{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:31:08Z","timestamp":1750307468165,"version":"3.41.0"},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2010,9,1]],"date-time":"2010-09-01T00:00:00Z","timestamp":1283299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Model. Comput. Simul."],"published-print":{"date-parts":[[2010,9]]},"abstract":"<jats:p>This article considers importance sampling as a tool for rare-event simulation. The focus is on estimating the probability of overflow in the downstream queue of a Jacksonian two-node tandem queue; it is known that in this setting \u201ctraditional\u201d state-independent importance-sampling distributions perform poorly. We therefore concentrate on developing a state-dependent change of measure, that we prove to be asymptotically efficient.<\/jats:p>\n          <jats:p>\n            More specific contributions are the following. (i) We concentrate on the probability of the second queue exceeding a certain predefined threshold before the system empties. Importantly, we identify an asymptotically efficient importance-sampling distribution for\n            <jats:italic>any<\/jats:italic>\n            initial state of the system. (ii) The choice of the importance-sampling distribution is backed up by appealing heuristics that are rooted in large-deviations theory. (iii) The method for proving asymptotic efficiency relies on probabilistic arguments only. The article is concluded by simulation experiments that show a considerable speedup.\n          <\/jats:p>","DOI":"10.1145\/1842713.1842718","type":"journal-article","created":{"date-parts":[[2010,10,5]],"date-time":"2010-10-05T14:38:15Z","timestamp":1286289495000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["State-dependent importance sampling for a Jackson tandem network"],"prefix":"10.1145","volume":"20","author":[{"given":"Denis","family":"Miretskiy","sequence":"first","affiliation":[{"name":"University of Twente, AE Enschede, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Werner","family":"Scheinhardt","sequence":"additional","affiliation":[{"name":"University of Twente, AE Enschede, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michel","family":"Mandjes","sequence":"additional","affiliation":[{"name":"University of Amsterdamm, TV Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2010,10,7]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"}}Anantharam V. Heidelberger P. and Tsoucas P. 1990. Analysis of rare events in continuous time Markov chains via time reversal and fluid approximation. IBM Res. rep. 16280.  }}Anantharam V. Heidelberger P. and Tsoucas P. 1990. Analysis of rare events in continuous time Markov chains via time reversal and fluid approximation. IBM Res. rep. 16280."},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1147224.1147226"},{"volume-title":"Proceedings of the Winter Simulation Conference (WSC'00)","author":"de Boer P.","key":"e_1_2_2_3_1"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11134-007-9049-2"},{"key":"e_1_2_2_5_1","doi-asserted-by":"crossref","unstructured":"}}Dembo A. and Zeitouni O. 1998. Large Deviations Techniques and Applications. 2nd Ed. Springer New York.  }}Dembo A. and Zeitouni O. 1998. Large Deviations Techniques and Applications. 2nd Ed. Springer New York.","DOI":"10.1007\/978-1-4612-5320-4"},{"key":"e_1_2_2_6_1","doi-asserted-by":"crossref","unstructured":"}}Dupuis P. and Ellis R. 1997. A Weak Convergence Approach to the Theory of Large Deviations. Wiley New York.  }}Dupuis P. and Ellis R. 1997. A Weak Convergence Approach to the Theory of Large Deviations. Wiley New York.","DOI":"10.1002\/9781118165904"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/17442509108833688"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1214\/105051607000000122"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/10451120410001733845"},{"key":"e_1_2_2_10_1","doi-asserted-by":"crossref","unstructured":"}}Ellis R. 1985. Entropy Large Deviations and Statistical Mechanics. Springer New York.  }}Ellis R. 1985. Entropy Large Deviations and Statistical Mechanics. Springer New York.","DOI":"10.1007\/978-1-4613-8533-2"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/203091.203093"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/203091.203094"},{"key":"e_1_2_2_13_1","unstructured":"}}Kelly F. 1979. Reversibility and Stochastic Networks. Wiley New York.  }}Kelly F. 1979. Reversibility and Stochastic Networks. Wiley New York."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/566392.566395"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1214\/105051604000000477"},{"key":"e_1_2_2_16_1","unstructured":"}}Miretskiy D. 2009. Queueing networks: rare events and fast simulations. Ph.D. thesis University of Twente Enschede The Netherlands.  }}Miretskiy D. 2009. Queueing networks: rare events and fast simulations. Ph.D. thesis University of Twente Enschede The Netherlands."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1177\/0037549707086193"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/9.8649"},{"key":"e_1_2_2_19_1","unstructured":"}}Shwartz A. and Weiss A. 1995. Large Deviations for Performance Analysis. Queues Communications and Computing. Chapman &amp; Hall London.  }}Shwartz A. and Weiss A. 1995. Large Deviations for Performance Analysis. Queues Communications and Computing. Chapman &amp; Hall London."},{"key":"e_1_2_2_20_1","doi-asserted-by":"crossref","unstructured":"}}Varadhan S. 1984. Large Deviations and Applications. SIAM Philadelphia PA.  }}Varadhan S. 1984. Large Deviations and Applications. SIAM Philadelphia PA.","DOI":"10.1137\/1.9781611970241"}],"container-title":["ACM Transactions on Modeling and Computer Simulation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1842713.1842718","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1842713.1842718","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T12:08:36Z","timestamp":1750248516000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1842713.1842718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,9]]},"references-count":20,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2010,9]]}},"alternative-id":["10.1145\/1842713.1842718"],"URL":"https:\/\/doi.org\/10.1145\/1842713.1842718","relation":{},"ISSN":["1049-3301","1558-1195"],"issn-type":[{"type":"print","value":"1049-3301"},{"type":"electronic","value":"1558-1195"}],"subject":[],"published":{"date-parts":[[2010,9]]},"assertion":[{"value":"2008-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2009-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2010-10-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}