The solution of separable queueing network models using mean value analysis
The solution of separable queueing network models using mean value analysis
复制标题
利用均值分析求解可分离排队网络模型
DOI：
10.1145/800189.805477
复制
发表时间：
1981
影响因子：
3.6
通讯作者：
Eugene Wong
中科院分区：
文献类型：
--
作者：
J. Zahorjan;Eugene Wong
由于均值分析（MVA）比以往的卷积算法更直观，因此作为可分离卷积网络的精确求解技术，MVA得到了广泛的应用。然而，迄今为止所提出的MVA的推导仅适用于封闭的神经网络模型。此外，MVA的存储需求问题也没有得到令人满意的处理。在本文中，我们解决这两个问题，提出MVA解决方案的开放和混合负载独立的网络，和存储维护技术，我们假设是最低限度的任何“合理”的MVA技术。
Because it is more intuitively understandable than the previously existing convolution algorithms, Mean Value Analysis (MVA) has gained great popularity as an exact solution technique for separable queueing networks. However, the derivations of MVA presented to date apply only to closed queueing network models. Additionally, the problem of the storage requirement of MVA has not been dealt with satisfactorily. In this paper we address both these problems, presenting MVA solutions for open and mixed load independent networks, and a storage maintenance technique that we postulate is the minimum possible of any “reasonable” MVA technique.