Predictive Hybrid Autoscaling for Containerized Applications

Abstract

One of the main challenges in deploying container service is providing the scalability to satisfy the service performance and avoid resource wastage. To deal with this challenge, Kubernetes provides two kinds of scaling mode: vertical and horizontal. Several existing autoscaling methods make efforts to improve the default autoscalers in Kubernetes; however, most of these works only focus on one scaling mode at the same time, which results in some limitations. Only horizontal scaling may lead to low utilization of containers due to the fixed amount of resources for each instance, especially in the low-request period. In contrast, only vertical scaling may not ensure the quality of service (QoS) requirements in case of bursty workload due to reaching the upper limit. Besides, it is also necessary to provide burst identification for auto-scalers to guarantee service performance. This paper proposes a hybrid autoscaling method with burst awareness for containerized applications. This new approach considers a combination of both vertical and horizontal abilities to satisfy the QoS requirement while optimizing the utilization of containers. Our proposal uses a predictive method based on the machine learning technique to predict the future demand of the application and combines it with a burst identification module, which makes scaling decisions more effective. Experimental results show an enhancement in maintaining the response time below the QoS constraint whereas remaining high utilization of the deployment compared with existing baseline methods in single scaling mode.

Keywords

• Cloud computing
• Kubernetes
• autoscaling
• machine learning
• workload forecasting