  We propose a novel approach to select privacy-sensitive IaaS providers for a
long-term period. The proposed approach leverages a consumer's short-term trial
experiences for long-term selection. We design a novel equivalence partitioning
based trial strategy to discover the temporal and unknown QoS performance
variability of an IaaS provider. The consumer's long-term workloads are
partitioned into multiple Virtual Machines in the short-term trial. We propose
a performance fingerprint matching approach to ascertain the confidence of the
consumer's trial experience. A trial experience transformation method is
proposed to estimate the actual long-term performance of the provider.
Experimental results with real-world datasets demonstrate the efficiency of the
proposed approach.
