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

Fluid Analysis of Spatio-Temporal Properties of Agents in a Population Model

We consider large stochastic population models in which heterogeneous agents are interacting locally and moving in space. These models are very common, e.g. in the context of mobile wireless networks, crowd dynamics, traffic management, but they are typically very hard to analyze, even when space is discretized in a grid. Here we consider individual agents and look at their properties, e.g. quality of service metrics in mobile networks. Leveraging recent results on the combination of stochastic approximation with formal verification, and of fluid approximation of spatio-temporal population processes, we devise a novel mean-field based approach to check such behaviors, which requires the solution of a low-dimensional set of Partial Differential Equation, which is shown to be much faster than simulation. We prove the correctness of the method and validate it on a mobile peer-to-peer network example