Abstract
Language  Undefined 

Awarding Institution 

Supervisors/Advisors 

Award date  17 Dec 2014 
Place of Publication  Enschede 
Publisher  
Print ISBNs  9789036538213 
DOIs  
State  Published  17 Dec 2014 
Keywords
 IR93238
 meanfield approximationmodelcheckingMean Field LogicMean Field Continuous Stochastic Logicparameter estimation
 METIS307189
 EWI25594
Cite this
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ModelChecking MeanField Models: Algorithms & Applications. / Kolesnichenko, A.V.
Enschede : Centre for Telematics and Information Technology (CTIT), 2014. 194 p.Research output: Thesis › PhD Thesis  Research UT, graduation UT
TY  THES
T1  ModelChecking MeanField Models: Algorithms & Applications
AU  Kolesnichenko,A.V.
PY  2014/12/17
Y1  2014/12/17
N2  Large 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.
AB  Large 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.
KW  IR93238
KW  meanfield approximationmodelcheckingMean Field LogicMean Field Continuous Stochastic Logicparameter estimation
KW  METIS307189
KW  EWI25594
U2  10.3990/1.9789036538213
DO  10.3990/1.9789036538213
M3  PhD Thesis  Research UT, graduation UT
SN  9789036538213
PB  Centre for Telematics and Information Technology (CTIT)
CY  Enschede
ER 