Quality of service, QoS, has a great impact on wider adoption of cloud computing. Maintaining the QoS at an acceptable level for cloud users requires an accurate and well adapted performance analysis approach. In this paper, we describe a new approximate analytical model for performance evaluation of cloud server farms under burst arrivals and solve it to obtain important performance indicators such as mean request response time, blocking probability, probability of immediate service and probability distribution of number of tasks in the system. This model allows cloud operators to tune the parameters such as the number of servers and/or burst size, on one side, and the values of blocking probability and probability that a task request will obtain immediate service, on the other.
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