ACM Transactions on Computer Systems (TOCS)
The UltraSAN modeling environment
Performance Evaluation - Special issue: performance modeling tools
Generalized Stochastic Petri Nets: A Definition at the Net Level and its Implications
IEEE Transactions on Software Engineering
A Characterization of the Stochastic Process Underlying a Stochastic Petri Net
IEEE Transactions on Software Engineering
PNPM '87 The Proceedings of the Second International Workshop on Petri Nets and Performance Models
Stochastic Activity Networks: Structure, Behavior, and Application
International Workshop on Timed Petri Nets
Well-defined stochastic Petri nets
MASCOTS '96 Proceedings of the 4th International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Construction and solution of performability models based on stochastic activity networks
Construction and solution of performability models based on stochastic activity networks
Stochastic activity networks: formal definitions and concepts
Lectures on formal methods and performance analysis
Well-Defined Generalized Stochastic Petri Nets: A Net-Level Method to Specify Priorities
IEEE Transactions on Software Engineering
A new disk-based technique for solving the largeness problem of stochastic modeling formalisms
Journal of Systems and Software
A set of performance and dependability analysis components for CADP
TACAS'03 Proceedings of the 9th international conference on Tools and algorithms for the construction and analysis of systems
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Stochastic Petri nets (SPNs) and extensions are a popular method for evaluating a wide variety of systems. In most cases, their numerical solution requires generating a state-level stochastic process, which captures the behavior of the SPN with respect to a set of specified performance measures. These measures are commonly defined at the net level by means of a reward variable. In this paper, we discuss issues regarding the generation of state-level reward models for systems specified as stochastic activity networks (SANs) with "step-based reward structures." Step-based reward structures are a generalization of previously proposed reward structures for SPNs and can represent all reward variables that can be defined on the marking behavior of a net. While discussing issues related to the generation of the underlying state-level reward model, we provide an algorithm to determine whether a given SAN is "well specified." A SAN is well specified if choices about which instantaneous activity completes among multiple simultaneously enabled instantaneous activities do not matter, with respect to the probability of reaching next possible stable markings and the distribution of reward obtained upon completion of a timed activity. The fact that a SAN is well specified is both a necessary and sufficient condition for its behavior to be completely probabilistically specified and hence is an important property to determine.