Routing Control of Packet Flow by using Statistical-Physical Methods

1.分组路由控制的研究我们提出了一个大型计算机网络上的分组流的分散、自治和自适应的物理-物理模型。定义了一个能量函数来表示队列长度、到目的地的距离之间的竞争。通过使用能量函数来执行分组路由控制。我们已经提出了几种使用神经网络，伊辛模型或强化学习系统的配方。实验结果表明，这些模型对分组路由控制具有良好的性能.分组流最优网络结构设计的研究通过定义一个表示分组流效率的代价函数，研究了大型网络中分组流的最优网络结构。结果发现，随机网络在目的地随机确定的情况下是最优的，而具有无标度特性的网络在缓冲区大小分布具有幂律形式且目的地以与节点的缓冲区大小成比例的概率确定的情况下是最优的.无标度网络生成机制的研究我们研究了非生长网络自组织成无标度网络的可能性。通过在重布线过程中引入适应度参数，我们证明了在非增长的情况下也会产生具有无标度特性的网络。在二分图的非增长模型中也观察到了同样的行为。

1. Study on packet routing controlWe have proposed statistical-physical models which are decentralized, autonomous and adaptive for packet flow on a large computer network. An energy function is defined in order to express competition among queue length, distance to destination of a packet. The packet routing control is performed by using the energy function. We have proposed several formulations using neural networks, Ising models, or reinforcement learning systems. It has been shown that these models give good performance for packet routing control.2. Study on designing of optimal network strictures for packet flowWe have investigated an optimal network structure for packet flow on large networks by defining a cost function which represents efficiency of the packet flow. As a result, we have found that random networks are optimal in the case where a destination is determined randomly, and networks with scale-free property are optimal in the case where the buffer-size distribution has a power-law form and a destination is determined with the probability which is proportional to the buffer size of the node.3. Study on generating mechanism of scale-free networksWe have studied on possibility of self-organization of nongrowing networks into scale-free networks. By introducing fitness parameters in rewiring processes, we have shown that networks with scale-free property are generated also in the nongrowing case. The same behavior has been observed in the nongrowing model for bipartite graphs.