  Cloud computing involves complex technical and economical systems and
interactions. This brings about various challenges, two of which are: (1)
debugging and control to optimize the performance of computing systems, with
the help of sandbox experiments, and (2) privacy-preserving prediction of the
cost of ``spot'' resources for decision making of cloud clients. In this paper,
we formalize debugging by counterfactual probabilities and control by
post-(soft-)interventional probabilities. We prove that counterfactuals can
approximately be calculated from a ``stochastic'' graphical causal model (while
they are originally defined only for ``deterministic'' functional causal
models), and based on this sketch a data-driven approach to address problem
(1). To address problem (2), we formalize bidding by post-(soft-)interventional
probabilities and present a simple mathematical result on approximate
integration of ``incomplete'' conditional probability distributions. We show
how this can be used by cloud clients to trade off privacy against
predictability of the outcome of their bidding actions in a toy scenario. We
report experiments on simulated and real data.
