and infrastructure failure processes, and provide various types of Failure-Index. Our placement algorithms are implemented in an orchestration framework, Kubernetes, and in the simulation model. Experimental results show that our methods reduce the amount of application disruption by 20% than the state-of-the-art algorithms in Kubernetes.