QRF An Optimization-Based Framework for Evaluating Complex Stochastic Networks
QRF An Optimization-Based Framework for Evaluating Complex Stochastic Networks
复制标题
QRF 用于评估复杂随机网络的基于优化的框架
DOI：
10.1145/2724709
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发表时间：
2016
影响因子：
0.9
通讯作者：
Casale G
中科院分区：
文献类型：
--
作者：
Casale G
二次缩减框架(QRF)是一个用于评估复杂随机网络的数值建模框架，该网络由具有排队、阻塞、状态依赖行为、服务可变性、时间依赖或其子集的资源组成。这类系统被抽象为队列网络，其中QRF支持两种常见的阻塞机制：服务后阻塞和重复服务随机目的地。路由概率和服务进程都支持状态相关。为了评估这些模型，我们发展了一个新的映射，称为块感知二次归约(BQR)，它可以用一组大的线性不等式来描述一个难以处理的大马尔可夫过程。然后，使用提供不同精度和误差保证水平的优化程序来分析每个模型的性能指标的界限或近似值。数值结果表明，与精确分析方法相比，QRF具有很好的精度和更大的可扩展性。
The Quadratic Reduction Framework (QRF) is a numerical modeling framework to evaluate complex stochastic networks composed of resources featuring queueing, blocking, state-dependent behavior, service variability, temporal dependence, or a subset thereof. Systems of this kind are abstracted as network of queues for which QRF supports two common blocking mechanisms: blocking-after-service and repetitive-service random-destination. State-dependence is supported for both routing probabilities and service processes. To evaluate these models, we develop a novel mapping, calledBlocking-Aware Quadratic Reduction (BQR), which can describe an intractably large Markov process by a large set of linear inequalities. Each model is then analyzed for bounds or approximate values of performance metrics using optimization programs that provide different levels of accuracy and error guarantees. Numerical results demonstrate that QRF offers very good accuracy and much greater scalability than exact analysis methods.