Fluid semantics for passive stochastic process algebra cooperation

Fluid modelling is a next-generation technique for analysing massive performance models. Passive cooperation is a popular cooperation mechanism frequently used by performance engineers. Therefore having an accurate translation of passive cooperation into a fluid model is of direct practical application. We compare different existing styles of fluid model translation of passive cooperation in a stochastic process algebra. We explain why the development of a fluid semantics for passive cooperation is not straightforward and we present an alternative definition which more closely matches the underlying discrete model. Finally, we present quantitative comparisons with a previous version of the fluid semantics in which numerical discrepancies can be observed. © 2008 ICST ISBN

Evaluating fluid semantics for passive stochastic process algebra cooperation

Fluid modelling is a next-generation technique for analysing massive performance models. Passive cooperation is a popular cooperation mechanism frequently used by performance engineers. Therefore having an accurate translation of passive cooperation into a fluid model is of direct practical application. We compare different existing styles of fluid model translation of passive cooperation in a stochastic process algebra and show how the previous model can be improved upon significantly. We evaluate the new passive cooperation fluid semantics and show that the first-order fluid model is a good approximation to the dynamics of the underlying continuous-time Markov chain. We show that in a family of possible translations to the fluid model, there is an optimal translation which can be expected to introduce least error. Finally, we use these new techniques to show how the scalability of a passively-cooperating distributed software architecture could be assessed

Shared Transaction Markov Chains for Fluid Analysis of Massively Parallel Systems

Published versio

Hypergraph-based parallel computation of passage time densities in large semi-Markov models

AbstractPassage time densities and quantiles are important performance and quality of service metrics, but their numerical derivation is, in general, computationally expensive. We present an iterative algorithm for the calculation of passage time densities in semi-Markov models, along with a theoretical analysis and empirical measurement of its convergence behaviour. In order to implement the algorithm efficiently in parallel, we use hypergraph partitioning to minimise communication between processors and to balance workloads. This enables the analysis of models with very large state spaces which could not be held within the memory of a single machine. We produce passage time densities and quantiles for very large semi-Markov models with over 15 million states and validate the results against simulation

Performance queries on Semi-Markov Stochastic Petri nets with an extended continuous Stochastic logic

Semi-Markov Stochastic Petri Nets (SM-SPNs) are a highlevel formalism for defining semi-Markov processes. We present an extended Continuous Stochastic Logic (eCSL) which provides an expressive way to articulate performance queries at the SM-SPN model level. eCSL supports queries involving steady-state, transient and passage time measures. We demonstrate this by formulating and answering eCSL queries on an SM-SPN model of a distributed voting system with up to ¢¤£¦ ¥ states.

Derivation of passage-time densities in PEPA models using ipc: the imperial PEPA compiler

We present a technique for defining and extracting passage-time densities from high-level stochastic process algebra models. Our high-level formalism is PEPA, a popular Markovian process algebra for expressing compositional performance models. We introduce ipc, a tool which can process PEPA-specified passage-time densities and models by compiling the PEPA model and passage specification into the DNAmaca formalism. DNAmaca is an established modelling language for the low-level specification of very large Markov and semi-Markov chains. We provide performance results for ipc/DNAmaca and comparisons with another tool which supports PEPA, PRISM. Finally, we generate passage-time densities and quantiles for a case study of a high-availability web server. 1

The emotional movie database (EMDB): a self-report and psychophysiological study

Film clips are an important tool for evoking emotional responses in the laboratory. When compared with other emotionally potent visual stimuli (e.g., pictures), film clips seem to be more effective in eliciting emotions for longer periods of time at both the subjective and physiological levels. The main objective of the present study was to develop a new database of affective film clips without auditory content, based on a dimensional approach to emotional stimuli (valence, arousal and dominance). The study had three different phases: (1) the pre-selection and editing of 52 film clips (2) the self-report rating of these film clips by a sample of 113 participants and (3) psychophysiological assessment [skin conductance level (SCL) and the heart rate (HR)] on 32 volunteers. Film clips from different categories were selected to elicit emotional states from different quadrants of affective space. The results also showed that sustained exposure to the affective film clips resulted in a pattern of a SCL increase and HR deceleration in high arousal conditions (i.e., horror and erotic conditions). The resulting emotional movie database can reliably be used in research requiring the presentation of non-auditory film clips with different ratings of valence, arousal and dominance.Portuguese Foundation for Science and Technology with individual grants (SFRH/BD/41484/2007 and SFRH/BD/64355/2009