Making life better one large system at a time: Challenges for UAI research
This addresses management challenges in complex IT ecosystems for practitioners, but is incremental as it applies existing UAI methods to a new domain.
The paper argues that Uncertainty in Artificial Intelligence (UAI) research is well-suited to address challenges in managing complex IT ecosystems by transforming data into actionable information for decision-making, supporting this with examples from diagnosis, model discovery, and policy optimization on three real distributed systems.
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.