  The rapid growth and diversity in service offerings and the ensuing
complexity of information technology ecosystems present numerous management
challenges (both operational and strategic). Instrumentation and measurement
technology is, by and large, keeping pace with this development and growth.
However, the algorithms, tools, and technology required to transform the data
into relevant information for decision making are not. The claim in this paper
(and the invited talk) is that the line of research conducted in Uncertainty in
Artificial Intelligence is very well suited to address the challenges and close
this gap. I will support this claim and discuss open problems using recent
examples in diagnosis, model discovery, and policy optimization on three real
life distributed systems.
