{"title":"网络仿真中的连续时间隐马尔可夫模型","authors":"Tang Bo, Tan Xiaobin, Yin Bao-qun","doi":"10.1109/KAMW.2008.4810577","DOIUrl":null,"url":null,"abstract":"The use of continuous-time hidden Markov models for network protocol and application performance evaluation has been validated to simulate network environments. In this paper, we develop a better algorithm to infer the continuous-time hidden Markov model from a series of end-to-end delay and loss observation of probing packets. We prove the algorithm's feasibility by theory deduction and realize numerable validation by comparing the probability of the observed sequence produced by the model inferred by different methods. The algorithm complexity is lower.","PeriodicalId":375613,"journal":{"name":"2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Continuous-time Hidden Markov models in Network Simulation\",\"authors\":\"Tang Bo, Tan Xiaobin, Yin Bao-qun\",\"doi\":\"10.1109/KAMW.2008.4810577\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of continuous-time hidden Markov models for network protocol and application performance evaluation has been validated to simulate network environments. In this paper, we develop a better algorithm to infer the continuous-time hidden Markov model from a series of end-to-end delay and loss observation of probing packets. We prove the algorithm's feasibility by theory deduction and realize numerable validation by comparing the probability of the observed sequence produced by the model inferred by different methods. The algorithm complexity is lower.\",\"PeriodicalId\":375613,\"journal\":{\"name\":\"2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KAMW.2008.4810577\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KAMW.2008.4810577","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Continuous-time Hidden Markov models in Network Simulation
The use of continuous-time hidden Markov models for network protocol and application performance evaluation has been validated to simulate network environments. In this paper, we develop a better algorithm to infer the continuous-time hidden Markov model from a series of end-to-end delay and loss observation of probing packets. We prove the algorithm's feasibility by theory deduction and realize numerable validation by comparing the probability of the observed sequence produced by the model inferred by different methods. The algorithm complexity is lower.