  Performing diagnostics in IT systems is an increasingly complicated task, and
it is not doable in satisfactory time by even the most skillful operators.
Systems and their architecture change very rapidly in response to business and
user demand. Many organizations see value in the maintenance and management
model of NoOps that stands for No Operations. One of the implementations of
this model is a system that is maintained automatically without any human
intervention. The path to NoOps involves not only precise and fast diagnostics
but also reusing as much knowledge as possible after the system is reconfigured
or changed. The biggest challenge is to leverage knowledge on one IT system and
reuse this knowledge for diagnostics of another, different system. We propose a
framework of weighted graphs which can transfer knowledge, and perform
high-quality diagnostics of IT systems. We encode all possible data in a graph
representation of a system state and automatically calculate weights of these
graphs. Then, thanks to the evaluation of similarity between graphs, we
transfer knowledge about failures from one system to another and use it for
diagnostics. We successfully evaluate the proposed approach on Spark, Hadoop,
Kafka and Cassandra systems.
