Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model

Abstract

The resource usage behaviors of application workloads are currently the primary concern of cloud providers offering hosting services. These services should be able to adapt to workload changes by automatically provisioning and de-provisioning resources so that, at all times, the existing resources in a system match the current service demand. Such behavior can be achieved manually by hiring a DevOps team to manage the application’s resources. Another option would be automating the resource provisioning processing using automated rules. Once such rules are met, the hosting environment will scale the resources accordingly. However, managing a DevOps team or creating flaky rules can lead to over-scaling application resources. This work proposes a new approach: a proactive auto-scaling framework built on an ensemble model. Such a model utilizes several machine learning techniques to scale application resources to match resource demand before the need arises. We evaluated our solution against three real production applications hosted on Cegedim Cloud Hosting Environment, an industrial environment serving several cloud applications from various domains, and against other machine learning models used in similar proactive auto-scaling experiments mentioned in past work. The experimentation results show that predicting application resources like CPU or RAM is feasible. Moreover, even in production environments, our ensemble model performs optimally in the CPU case and is near the optimal model when predicting RAM resources.

Keywords

• Auto-scaling
• resource allocation
• dynamic resource provisioning
• resource management on clouds