Data Transmission Reliability of a Two-source Single-sink Computer Network with a Common Arc
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摘要: 在多态网络中, 数据可以通过不同路径来传输. 之前研究多集中于路径不相交之情形, 较少考虑路径含有共享链路情形. 本文考虑计算机网络两个源点通过各自的最小路集向各自宿点传输数据的情况, 其中, 不同的最小路集含有共享链路. 各源点产生一数据序列, 其产生数据的时间间隔随机分布, 不同时刻产生的数据量也随机分布. 时间间隔和数据量可通过Monte-Carlo模拟方法获得. 由于所有数据都通过共享链路进行传输, 数据需要竞争使用链路的优先权, 而这可能会导致冲突. 本文考虑路径连通情况下, 各数据在传输时间限制下成功传输的可靠度评估问题. 仿真结果显示冲突会延长数据的传输时间并由此影响网络可靠度. 本文研究结果为管理者调整时间间隔和数据量以达到理想的网络可靠度提供了参考.
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关键词:
- 多态网络 /
- 最小路集 /
- 共享链路 /
- 网络可靠度 /
- Monte-Carlo仿真
Abstract: In a multi-state network (MSN), data can be transmitted through different paths. The previous literatures focus on the cases with arc-disjoint paths, but not the cases in which the paths have a common arc. In this work, we consider the case that two source nodes are sending data to the sink node through their own minimal paths (MP), where these two MPs possess a common arc in the computer network. Each source node generates a data sequence. The time intervals of the generated data are randomly distributed. Also, the amount of the data generated in different time instants are randomly distributed. These two variables can be obtained by the Monte-Carlo simulation method. As all the data are transmitted through a common arc, the data need to compete for the priority using the arc and conflictions may be caused. We consider the case that the two MPs are in connection and calculate the reliabilities of the data under their transmission time constraints. Simulation results reveal that the conflictions could prolong their transmission time and thus affect their reliabilities. This work may provide a cross-reference for managers to decide the amount of the data and the time intervals to get the ideal network reliabilities. -
[1] Xue J. On multistate system analysis. IEEE Transactions on Reliability, 1985, R-34(4): 329-337 [2] Jane C C, Lin J S, Yuan J. Reliability evaluation of a limited-flow network in terms of minimal cutsets. IEEE Transactions on Reliability, 1993, 42(3): 354-361, 368 [3] Lin J S. Reliability evaluation of capacitated-flow networks with budget constraints. IIE Transactions, 1998, 30(12): 1175-1180 [4] Hsieh C C, Chen Y T. Reliable and economic resource allocation in an unreliable flow network. Computers and Operations Research, 2005, 32(3): 613-628 [5] Zio E. Reliability engineering: old problems and new challenges. Reliability Engineering and System Safety, 2009, 94(2): 125-141 [6] Rocco S C M, Muselli M. Approximate multi-state reliability expressions using a new machine learning technique. Reliability Engineering and System Safety, 2005, 89(3): 261-270 [7] Marseguerra M, Zio E, Podofillini L, Coit D W. Optimal design of reliable network systems in presence of uncertainty. IEEE Transactions on Reliability, 2005, 54(2): 243-253 [8] Castet J F, Saleh J H. Beyond reliability, multi-state failure analysis of satellite subsystems: a statistical approach. Reliability Engineering and System Safety, 2010, 95(4): 311-322 [9] Yeh W C. A simple minimal path method for estimating the weighted multi-commodity multistate unreliable networks reliability. Reliability Engineering and System Safety, 2008, 93(1): 125-136 [10] Lin Y K. Calculation of minimal capacity vectors through k minimal paths under budget and time constraints. European Journal of Operational Research, 2010, 200(1): 160-169 [11] Lin Y K. Reliability of k separate minimal paths under both time and budget constraints. IEEE Transactions on Reliability, 2010, 59(1): 183-190 [12] Yeh W C. Multistate-node acyclic networks reliability