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.