State-space support for path-based reward variables

Many sophisticated formalisms exist for specifying complex system behaviors , but methods for specifying performance and dependability variables have r emained quite primitive. To cope with this problem, modelers often must aug ment system models with extra state information and event types to support particular variables. This often leads to models that are nonintuitive, and must be changed to support different variables. To address this problem, w e extend the array of performance measures that may be derived from a given system model, by developing new performance measure specification and mode l construction techniques. Specifically, we introduce a class of path-based reward variables, and show how various performance measures may be specifi ed using these variables. Path-based reward variables extend the previous w ork with reward structures to allow rewards to be accumulated based on sequ ences of states and transitions. To maintain the relevant history, we intro duce the concept of a path automaton, whose state transitions are based on the system model state and transitions. Furthermore, we present a new proce dure for constructing state spaces and the associated transition rate matri ces that support path-based reward variables. Our new procedure takes advan tage of the path automaton to allow a single system model to be used as the basis of multiple performance measures that would otherwise require separa te models or a single more complicated model. (C) 1999 Published by Elsevie r Science B.V. All rights reserved.