Autoscaling is a technology to automatically scale the resources provided to their applications without human intervention to guarantee runtime Quality of Service (QoS) while saving costs. However, user-facing cloud applications serve dynamic workloads that often exhibit variable and contain bursts, posing challenges to autoscaling for maintaining QoS within Service-Level Objectives (SLOs). Conservative strategies risk over-provisioning, while aggressive ones may cause SLO violations, making it more challenging to design effective autoscaling. This paper introduces BAScaler, a Burst-Aware Autoscaling framework for containerized cloud services or applications under complex workloads, combining multi-level machine learning (ML) techniques to mitigate SLO violations while saving costs. BAScaler incorporates a novel prediction-based burst detection mechanism that distinguishes between predictable periodic workload spikes and actual bursts. When bursts are detected, BAScaler appropriately overestimates them and allocates resources accordingly to address the rapid growth in resource demand. On the other hand, BAScaler employs reinforcement learning to rectify potential inaccuracies in resource estimation, enabling more precise resource allocation during non-bursts. Experiments across ten real-world workloads demonstrate BAScaler's effectiveness, achieving a 57% average reduction in SLO violations and cutting resource costs by 10% compared to other prominent methods.

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