  Due to the growing complexity of modern data centers, failures are not
uncommon any more. Therefore, fault tolerance mechanisms play a vital role in
fulfilling the availability requirements. Multiple availability models have
been proposed to assess compute systems, among which Bayesian network models
have gained popularity in industry and research due to its powerful modeling
formalism. In particular, this work focuses on assessing the availability of
redundant and replicated cloud computing services with Bayesian networks. So
far, research on availability has only focused on modeling either
infrastructure or communication failures in Bayesian networks, but have not
considered both simultaneously. This work addresses practical modeling
challenges of assessing the availability of large-scale redundant and
replicated services with Bayesian networks, including cascading and
common-cause failures from the surrounding infrastructure and communication
network. In order to ease the modeling task, this paper introduces a high-level
modeling formalism to build such a Bayesian network automatically. Performance
evaluations demonstrate the feasibility of the presented Bayesian network
approach to assess the availability of large-scale redundant and replicated
services. This model is not only applicable in the domain of cloud computing it
can also be applied for general cases of local and geo-distributed systems.
