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Kubernetes, the prevalent container orchestrator for cloud-deployed web applications, offers an automatic scaling feature for the application provider in order to meet the ever-changing amount of demand from its clients. This auto-scaling service, however, requires a seemingly difficult parameter set to be customized by the application provider, and those management parameters are static while incoming web request dynamics often change, not to mention the fact that scaling decisions are inherently reactive, instead of being proactive. Therefore we set the ultimate goal of making cloud-based web applications' management easier and more effective. We propose a Kubernetes scaling engine that makes the auto-scaling decisions apt for handling the actual variability of incoming requests. In this engine various AI-based forecast methods compete with each other via a short-term evaluation loop in order to always …