The Power of Machine Learning and Market Design for Cloud Computing Admission Control
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Cloud computing providers must handle customer workloads that wish to scale their use of resources such as virtual machines up and down over time. Currently, this is often done using simple threshold policies to reserve large parts of each cluster. This leads to low utilization of the cluster on average. In this paper, we propose more sophisticated policies for controlling admission to a cluster and demonstrate that our policies significantly increase cluster utilization. We first introduce a model and fit its parameters on a data trace from Microsoft Azure. We then design policies that estimate moments of each workload's distribution of future resource usage. Via simulations we show that, while estimating the first moments of workloads leads to a substantial improvement over the simple threshold policy, also taking the second moments into account yields another improvement in utilization. We then evaluate how much further this can be improved with learned or elicited prior information and how to incentivize users to provide this information.
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