We discuss findings from a large-scale study of Internet packet dynamics conducted by tracing 20 000 TCP bulk transfers between 35 Internet sites. Because we traced each 100-kbyte transfer at both the sender and the receiver, the measurements allow us to distinguish between the end-toend behaviors due to the different directions of the Internet paths, which often exhibit asymmetries. We: 1) characterize the prevalence of unusual network events such as out-of-order delivery and packet replication; 2) discuss a robust receiver-based algorithm for estimating “bottleneck bandwidth ” that addresses deficiencies discovered in techniques based on “packet pair;” 3) investigate patterns of packet loss, finding that loss events are not well modeled as independent and, furthermore, that the distribution of the duration of loss events exhibits infinite variance; and 4) analyze variations in packet transit delays as indicators of congestion periods, finding that congestion periods also span a wide range of time scales.

Engineering a large IP backbone network without an accurate, network-wide view of the traffic demands is challenging. Shifts in user behavior, changes in routing policies, and failures of network elements can result in significant (and sudden) fluctuations in load. In this paper, we present a model of traffic demands to support traffic engineering and performance debugging of large Internet Service Provider networks. By de ning a traffic demand as a volume of load originating from an ingress link and destined to a set of egress links, we can capture and predict how routing affects the traffic traveling between domains. To infer the traffic demands, we propose a measurement methodology that combines flow-level measurements collected at all ingress links with reachability information about all egress links. We discuss how to cope with situations where practical considerations limit the amount and quality of the necessary data. Specifically, we show how to infer interdomain traffic demands using measurements collected at a smaller number of edge links -- the peering links connecting to neighboring providers. We report on our experiences in deriving the traffic demands in the AT&T IP Backbone, by collecting, validating, and joining very large and diverse sets of usage, configuration, and routing data over extended periods of time. The paper concludes with a preliminary analysis of the observed dynamics of the traffic demands and a discussion of the practical implications for traffic engineering.

Many routers can generate and export statistics on flows of packets that traverse them. Increasingly, high end routers form flow statistics from only a sampled packet stream in order to manage resource consumption involved.

In this paper we describe an analytical approach for estimating the queuing delay distribution on an Internet link carrying realistic TCP traffic, such as that produced by a large number of finite-size connections transferring files whose sizes are taken from a long-tail distribution.The analytical predictions are validated against detailed simulation experiments and real network measurements.Despite its simplicity, our model proves to be accurate and robust under a variety of operating conditions, and offers novel insights into the impact on the network of long-tail flow length distributions.Our contribution is a performance evaluation methodology that could be usefully employed in network dimensioning and engineering.

This document specifies an Internet standards track protocol for the Internet community, and requests discussion and suggestions for improvements. Please refer to the current edition of the "Internet Official Protocol Standards" (STD 1) for the standardization state and status of this protocol. Distribution of this memo is unlimited. Copyright Notice Copyright (C) The Internet Society (1999). All Rights Reserved. 1.

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. Per-hop behaviours capable of supporting different traffic classes are essential for the provision of quality of service (QoS) on the Internet according to the Differentiated Services model. This paper presents an approach for supporting different traffic classes in IP networks, proposing a new per-hop behaviour called D3 - Dynamic Degradation Distribution. The approach allows for the dynamic distribution of network resources among classes, based on the measured quality of service and on the sensitivity of classes to performance degradation, without implying any substantial change to current IP technologies. A router prototype developed according to the proposed approach is presented, along with the results of experimental tests that were performed. The test results demonstrate the feasibility and the effectiveness of the underlying ideas. 1 Introduction and Framework Differentiated Services (Diffserv) is an architectural framework currently being studied and proposed fo...

As Internet traffic continues to grow exponentially, it is essential for both the users and service providers to have a clear understanding on the performance of the network. There has been many research activities on Internet performance measurement and analysis. In this paper, we first give a concise survey on the research efforts in this area. Our survey findings show that the networking research community has converged to the common understanding that a measurement infrastructure is inevitable for the optimal operation and future growth of the Internet. Despite many proposals on building an Internet measurement infrastructure from the research community, we believe that it will not be in the near future for such an infrastructure to be fully deployed and operational, due to both the scale and the complexity of the Internet. In the meantime, in order to take advantage of the enormous benefits that can be offered from such an infrastructure, we propose to build a measurement infrast...

With the constantly growing usage of the Internet, the need for deployment of value-added IP services has recently yielded a dramatic investigation effort in the field of IP traffic engineering. Traffic engineering can be defined as a collection of techniques that will allow service providers to use the network resources as efficiently as possible, according to the different quality levels that are associated with the range of services they provide. The monitoring activity can play an important role for assisting the operation of traffic engineered networks. This paper explains how to obtain and manage the measurement information which is required by traffic engineering algorithms in dimensioning the network, dynamic resource allocation and route management, and in-service verification of traffic and performance characteristics of value-added IP services. The paper focuses on the description of a monitoring and measurement architecture that is designed within the context of the TEQUILA project, partly funded by the European Commission within the context of IST development program. ########: Monitoring, Measurements, Quality of Service (QoS), Service Level Specification (SLS), Traffic Engineering (TE), Per Hop Behaviour (PHB), Label Switch Path (LSP). 1

IP flows have heavy-tailed packet and byte size distributions. This make them poor candidates for uniform sampling---i.e. selecting # in # flows---since omission or inclusion of a large flow can have a large effect on estimated total traffic. Flows selected in this manner are thus unsuitable for use in usage sensitive billing. We propose instead using a size-dependent sampling scheme which gives priority to the larger contributions to customer usage. This turns the heavy tails to our advantage; we can obtain accurate estimates of customer usage from a relatively small number of important samples. The sampling scheme allows us to control error when charging is sensitive to estimated usage only above a given base level. A refinement allows us to strictly limit the chance that a customers estimated usage will exceed their actual usage. Furthermore, we show that a secondary goal, that of controlling the rate at which samples are produced, can be fulfilled provided the billing cycle is sufficiently long. All these claims are supported by experiments on flow traces gathered from a commercial network. I.