

Kolesnichenko, A.V.
(2014)
ModelChecking MeanField Models: Algorithms & Applications.
PhD thesis, University of Twente.
CTIT Ph.D.thesis series No. 14341
ISBN 9789036538213
Full text available as:
Official URL: http://dx.doi.org/10.3990/1.9789036538213 AbstractLarge systems of interacting objects are highly prevalent in today's world. Such system usually consist of a large number of relatively simple identical objects, and can be observed in many different field as, e.g., physics (interactions of molecules in gas), chemistry (chemical reactions), epidemiology (spread of the infection), etc.
In this thesis we primarily address large systems of interacting objects in computer science, namely, computer networks.
Analysis of such large systems is made difficult by the state space explosion problem, i.e., the number of states of the model grows exponentially with the number of interacting objects.
In this thesis we tackle the statespace explosion problem by applying meanfield approximation, which was originally developed for models in physics, like the interaction of molecules in a gas. The meanfield method works by not considering the state of each individual object separately, but only their average, i.e., what fraction of the objects are in each possible state at any time. It allows to compute the exact limiting behaviour of an infinite population of identical objects, and this limiting behaviour is a good approximation, even when the number of objects is not infinite but sufficiently large. In this thesis we provide the theoretical background necessary for applying the meanfield method and illustrate the approach by a peertopeer Botnet case study.
This thesis aims at formulating and analysing advanced properties of large systems of interacting objects using fast, efficient, and accurate algorithms. We propose to apply modelchecking techniques to meanfield models. This allows (i) defining advanced properties of meanfield models, such as survivability, steadystate availability, conditional instantaneous availability using logic; and (ii) automatically checking these properties using modelchecking algorithms.
Existing modelchecking logics and algorithms can not directly be applied to meanfield models since the model consist of two layers: the local level, describing the behaviour of a randomly chosen individual object in a large system, and the global level, which addresses the overall system of all interacting objects.
Therefore, we motivate and define two logics, called Mean Field Continuous Stochastic Logic (MFCSL), and MeanField Logic (MFL), for describing properties of systems composed of many identical interacting objects, on both the local and the global level.
We present modelchecking algorithms for checking both MFCSL and MFL properties, and illustrated these algorithms using an extensive example on virus propagation in a computer network.
We discuss the differences in the expressiveness of these two logics as well as their possible combination.
Additionally, we combine the meanfield method with parameter fitting techniques in order to model realworld large systems, and obtain a better understanding of the behaviour of such systems. We explain how to build a meanfield model of the system, and how to estimate the corresponding parameter values, so as to find the best fit between the available data and the model prediction. We also discuss a number of intricate technical issues, ranging from the additional (preprocessing) work to be done on the measurement data, the interpretation of the data to, for instance, a restructuring of the model (based on data unavailability), that has to be performed before applying the parameter estimation procedures. To illustrate the approach we estimate the parameter values for the outbreak of the realworld computer worm CodeRed.
The techniques presented in this thesis allow an involved analysis of large systems of interacting objects, including
(i) obtaining parameter values of meanfield model using measurements;
(ii) defining advanced properties of the model; and
(iii) automatically checking such properties. Item Type:  PhD Thesis 

Supervisors:  Haverkort, B.R.H.M. and Remke, A.K.I. 

Assistant Supervisors:  de Boer, P.T. 

Research Group:  EWIDACS: Design and Analysis of Communication Systems 

Research Program:  CTITDSN: Dependable Systems and Networks 

Research Project:  MATMaM: MeanField Approximation Techniques for Markov Models 

Uncontrolled Keywords:  meanfield approximation
modelchecking
Mean Field Logic
Mean Field Continuous Stochastic Logic
parameter estimation 

ID Code:  25594 

Deposited On:  04 February 2015 

More Information:  statistics 

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