6 research outputs found
Data as processes: introducing measurement data into CARMA models
Measurement data provides a precise and detailed description of components
within a complex system but it is rarely used directly as a component of a
system model. In this paper we introduce a model-based representation of
measurement data and use it together with modeller-defined components expressed
in the CARMA modelling language. We assess both liveness and safety properties
of these models with embedded data.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200
Automatic Moment-Closure Approximation of Spatially Distributed Collective Adaptive Systems
Spatially distributed collective adaptive systems are an important class of systems that pose significant challenges to modeling due to the size and complexity of their state spaces. This problem is acute when the dynamic behavior of the system must be captured, such as to predict system performance. In this article, we present an abstraction technique that automatically derives a moment-closure approximation of the dynamic behavior of a spatially distributed collective adaptive system from a discrete representation of the entities involved. The moment-closure technique is demonstrated to give accurate estimates of dynamic behavior, although the number of ordinary differential equations generated for the second-order joint moments can grow large in some cases. For these cases, we propose a rigorous model reduction technique and demonstrate its use to substantially reduce the computational effort with only limited impact on the accuracy if the reduction threshold is set appropriately. All techniques reported in this article are implemented in a tool that is freely available for download
Process algebra for located Markovian agents and scalable analysis techniques for the modelling of Collective Adaptive Systems
Recent advances in information and communications technology have led to a surge
in the popularity of artificial Collective Adaptive Systems (CAS). Such systems, comprised
by many spatially distributed autonomous entities with decentralised control,
can often achieve discernible characteristics at the global level; a phenomenon sometimes
termed emergence. Examples include smart transport systems, smart electricity
power grids, robot swarms, etc. The design and operational management of CAS are
of vital importance because different configurations of CAS may exhibit very large
variability in their performance and the quality of services they offer. However, due to
their complexity caused by varying degrees of behaviour, large system scale and highly
distributed nature, it is often very difficult to understand and predict the behaviour of
CAS under different situations. Novel modelling and quantitative analysis methodologies
are therefore required to address the challenges posed by the complexity of such
systems.
In this thesis, we develop a process algebraic modelling formalism that can be used
to express complex dynamic behaviour of CAS and provide fast and scalable analysis
techniques to investigate the dynamic behaviour and support the design and operational
management of such systems. The major contributions of this thesis are:
(i) development of a novel high-level formalism, PALOMA, the Process Algebra
for Located Markovian Agents for the modelling of CAS. CAS specified in PALOMA
can be automatically translated to their underlying mathematical models called Population
Continuous-Time Markov Chains (PCTMCs).
(ii) development of an automatic moment-closure approximation method which
can provide rapid Ordinary Differential Equation-based analysis of PALOMA models.
(iii) development of an automatic model reduction algorithm for the speed up of
stochastic simulation of PALOMA/PCTMC models.
(iv) presenting a case study, predicting bike availability in stations of Santander
Cycles, the public bike-sharing system in London, to show that our techniques are
well-suited for analysing real CAS
WICC 2016 : XVIII Workshop de Investigadores en Ciencias de la Computaci贸n
Actas del XVIII Workshop de Investigadores en Ciencias de la Computaci贸n (WICC 2016), realizado en la Universidad Nacional de Entre R铆os, el 14 y 15 de abril de 2016.Red de Universidades con Carreras en Inform谩tica (RedUNCI