Abstract

Layered queueing network (LQN) models are effective for large systems but lack the capability to model some kinds of decision logic that may be important for system performance. This paper describes the use of a "complementary model" which focuses on the decision logic, and which provides the results of the logic in a form that the LQN can use. The complementary model is constructed from the same base information as the LQN but system elements away from the focal point are approximated in reduced detail, by aggregating them. The complementary model is thus made consistent with the LQN. The two models together form a "hybrid" multi-formalism model, which is solved by a fixed point iteration. The approach is described through an example which uses Stochastic Petri Nets for the complementary submodel.

-- Daniel Amyot - 05 Jul 2010

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