The number of customers of a service for Internet access from cellular phones in Japan has been explosively increasing for some time. We analyze the relation between the number of customers and the volume of traffic, with a view to finding clues to the structure of human relations among the very large set of potential customers of the service. The traffic data reveals that this structure is a scale-free network, and we calculate the exponent that governs the distribution of node degree in this network. The data also indicates that people who have many friends tend to subscribe to the service at an earlier stage. These results are useful for investigating various fields, including marketing strategies, the propagation of rumors, the spread of computer viruses, and so on.

Recent growth in computer communications has led to an increased requirement for high-speed backbone networks. In such high-speed networks, the principle adopted for a time-sensitive flow control mechanism should be that of autonomous decentralized control. In this mechanism, each node in a network manages its local traffic flow only on the basis of the local information directly available to it, although it is desirable that the individual decisions made at each node lead to high performance of the network as a whole. In our previous studies, we have investigated the behavior of local packet flows and the global performance achieved when a node is congested, and proposed the diffusion-type flow control model. However, since we used a simple and homogeneous network model in the evaluation, the results cannot be generalized. In this paper, we propose an extension of the diffusion-type flow control model in order to apply it to networks with inhomogeneous configurations. We show simulation results for two cases: different propagation delays and multiple bottlenecks. Both results show that the proposed diffusion-type flow control achieves high and stable performance even if the network is congested.

There has been a rapid traffic volume increase in packet switched networks due to the recent data communication growth. Consequently, the ease with which networks are capable of expanding to keep pace with this increase has become an important consideration. In this paper, a comparison is made of the hierarchical and non-hierarchical network configurations which may serve as possible methods for readily accommodating this increasing network expansion. A special developed simulation providing essential simulation analyses confirms that the hierarchical network offers the most promising approach to this problem from the viewpoints of cost effectiveness and network maintenance.

This paper discusses research into the capacity dimensioning of Virtual Private Network (VPN) access links for elastic traffic, such as the Web or ftp. Assuming that the core-VPN network is provisioned with a sufficiently large capacity, managing the capacity of the VPN access link comes to sharing the bandwidth for the elastic traffic of the two bottlenecks, the ingress and egress access links. In the case of a single bottleneck with a limited capacity for access links, the processor-sharing model provides a simple formula for mean transfer time, but here, the value may be less than the actual transfer time because multiple flow may compete the bandwidth of both ingress and egress links. In contrast, max-min fair sharing provides an accurate sharing model which is similar to the TCP, but it is difficult to obtain a closed form for performance statistics. We propose a closed form approximation for a max-min fair sharing model, within a specific but realistic topology, through an investigation into the difference between the max-min and the processor sharing model. Using approximation, we calculate the capacity dimensioning of VPN access links.

Since the TCP is the transport protocol for most Internet applications, evaluation of TCP throughput is important. In this paper, we establish a framework of evaluating TCP throughput by simple measurement. TCP throughput is generally measured by sending TCP traffic and monitoring its arrival or using data from captured packets, neither of which suits our proposal because of heavy loads and lack of scalability. While there has been much research into the analytical modeling of TCP behavior, this has not been concerned with the relationship between modeling and measurement. We thus propose a lightweight method for the evaluation of TCP throughput by associating measurement with TCP modeling. Our proposal is free from the defects of conventional methods, since measurement is performed to obtain the input parameters required to calculate TCP throughput. Numerical examples show the proposed framework's effectiveness.

We previously proposed a change-of-measure based performance measurement method which combines active and passive measurement to estimate performance experienced by user packets and applied this to estimate packet delay. In this paper, we apply it to estimating loss rate. Since packets are rarely lost in current networks, rate measurement usually requires a huge number of probe packets, which imposes a non-negligible load on networks. We propose a loss-rate estimation method which requires significantly fewer number of probe packets. In our proposed method, the correlation between delay and loss is measured in advance, and at the time of measurement, the time-averaged loss rate is estimated by using the delay of probe packets and the correlation. We also applied our change-of-measure framework to estimating the loss rate in user packets by using this time-averaged loss rate. We prove that the mean square error in our method is lower than that simple loss measurement, which is estimated by dividing the number of lost packets by the total number of sent packets. We evaluated our method through simulations and actual measurements and found that it can estimate below 10-3 packet loss rate with only 900 probe packets.

This paper investigates a method of dynamically adjusting inter-packet gaps in accordance with network conditions, and demonstrates that the number of dropped packets is a critical parameter for adjusting inter-packet gaps. This technique, known as rate flow control, can prevent overruns in high-speed, low-delay, low-error-rate networks.