Quantitative Evaluation of Systems - 19th International Conference, QEST 2022, Warsaw, Poland, September 12-16, 2022, Proceedings
Quantitative Evaluation of Systems - 19th International Conference, QEST 2022, Warsaw, Poland, September 12-16, 2022, Proceedings
复制标题
系统定量评估 - 第 19 届国际会议，QEST 2022，波兰华沙，2022 年 9 月 12-16 日，会议记录
DOI：
10.1007/978-3-031-16336-4_12
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Casale G
中科院分区：
文献类型：
--
作者：
Casale G
介绍了LINE软件包中引入的一种用于分析分层排队网络(LQN)模型的新型求解器LN。LNsolver的创新之处在于它能够使用用户定义的解决方案范例组合来分析LQN，这些范例包括离散事件和随机模拟、连续时间马尔可夫链分析(CTMC)、归一化常数评估(NC)、矩阵分析方法(MAM)、平均场近似(FUID)和平均值分析(MVA)。作为用于每个LQN层的求解器中的参数，LNA作为一个整体，能够有效地计算高级性能指标，例如边际和联合状态概率、响应和通过时间分布以及瞬时测量，从而利用所支持的解决方案范例的单独优势。我们特别讨论了最近添加到LN的缺省层求解器NC中的最新进展，这些进展显著改进了使用LQN的松散分层得到的排队网络模型的解。
We overviewLN, a novel solver introduced in the LINE software package to analyze layered queueing network (LQN) models. The novelty of theLNsolver lies in its capability to analyze LQNs with a user-defined combination of solution paradigms, including discrete-event and stochastic simulation, continuous-time Markov chain analysis (CTMC), normalizing constant evaluation (NC), matrix analytic methods (MAM), mean-field approximations (FLUID), and mean-value analysis (MVA). Being parametric in the solver used for each LQN layer,LNas a whole enables the efficient computation of advanced performance metrics such as marginal and joint state probabilities, response and passage time distributions, and transient measures, leveraging individual strengths of the supported solution paradigms. We discuss in particular recent developments added to NC, the default layer solver ofLN, which significantly improve the solution of queueing network models obtained using loose layering of the LQN.