Light traffic derivatives via likelihood ratios

The steady-state behavior of open queuing systems with Poisson arrival processes in light traffic, that is, as the arrival rate tends to zero, is considered. Expressions are provided for the derivatives with respect to the arrival rate of various quantities of interest (such as moments of steady-state sojourn times and queue lengths), evaluated at an arrival rate of zero. These expressions are obtained using the regenerative structure of the queuing system and a change-of-measure formula based on likelihood ratios. The derivatives, which can be used in interpolation approximations, can be evaluated analytically in simple cases and by simulation in general     

基于统计型流量包络的网络流量建模

统计型流量包络以统一的数学形式抽象具有各种统计特性的网络流量过程,从而得到一般性的理论分析结果.文章全面介绍了各种不同的统计型流量包络,分析、比较了其适用性和应用局限,并提出了仍然存在的主要问题,以期为进一步的理论研究和实际应用提供有益的参考.     

Identifying file-sharing P2P traffic based on traffic characteristics

This article focuses on identifying file-sharing peer-to-peer （P2P） （such as BitTorrent （BT）） traffic at the borders of a stub network. By analyzing protocols and traffic of applications, it is found that file-sharing P2P traffic of a single user differs greatly from traditional and other P2P （such as QQ） applications＇ traffic in the distribution of involved remote hosts and remote ports. Therefore, a method based on discreteness of remote hosts （RHD） and discreteness of remote ports （RPD） is proposed to identify BT-like traffic. This method only relies on flow information of each user host in a stub network, and no packet payload needs to be monitored. At intervals, instant RHD for concurrent transmission control protocol and user datagram protocol flows for each host are calculated respectively through grouping flows by the stub network that the remote host of each flow belongs to. On given conditions, instant RPD are calculated through grouping flows by the remote port to amend instant RHD. Whether a host has been using a BT-like application or not can be deduced from instant RHD or average RHD for a period of time. The proposed method based on traffic characteristics is more suitable for identifying protean file-sharing P2P traffic than content-based methods Experimental results show that this method is effective with high accuracy.     

Computer-controlled vehicular traffic

It is no exaggeration that a kid on roller skates can propel himself crosstown, from the East River to the North River, on any of Manhattan's midtown streets in considerably less time than that required by a motorist. And we all have had the frustrating experience of spending more time in a bus between an airport and center city than in making the actual flight of 500 km or more. About five years ago, Boston, Mass., got a foretaste of what complete vehicular strangulation can be like when the granddaddy of traffic jams immobilized all surface transportation downtown for more than three hours. But the future may not be completely bleak: thanks to the digital computer, sensor inputs?and a lot of software synthesis?traffic intersections can be placed under machine control to break up the traffic bottlenecks that have become one of the major blights of our cities.     

Internet traffic engineering

The main goal of Internet traffic engineering is to efficiently optimize the performance of operational networks in order to avoid the well-known shortcomings of the typical destination-based IP routing. Traffic engineering attempts to reduce or even avoid congestion hot spots and to improve resource utilization across the backbone IP network. During the last years traffic engineering has become an inevitable tool concerning performance optimization in large Internet backbones. The core objective of this paper is to give an overview of the architectures and mechanisms for traffic engineering.     

Inverting sampled traffic

Routers have the ability to output statistics about packets and flows of packets that traverse them. Since, however, the generation of detailed traffic statistics does not scale well with link speed, increasingly routers and measurement boxes implement sampling strategies at the packet level. In this paper, we study both theoretically and practically what information about the original traffic can be inferred when sampling, or "thinning", is performed at the packet level. While basic packet level characteristics such as first order statistics can be fairly directly recovered, other aspects require more attention. We focus mainly on the spectral density, a second-order statistic, and the distribution of the number of packets per flow, showing how both can be exactly recovered, in theory. We then show in detail why in practice this cannot be done using the traditional packet based sampling, even for high sampling rate. We introduce an alternative flow-based thinning, where practical inversion is possible even at arbitrarily low sampling rate. We also investigate the theory and practice of fitting the parameters of a Poisson cluster process, modeling the full packet traffic, from sampled data.