A compositional approach to performance modelling
A compositional approach to performance modelling
Communicating and mobile systems: the &pgr;-calculus
Communicating and mobile systems: the &pgr;-calculus
Fluid Flow Approximation of PEPA models
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
A Generic Mean Field Convergence Result for Systems of Interacting Objects
QEST '07 Proceedings of the Fourth International Conference on Quantitative Evaluation of Systems
Analysis of On-off policies in Sensor Networks Using Interacting Markovian Agents
PERCOM '08 Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
Analysis of Large Scale Interacting Systems by Mean Field Method
QEST '08 Proceedings of the 2008 Fifth International Conference on Quantitative Evaluation of Systems
On the Approximation of Stochastic Concurrent Constraint Programming by Master Equation
Electronic Notes in Theoretical Computer Science (ENTCS)
Modelling Network Performance with a Spatial Stochastic Process Algebra
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
A fluid analysis framework for a Markovian process algebra
Theoretical Computer Science
A Markovian agent model for fire propagation in outdoor environments
EPEW'10 Proceedings of the 7th European performance engineering conference on Computer performance engineering
Fluid computation of passage-time distributions in large Markov models
Theoretical Computer Science
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We introduce a spatial stochastic process algebra called MASSPA, which provides a formal behavioural description of Markovian Agent Models, a spatial stochastic modelling framework. We provide a translation to a master equation which governs the underlying transition behaviour. This provides a means of simulation and thus comparison of numerical results with simulation that was previously not available. On the theoretical side, we develop a higher moment analysis to allow quantities such as variance to be produced for spatial stochastic models in performance analysis for the first time. We compare the simulation results against resulting ODEs for both mean and standard deviations of model component counts and finish by analysing a distributed wireless sensor network model.