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What can multi-cloud configuration learn from AutoML?

Multi-cloud computing has become increasingly popular with enterprises looking to avoid vendor lock-in. While most cloud providers offer similar functionality, they may differ significantly in terms of performance and/or cost. A customer looking to benefit from such differences will naturally want to solve the multi-cloud configuration problem: given a workload, which cloud provider should be chosen and how should its nodes be configured in order to minimize runtime or cost? In this work, we consider this multi-cloud optimization problem and publish a new offline benchmark dataset, MOCCA, comprising 60 different multi-cloud configuration tasks across 3 public cloud providers, to enable further research in this area. Furthermore, we identify an analogy between multi-cloud configuration and the selection-configuration problems that are commonly studied in the automated machine learning (AutoML) field. Inspired by this connection, we propose an algorithm for solving multi-cloud configuration, CloudBandit (CB). It treats the outer problem of cloud provider selection as a best-arm identification problem, in which each arm pull corresponds to running an arbitrary black-box optimizer on the inner problem of node configuration. Extensive experiments on MOCCA indicate that CB achieves (a) significantly lower regret relative to its component black-box optimizers and (b) competitive or lower regret relative to state-of-the-art AutoML methods, whilst also being cheaper and faster.

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