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To improve customer experience, datacenter operators offer support forsimplifying application and resource management. For example, running workloadsof workflows on behalf of customers is desirable, but requires increasinglymore sophisticated autoscaling policies, that is, policies that dynamicallyprovision resources for the customer. Although selecting and tuning autoscalingpolicies is a challenging task for datacenter operators, so far relatively fewstudies investigate the performance of autoscaling for workloads of workflows.Complementing previous knowledge, in this work we propose the firstcomprehensive performance study in the field. Using trace-based simulation, wecompare state-of-the-art autoscaling policies across multiple applicationdomains, workload arrival patterns (e.g., burstiness), and system utilizationlevels. We further investigate the interplay between autoscaling and regularallocation policies, and the complexity cost of autoscaling. Our quantitativestudy focuses not only on traditional performance metrics and onstate-of-the-art elasticity metrics, but also on time- and memory-relatedautoscaling-complexity metrics. Our main results give strong and quantitativeevidence about previously unreported operational behavior, for example, thatautoscaling policies perform differently across application domains and by howmuch they differ.

Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters