Serverless computing platforms must preserve quality of service (QoS) while adapting to highly bursty and unpredictable workloads. To cope with workload fluctuations while satisfying QoS requirements and service-level agreements (SLAs), these platforms rely on automatic resource scaling. Commercial function-as-a-service (FaaS) platforms employ different autoscaling strategies to dynamically adjust the number of active function instances and the available processing capacity. In this work, three representative autoscaling strategies are modeled and compared: scale-per-request (SPR), concurrencylevel scaling (CLS), and metric-based scaling (MBS). To enable systematic evaluation independent of proprietary platform-specific implementations, we develop a unified modeling framework that combines analytical queueing network (QN) modeling with queueing Petri nets (QPN) implemented in Java Modeling Tools (JMT) to evaluate autoscaling behavior under two-phase hyper-exponential (H2) arrivals. Evaluation is conducted using four system-level metrics: throughput, mean response time, system population, and system power. The framework captures dynamic function instance (FI) activation, concurrency constraints, and queue buildup under saturation. Comparative analysis reveals a clear trade-off between peak performance and stability: SPR provides the lowest delay under moderate load but becomes fragile near capacity limits; CLS is governed by concurrency tokens and is highly sensitive to configuration under bursty conditions; whereas MBS provides the most stable QoS across the workload range by keeping utilization below a critical region and preventing explosive queue growth under critical load.

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