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Title:Solution of Large Markov Models Using Lumping Techniques and Symbolic Data Structures
Author(s):Derisavi, Salem
Doctoral Committee Chair(s):Sanders, William H.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Computer Science
Abstract:In particular, we have developed the fastest known CTMC lumping algorithm with the running time of O (m log n), where n and m are the number of states and non-zero entries of the generator matrix of the CTMC, respectively. We have also combined the use of symbolic data structures with state-lumping techniques to develop an efficient symbolic state-space exploration algorithm for state-sharing composed models that exploits lumpings that are due to equally behaving components. Finally, we have developed a new compositional algorithm that lumps CTMCs represented as MDs. Unlike other compositional lumping algorithms, our algorithm does not require any knowledge of the modeling formalisms from which the MDs were generated. Our approach relies on local conditions, i.e., conditions on individual nodes of the MD.
Issue Date:2005
Type:Text
Language:English
Description:158 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.
URI:http://hdl.handle.net/2142/81681
Other Identifier(s):(MiAaPQ)AAI3198968
Date Available in IDEALS:2015-09-25
Date Deposited:2005


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