Cloud
data center management is a key problem due to the numerous and heterogeneous
strategies that can be applied, ranging from the VM placement to the federation
with other clouds. Performance evaluation of cloud computing infrastructures is
required to predict and quantify the cost-benefit of a strategy portfolio and
the corresponding quality of service (QoS) experienced by users. Such analyses
are not feasible by simulation or on-the-field experimentation, due to the
great number of parameters that have to be investigated. In this paper, we
present an analytical model, based on stochastic reward nets (SRNs), that is
both scalable to model systems composed of thousands of resources and flexible
to represent different policies and cloud-specific strategies. Several
performance metrics are defined and evaluated to analyze the behavior of a
cloud data center: utilization, availability, waiting time, and responsiveness.
A resiliency analysis is also provided to take into account load bursts.
Finally, a general approach is presented that, starting from the concept of
system capacity, can help system managers to opportunely set the data center
parameters under different working conditions.
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