  Infrastructure as a Service (IaaS) Cloud services allow users to deploy
distributed applications in a virtualized environment without having to
customize their applications to a specific Platform as a Service (PaaS) stack.
It is common practice to host multiple Virtual Machines (VMs) on the same
server to save resources. Traditionally, IaaS data center management required
manual effort for optimization, e.g. by consolidating VM placement based on
changes in usage patterns. Many resource management algorithms and frameworks
have been developed to automate this process. Resource management algorithms
are typically tested via experimentation or using simulation. The main drawback
of both approaches is the high effort required to conduct the testing. Existing
Cloud or IaaS simulators require the algorithm engineer to reimplement their
algorithm against the simulator's API. Furthermore, the engineer manually needs
to define the workload model used for algorithm testing. We propose an approach
for the simulative analysis of IaaS Cloud infrastructure that allows algorithm
engineers and data center operators to eval- uate optimization algorithms
without investing additional effort to reimplement them in a simulation
environment. By leveraging runtime monitoring data, we automatically construct
the simula- tion models used to test the algorithms. Our validation shows that
algorithm tests conducted using our IaaS Cloud simulator match the measured
behavior on actual hardware.
