The Markovian description of a system requires one to specify all possible states that the system can occupy and transitions among them. Most of the time, the complexity of the underlying system implies a very large state space. Sometimes, it is even difficult to enumerate all possible states and generate the transitions in the system. Even if we are able to do so, the size of the system remains a major obstacle in performance analysis.
SANs provide a methodology for modeling large systems with interacting components.
The main idea is to decompose the system of interest into its components and to model each component separately. Once this is done, interactions and dependencies among components can be brought into the picture and the model finalized. With this decompositional approach, the global system ends up having as many states as the product of the number of states of the individual components. The benefit of the SAN approach is twofold. First, each component can be modeled much easier compared to the global system due to state space reduction.