  Infrastructure as a service clouds hide the complexity of maintaining the
physical infrastructure with a slight disadvantage: they also hide their
internal working details. Should users need knowledge about these details e.g.,
to increase the reliability or performance of their applications, they would
need solutions to detect behavioural changes in the underlying system. Existing
runtime solutions for such purposes offer limited capabilities as they are
mostly restricted to revealing weekly or yearly behavioural periodicity in the
infrastructure. This article proposes a technique for predicting generic
background workload by means of simulations that are capable of providing
additional knowledge of the underlying private cloud systems in order to
support activities like cloud orchestration or workflow enactment. Our
technique uses long-running scientific workflows and their behaviour
discrepancies and tries to replicate these in a simulated cloud with known
(trace-based) workloads. We argue that the better we can mimic the current
discrepancies the better we can tell expected workloads in the near future on
the real life cloud. We evaluated the proposed prediction approach with a
biochemical application on both real and simulated cloud infrastructures. The
proposed algorithm has shown to produce significantly (~20%) better workload
predictions for the future of simulated clouds than random workload selection.
