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Autoscaling is a vital mechanism in cloud computing that supports theautonomous adjustment of computing resources under dynamic workloads. A primarygoal of autoscaling is to stabilize resource utilization at a desirable level,thus reconciling the need for resource-saving with the satisfaction of ServiceLevel Objectives (SLOs). Existing proactive autoscaling methods anticipate thefuture workload and scale the resources in advance, whereas the reliability maysuffer from prediction deviations arising from the frequent fluctuations andnoise of cloud workloads; reactive methods rely on real-time system feedback,while the hysteretic nature of reactive methods could cause violations of therigorous SLOs. To this end, this paper presents OptScaler, a hybrid autoscalingframework that integrates the power of both proactive and reactive methods forregulating CPU utilization. Specifically, the proactive module of OptScalerconsists of a sophisticated workload prediction model and an optimizationmodel, where the former provides reliable inputs to the latter for makingoptimal scaling decisions. The reactive module provides a self-tuning estimatorof CPU utilization to the optimization model. We embed Model Predictive Control(MPC) mechanism and robust optimization techniques into the optimization modelto further enhance its reliability. Numerical results have demonstrated thesuperiority of both the workload prediction model and the hybrid framework ofOptScaler in the scenario of online services compared to prevalent reactive,proactive, or hybrid autoscalers. OptScaler has been successfully deployed atAlipay, supporting the autoscaling of applets in the world-leading paymentplatform.

OptScaler: A Hybrid Proactive-Reactive Framework for Robust Autoscaling in the Cloud