Activities per year
Over the last decade considerable effort has been put in the development of techniques to assess the performance and the dependability of computer and communication systems in an integrated way. This so-called performability modelling becomes especially useful when the system under study can operate partially, which is for instance the case for fault-tolerant computer systems and distributed systems. Modelling techniques are a fundamental prerequisite for actually doing performability analysis. A prerequisite of a more practical but not less important nature is the availability of software tools to support the modelling techniques and to allow system designers to incorporate the new techniques in the design process of systems. Since performability modelling requires many aspects of a system to be specified, high requirements should be posed on performability modelling tools. Moreover, these tools should be structured such that the models can be specified at a level that is easy to understand for a system designer, and that the mathematical aspects are hidden as much as possible. The output of the tool should also be such that it can be understood with only limited knowledge of the underlying mathematical model. We have developed a new, fairly general modelling tool framework that can be used as a guide to assess the usability and structure of performability modelling tools. After briefly reviewing the mathematical aspects of performability modelling we discuss this framework. We then discuss 12 recently developed tools (Metaphor, Numas, Metasan, Metfac, Save, Sharpe, SPNP, Tangram, Penpet, UltraSAN, Surf-2, DyQNtool+) that can all be used for some aspects of performability modelling and analysis. We assess among other things their structure, their capabilities in terms of measures that can be obtained, and the used modelling formalism. We also discuss directions for future work in the field of performability modelling tools.
- General modelling tool framework
- Markov reward models
- Queueing networks
- Stochastic Petri nets