Specification Techniques for Markov Reward Models

Markov reward models (MRMs) are commonly used for the performance, dependability, and performability analysis of computer and communication systems. Many papers have addressed solution techniques for MRMs. Far less attention has been paid to the specification of MRMs and the subsequent derivation of the underlying MRM. In this paper we only briefly address the mathematical aspects of MRMs. Instead, emphasis is put on specification techniques. In an application independent way, we distinguish seven classes of specification techniques: stochastic Petri nets, queuing networks, fault trees, production rule systems, communicating processes, specialized languages, and hybrid techniques. For these seven classes, we discuss the main principles, give examples and discuss software tools that support the use of these techniques. An overview like this has not been presented in the literature before. Finally, the paper addresses the generation of the underiying MRM from the high-level specification, and indicates important future research areas.