SRMC: Sensoria Reference Markovian Calculus
SRMC is a process calculus which allows modellers to make high-level descriptions of computer systems where there is some uncertainty about which of a set of components will be used. Such uncertainty arises naturally in service-oriented computing where it is not known to which service instance a service consumer will choose to bind. This kind of uncertainty also arises when evaluating two or more candidate designs for a component. Either might be usable but which will give better performance when in use?
Using a compact and intuitive process calculus language, SRMC allows modellers to document this kind of uncertainty formally. The SRMC software investigates this uncertainty by evaluating all of the possible configurations which can be reached, generating Markov chain representations for each one.
SRMC scales to support "real-world" examples by distributing the numerical solution of many representative Markov chains across a network of workstations. Modellers can perform parameter sweep by varying rates across their models to discover where tuning the performance of a component is most likely to bring the best return for the effort invested.