  User demand on the computational resources of cloud computing platforms
varies over time. These variations in demand can be predictable or
unpredictable, resulting in `bursty' fluctuations in demand. Furthermore,
demand can arrive in batches, and users whose demands are not met can be
impatient. We demonstrate how to compute the expected revenue loss over a
finite time horizon in the presence of all these model characteristics through
the use of matrix analytic methods. We then illustrate how to use this
knowledge to make frequent short term provisioning decisions --- transient
provisioning. It is seen that taking each of the characteristics of fluctuating
user demand (predictable, unpredictable, batchy) into account can result in a
substantial reduction of losses. Moreover, our transient provisioning framework
allows for a wide variety of system behaviors to be modeled and gives simple
expressions for expected revenue loss which are straightforward to evaluate
numerically.
