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Multi-stage serverless applications, i.e., workflows with many computationand I/O stages, are becoming increasingly representative of FaaS platforms.Despite their advantages in terms of fine-grained scalability and modulardevelopment, these applications are subject to suboptimal performance, resourceinefficiency, and high costs to a larger degree than previous simple serverlessfunctions.  We present Aquatope, a QoS-and-uncertainty-aware resource scheduler forend-to-end serverless workflows that takes into account the inherentuncertainty present in FaaS platforms, and improves performance predictabilityand resource efficiency. Aquatope uses a set of scalable and validated Bayesianmodels to create pre-warmed containers ahead of function invocations, and toallocate appropriate resources at function granularity to meet a complexworkflow's end-to-end QoS, while minimizing resource cost. Across a diverse setof analytics and interactive multi-stage serverless workloads, Aquatopesignificantly outperforms prior systems, reducing QoS violations by 5x, andcost by 34% on average and up to 52% compared to other QoS-meeting methods.

QoS-Aware Resource Management for Multi-phase Serverless Workflows with Aquatope