  Many enterprise environments have databases running on network-attached
server-storage infrastructure (referred to as Storage Area Networks or SANs).
Both the database and the SAN are complex systems that need their own separate
administrative teams. This paper puts forth the vision of an innovative
management framework to simplify administrative tasks that require an in-depth
understanding of both the database and the SAN. As a concrete instance, we
consider the task of diagnosing the slowdown in performance of a database query
that is executed multiple times (e.g., in a periodic report-generation
setting). This task is very challenging because the space of possible causes
includes problems specific to the database, problems specific to the SAN, and
problems that arise due to interactions between the two systems. In addition,
the monitoring data available from these systems can be noisy.
  We describe the design of DIADS which is an integrated diagnosis tool for
database and SAN administrators. DIADS generates and uses a powerful
abstraction called Annotated Plan Graphs (APGs) that ties together the
execution path of queries in the database and the SAN. Using an innovative
workflow that combines domain-specific knowledge with machine-learning
techniques, DIADS was applied successfully to diagnose query slowdowns caused
by complex combinations of events across a PostgreSQL database and a production
SAN.
