In: Proc. 3-rd IEEE Annual Int. Computer Performance and Dependability Symposium (IPDS'98), 7-9 September 1998, Durham, NC, pages 228-237. 1998.
Also in: Performance Evaluation, Vol. 35, No. 3-4, pages 233-251. 1999.
Abstract: Many sophisticated formalisms exist for specifying complex system behaviors, but methods for specifying performance and dependability variables have remained quite primitive. To cope with this problem, modelers often must augment system models with extra state information and event types to support particular variables. This often leads to models which are non-intuitive, and must be changed to support different variables. To address this problem, this paper extends the array of performance measures that may be derived from a given system model by developing new performance measure specification and model construction techniques. Specifically, a class of path-based reward variables is introduced, and it is shown how various performance measures may be specified using these variables. Path-based regard variables extend the previous work with reward structures to allow rewards to be accumulated based on sequences of states and transitions. To maintain the relevant history, the concept of path automaton is introduced whose state transitions are based on the system model state and transitions. Furthermore, a new procedure for constructing state spaces and the associated transition rate matrix, that support path-based reward variables, are presented. The new procedure takes advantage of the path automaton to allow a single system model to be used as the basis of multiple performance measures that would otherwise separate models of a single more complicates model.
Keywords: Markov reward processes; path-based reward variables; state-space construction; stochastic Petri nets; stochastic activity networks.