Polynomial Time Algorithms for Stochastic Petri Net Analysis

这项研究将为随机变量的分析开发新的技术 Petri网是一种高效、精确的网络模型。 这一方法将 为分布式的实际模型提供性能评估， 并行计算机系统，目前不可能没有 仿真 这项研究的结果将提供算法， 随机Petri网模型的有效评价。 这些 算法的计算复杂度类似于乘积形式 排队网络模型的技术。 适用性评价 的理论框架将完成使用原型 设计系统，SPAN，已经在开发中。 理由和建议 一个相对年轻的研究者正在研究随机Petri网模型。 如果成功的话，这项研究可以改进模型， 理解分布式和并行计算。 支持是 推荐

This research will develop new analytical techniques for Stochastic Petri Nets which are both efficient and exact. The methodology will provide performance evaluation for realistic models of distributed and parallel computer systems which is not currently possible without simulation. The results of this research will provide algorithms for the efficient evaluation of Stochastic Petri Net models. These algorithms have a computational complexity similar to the product form techniques for queuing network models. Evaluation of the applicability of the theoretical framework will be accomplished using a prototype design system, SPAN, which is already under development. Justification and Recommendation A relatively young investigator is studying Stochastic Petri Net models. If successful the research could lead to improved modelling and understanding distributed and parallel computations. Support is recommended.