evaluation based on MC. Reliability Engineering and System Safety, 2003, 81(2): 225-231 [13] Zio E, Podofillini L. Monte Carlo simulation analysis of the effects of different system performance levels on the importance of multi-state components. Reliability Engineering and System Safety, 2003, 82(1): 63-67 [14] Zio E, Podofillini L, Levitin G. Estimation of the importance measures of multi-state elements by Monte Carlo simulation. Reliability Engineering and System Safety, 2004, 86(3): 191-204 [15] Zio E, Marella M, Podofillini L. A Monte Carlo simulation approach to the availability assessment of multi-state systems with operational dependencies. Reliability Engineering and System Safety, 2007, 92(7): 871-882 [16] Ramirez-Marquez J E, Coit D W. A Monte-Carlo simulation approach for approximating multi-state two-terminal reliability. Reliability Engineering and System Safety, 2005, 87(2): 253-264 [17] Yeh W C. An improved Monte-Carlo method for estimating the continuous-state network one-to-one reliability. WSEAS Transaction on Systems, 2007, 6(5): 959-966 [18] Yeh W C, Lin Y C, Chung Y K. Performance analysis of cellular automata Monte Carlo simulation for estimating network reliability. Expert Systems with Applications, 2010, 37(5): 3537-3544 [19] Ge H F, Asgarpoor S. Parallel Monte Carlo simulation for reliability and cost evaluation of equipment and systems. Electric Power Systems Research, 2011, 81(2): 347-356 [20] Haroonabadia H, Haghifam M R. Generation reliability assessment in power markets using Monte Carlo simulation and soft computing. Applied Soft Computing, 2011, 11(8): 5292-5298 [21] Lin Y K. On transmission time through k minimal paths of a capacitated-flow network. Applied Mathematical Modelling, 2010, 34(2): 245-253 [22] Lin Y K. Spare routing reliability for a stochastic flow network through two minimal paths under budget constraint. IEEE Transactions on Reliability, 2010, 59(1): 2-10 [23] Lin Y K. Spare routing problem with p minimal paths for time-based stochastic flow networks. Applied Mathematical Modelling, 2011, 35(3): 1427-1438 [24] Lin Y K. Network reliability of a time-based multistate network under spare routing with p minimal paths. IEEE Transactions on Reliability, 2011, 60(1): 61-69 [25] Hsieh C C, Lin M H. Reliability oriented multi-resource allocation in a stochastic-flow network. Reliability Engineering and System Safety, 2003, 81(2): 155-161 [26] Hsieh C C, Lin M H. Simple algorithms for updating multi-resource allocations in an unreliable flow network. Computers and Industrial Engineering, 2006, 50(1-2): 120-129 [27] Lian Feng, Han Chong-Zhao, Liu Wei-Feng, Yuan Xiang-Hui. Tracking partly resolvable group targets using SMC-PHDF. Acta Automatica Sinica, 2010, 36(5): 731-741 (in Chinese) [28] Li Tian-Cheng, Sun Shu-Dong. Double-resampling based Monte Carlo localization for mobile robot. Acta Automatica Sinica, 2010, 36(9): 1279-1286 (in Chinese) [29] Liang Yong-Qi, Han Chong-Zhao, Sun Yao-Jie, Lin Yan-Dan, Yang Yong-An. Modeling and multiple-model estimation of invariable-structure semi-ballistic reentry vehicle. Acta Automatica Sinica, 2011, 32(6): 700-712 (in Chinese) [30] Aggarwa K K, Misra K B, Gupta J S. A simple method for reliability evaluation of a communication system. IEEE Transactions on Communications, 1975, 23(5): 563-566 [31] Jane C C, Laih Y W. Computing multi-state two-terminal reliability through critical arc states that interrupt demand. IEEE Transactions on Reliability, 2010, 59(2): 338-345 [32] Ford L R, Fulkerson D R. Flows in Networks. New Jersey: Princeton University Press, 1962
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