Hybrid performance modelling of opportunistic networks

Abstract

We demonstrate the modelling of opportunistic networks using the process algebra stochastic HYPE. Network traffic is modelled as continuous flows, contact between nodes in the network is modelled stochastically, and instantaneous decisions are modelled as discrete events. Our model describes a network of stationary video sensors with a mobile ferry which collects data from the sensors and delivers it to the base station. We consider different mobility models and different buffer sizes for the ferries. This case study illustrates the flexibility and expressive power of stochastic HYPE. We also discuss the software that enables us to describe stochastic HYPE models and simulate them

Quantitative Aspects of Programming Languages and Systems over the past 2^4 years and beyond

“…Such studies go back to the early 1990s and reached their full maturity in the 2000s, where it is easy to find an amount of extensions and variants of both qualitative and quantitative languages. In the setting of QAPL, we can mention several such examples: probabilistic variant of the process-algebraic µCRL language [90], discrete time variant of distributed πcalculus [48], probabilistic extension of π-calculus [117], interleaving semantics and true concurrent semantics for the probabilistic variant of π-calculus [142], stochastic broadcast π-calculus [131], stochastic version of Mobile Ambient [143], stochastic extension of the hybrid process algebra HYPE [32,33,68], stochastic extension of the Software Component Ensemble Language for modeling ensemble based autonomous systems [101], Linda-like coordination calculus extended with quantitative information [38], and finally a mixture of concurrent and probabilistic Kleene algebras enriched with probabilistic choices [110]. Further examples include process calculi for performance evaluation, like LYSA [28], proposed for the context of cryptographic protocols, CARMA [31], specifically defined for collective adaptive systems, MELA [107], for modeling in ecology with location attributes, and PEPA Queues [13], introduced for the modeling of queueing networks with mobility features.…”

Hybrid semantics for Bio-PEPA

“…The influence is positive or negative depending on the role of S i in the reaction. To simplify presentation, instead of using I[W] and defining I[W] separately, the function I[W] will appear explicitly in influences 8 . The subcomponents can react to two events: one which describes when the reaction becomes treated deterministically which has the same influence as the influence for the initial event and one which describes when the reaction becomes treated stochastically.…”

“…The output of a single simulation run using these thresholds are given in Figure 7. All simulations of the HYPE model were generated by the stochastic hybrid simulator described in [8].…”

U-Check: Model Checking and Parameter Synthesis Under Uncertainty

“…This can take one of three values: ssa for Gillespie's stochastic simulation algorithm, odes for mean-field approximation, and hybrid. The odes and the hybrid simulation are based on the implementation of SimHyA [5], and they support SimHyA models only. Moreover, the mean-field approximation by ODEs can only be used for robust parameter synthesis.…”

“…GPs are at the core of several novel developments in formal analysis [8,7,2,9,3,18]; here we focus on three particular tasks: estimating the parametric dependence of the truth probability of a linear temporal logic formula; synthesising parameters from logical constraints on trajectories, and identifying parameters that maximise the robustness (quantitative satisfaction score [13,2]) of a formula. Our tools are based on Java and interface with popular formal modelling programming languages such as PRISM [17] and Bio-PEPA [11]; we also o↵er support for hybrid models specified in the SimHyA modelling language (for stochastic hybrid systems) [5]. U-check is available to download at https://github.com/dmilios/U-check.…”