  Performance analysis based on modelling consists of two major steps: model
construction and model analysis. Formal modelling techniques significantly aid
model construction but can exacerbate model analysis. In particular, here we
consider the analysis of large-scale systems which consist of one or more
entities replicated many times to form large populations. The replication of
entities in such models can cause their state spaces to grow exponentially to
the extent that their exact stochastic analysis becomes computationally
expensive or even infeasible.
  In this paper, we propose a new approximate aggregation algorithm for a class
of large-scale PEPA models. For a given model, the method quickly checks if it
satisfies a syntactic condition, indicating that the model may be solved
approximately with high accuracy. If so, an aggregated CTMC is generated
directly from the model description. This CTMC can be used for efficient
derivation of an approximate marginal probability distribution over some of the
model's populations. In the context of a large-scale client-server system, we
demonstrate the usefulness of our method.
