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| Transient Reward Approximation for Continuous-Time Markov Chains | |
| Hahn, EM; Hermanns, H; Wimmer, R; Becker, B | |
| 2015 | |
| Source | IEEE TRANSACTIONS ON RELIABILITY
 |
| ISSN | 0018-9529 |
| Volume | 64Issue:4Pages:1254-1275 |
| English Abstract | We are interested in the analysis of very large continuous-time Markov chains (CTMCs) with many distinct rates. Such models arise naturally in the context of reliability analysis, e.g., of computer network performability analysis, of power grids, of computer virus vulnerability, and in the study of crowd dynamics. We use abstraction techniques together with novel algorithms for the computation of bounds on the expected final and accumulated rewards in continuous-time Markov decision processes (CTMDPs). These ingredients are combined in a partly symbolic and partly explicit (symblicit) analysis approach. In particular, we circumvent the use of multi-terminal decision diagrams, because the latter do not work well if facing a large number of different rates. We demonstrate the practical applicability and efficiency of the approach on two case studies.; We are interested in the analysis of very large continuous-time Markov chains (CTMCs) with many distinct rates. Such models arise naturally in the context of reliability analysis, e.g., of computer network performability analysis, of power grids, of computer virus vulnerability, and in the study of crowd dynamics. We use abstraction techniques together with novel algorithms for the computation of bounds on the expected final and accumulated rewards in continuous-time Markov decision processes (CTMDPs). These ingredients are combined in a partly symbolic and partly explicit (symblicit) analysis approach. In particular, we circumvent the use of multi-terminal decision diagrams, because the latter do not work well if facing a large number of different rates. We demonstrate the practical applicability and efficiency of the approach on two case studies. |
| Indexed Type | SCI |
| Keyword | Continuous-time Markov Chains Continuous-time Markov Decision Processes Abstraction Symbolic Methods Ordered Binary Decision Diagrams |
| Department | Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China. Univ Saarland, D-66123 Saarbrucken, Germany. Univ Oxford, Oxford OX1 2JD, England. Univ Saarland, Dept Comp Sci, D-66123 Saarbrucken, Germany. Univ Freiburg, Breisgau, Germany. Univ Freiburg, Fac Engn, Chair Comp Architecture, Breisgau, Germany. |
| Language | 英语 |
| WOS ID | WOS:000366323500012 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/17427 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Hahn, EM,Hermanns, H,Wimmer, R,et al. Transient Reward Approximation for Continuous-Time Markov Chains[J]. IEEE TRANSACTIONS ON RELIABILITY,2015,64(4):1254-1275. |
| APA | Hahn, EM,Hermanns, H,Wimmer, R,&Becker, B.(2015).Transient Reward Approximation for Continuous-Time Markov Chains.IEEE TRANSACTIONS ON RELIABILITY,64(4),1254-1275. |
| MLA | Hahn, EM,et al."Transient Reward Approximation for Continuous-Time Markov Chains".IEEE TRANSACTIONS ON RELIABILITY 64.4(2015):1254-1275. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 07163373.pdf（4166KB） | 开放获取 | License | Application Full Text | |||
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