Network traffic forecasts, as it is well known, can be useful for network resource optimization. In order to minimize the forecast error by maximizing information utilization with low complexity, this paper concerns the difference of traffic trends at large time scales and fits a dual-related model to predict it. First, by analyzing traffic trends based on user behavior, we find both hour-to-hour and day-to-day patterns, which means that models based on either of the single trends are unable to offer precise predictions. Then, a prediction method with the consideration of both daily and hourly traffic patterns, called the dual-related forecasting method, is proposed. Finally, the correlation for traffic data is analyzed based on model parameters. Simulation results demonstrate the proposed model is more effective in reducing forecasting error than other models.

Measuring and modeling network traffic is of key importance for the traffic engineering of IP networks, due to the growing diversity of multimedia applications and the need to efficiently support QoS differentiation in the network. Several recent measurements have shown that Internet traffic may incorporate long-range dependence and self-similar characteristics, which can have significant impact on network performance. Self-similar traffic shows variability over many time scales, and this behavior must be taken into account for accurate prediction of network performance. In this paper, we propose a new parameter fitting procedure for a superposition of Markov Modulated Poisson Processes (MMPPs), which is able to capture self-similarity over a range of time scales. The fitting procedure matches the complete distribution of the arrival process at each time scale of interest. We evaluate the procedure by comparing the Hurst parameter, the probability mass function at each time scale, and the queuing behavior (as assessed by the loss probability and average waiting time), corresponding to measured traffic traces and to traces synthesized according to the proposed model. We consider three measured traffic traces, all exhibiting self-similar behavior: the well-known pOct Bellcore trace, a trace of aggregated IP WAN traffic, and a trace corresponding to the popular file sharing application Kazaa. Our results show that the proposed fitting procedure is able to match closely the distribution over the time scales present in data, leading to an accurate prediction of the queuing behavior.

This paper studies the local Poisson property of aggregated IP traffic. First, it describes the scenario where IP traffic presents a Poisson-like characteristic within some limited range of time scales when packets from independent traffic streams are aggregated. Each of the independent traffic streams corresponds to a series of correlated IP packets such as those of a transport connection. Since the Poisson-like characteristic is observed only within some limited range of time scales, we call this characteristic the local Poisson property. The limited range of time scales of the local Poisson property can be estimated from a network configuration and characteristics of transport connections. Second, based on these observations, we seek the possibility to apply an ordinary Poisson process to evaluation of the packet loss probability in IP networks. The analytical investigation, where IP traffic is modeled by a superposition of independent branching Poisson processes that presents the local Poisson property, suggests that the packet loss probability can be estimated by a finite-buffer queue with a Poisson process when the buffer size is within a certain range. The investigation is verified by simulations. These findings expand the applicability of conventional Poisson-based approaches to IP network design issues.

The international telecommunication standards bodies such as ITU-T, ATM Forum, and IETF recommend the dual leaky bucket for the traffic specifications for VBR service. On the other hand, recent studies have demonstrated multiple time-scale burstiness in compressed video traffic. In order to fill this gap between the current standards and real traffic characteristics, we present a standard-compatible traffic parameter selection method based on the notion of a critical time scale (CTS). The defined algorithm is optimal in the sense that it minimizes the required amount of link capacity for a traffic flow under a maximum delay constraint. Simulation results with compressed video traces demonstrate the efficiency of the defined traffic parameter selection algorithm in resource allocation.