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oai:www.ideals.illinois.edu:2142/81681 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Sanders, William H. Derisavi, Salem 2015-09-25T20:19:59Z 2015-09-25T20:19:59Z 10000-01-01 2005 2005 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. Made available in DSpace on 2015-09-25T20:19:59Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3198968.pdf: 4444528 bytes, checksum: 4b0971b3295d6f20505f09d83351ce27 (MD5) Previous issue date: 2005 Embargo set by: Seth Robbins for item 82962 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 158 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005. http://hdl.handle.net/2142/81681 (MiAaPQ)AAI3198968 eng Computer Science Solution of Large Markov Models Using Lumping Techniques and Symbolic Data Structures text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.