Optimal Resource Capacity Management for Stochastic Networks
针对随机网络中每个站点的资源容量优化问题，提出一种仅依赖观察队列长度的迭代求解方法，在单类布朗树网络上验证了理论性质，并通过计算实验展示了效果。
We develop a framework for determining the optimal resource capacity of each station composing a stochastic network, motivated by applications arising in computer capacity planning and business process management. The problem is mathematically intractable in general and therefore one typically resorts to either simplistic analytical approximations or time-consuming simulation-based optimization methods. Our solution framework includes an iterative methodology that relies only on the capability of observing the queue lengths at all network stations for a given resource capacity allocation. We theoretically investigate this proposed methodology for single-class Brownian tree networks and illustrate the use of our framework and the quality of its results through computational experiments.