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In this paper, we present PerfEnforce, a scaling engine designed to enablecloud providers to sell performance levels for data analytics cloud services.PerfEnforce scales a cluster of virtual machines allocated to a user in a waythat minimizes cost while probabilistically meeting the query runtimeguarantees offered by a service level agreement. With PerfEnforce, we show howto scale a cluster in a way that minimally disrupts a user's query session. Wefurther show when to scale the cluster using one of three methods: feedbackcontrol, reinforcement learning, or perceptron learning. We find thatperceptron learning outperforms the other two methods when making clusterscaling decisions.

PerfEnforce: A Dynamic Scaling Engine for Analytics with Performance Guarantees