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In this article, we consider the dynamic allocation of bursty requests stochastically arriving at heterogeneous servers with uncertain setup times. Lower expected response time and less power consumption are desirable objectives of users and service providers respectively. However, sudden increase and decrease of cloud servers caused by bursty requests are rather challenging to get an appropriate trade-off between the two conflicting objectives which are closely related to the launched servers. The heterogeneity of the cloud servers further makes it more difficult to decide how to switch on and off servers and effectively and efficiently allocate bursty requests with balanced objectives. Based on a Markov decision process, a real-time bilevel decision-making model is constructed for unallocated requests which includes: whether to launch a server and which type of server to launch. A learn-and-deploy algorithm …