Revisiting Interactive Markov Chains

Abstract The usage of process algebras for the performance modeling and evaluation of concurrent systems turned out to be convenient due to their feature of compositionality. A particularly simple and elegant solution in this field is the calculus of Interactive Markov Chains (IMCs), where the behavior of processes is just represented by Continuous Time Markov Chains extended with action transitions representing process interaction. The main advantage of IMCs with respect to other existing approaches is that a notion of bisimulation which abstracts from τ-transitions ("complete" interactions) can be defined which is a congruence. However in the original definition of the calculus of IMCs the high potentiality of compositionally minimizing the system state space given by the usage of a "weak" notion of equivalence and the elegance of the approach is somehow limited by the fact that the equivalence adopted over action transitions is a finer variant of Milner's observational congruence that distinguishes τ-divergent "Zeno" processes from non-divergent ones. In this paper we show that it is possible to reformulate the calculus of IMCs in such a way that we can just rely on simple standard observational congruence. Moreover we show that the new calculus is the first Markovian process algebra allowing for a new notion of Markovian bisimulation equivalence which is coarser than the standard one

Process Algebras

Process Algebras are mathematically rigorous languages with well defined semantics that permit describing and verifying properties of concurrent communicating systems. They can be seen as models of processes, regarded as agents that act and interact continuously with other similar agents and with their common environment. The agents may be real-world objects (even people), or they may be artifacts, embodied perhaps in computer hardware or software systems. Many different approaches (operational, denotational, algebraic) are taken for describing the meaning of processes. However, the operational approach is the reference one. By relying on the so called Structural Operational Semantics (SOS), labelled transition systems are built and composed by using the different operators of the many different process algebras. Behavioral equivalences are used to abstract from unwanted details and identify those systems that react similarly to external experiments

A uniform framework for modelling nondeterministic, probabilistic, stochastic, or mixed processes and their behavioral equivalences

Labeled transition systems are typically used as behavioral models of concurrent processes, and the labeled transitions define the a one-step state-to-state reachability relation. This model can be made generalized by modifying the transition relation to associate a state reachability distribution, rather than a single target state, with any pair of source state and transition label. The state reachability distribution becomes a function mapping each possible target state to a value that expresses the degree of one-step reachability of that state. Values are taken from a preordered set equipped with a minimum that denotes unreachability. By selecting suitable preordered sets, the resulting model, called ULTraS from Uniform Labeled Transition System, can be specialized to capture well-known models of fully nondeterministic processes (LTS), fully probabilistic processes (ADTMC), fully stochastic processes (ACTMC), and of nondeterministic and probabilistic (MDP) or nondeterministic and stochastic (CTMDP) processes. This uniform treatment of different behavioral models extends to behavioral equivalences. These can be defined on ULTraS by relying on appropriate measure functions that expresses the degree of reachability of a set of states when performing single-step or multi-step computations. It is shown that the specializations of bisimulation, trace, and testing equivalences for the different classes of ULTraS coincide with the behavioral equivalences defined in the literature over traditional models