The large-scale behavior of routing in the Internet has gone virtually without any formal study, the exceptions being Chinoy's analysis of the dynamics of Internet routing information [Ch93], and recent work, similar in spirit, by Labovitz, Malan and Jahanian [LMJ97]. We report on an analysis of 40,000 end-to-end route measurements conducted using repeated “traceroutes ” between 37 Internet sites. We analyze the routing behavior for pathological conditions, routing stability, and routing symmetry. For pathologies, we characterize the prevalence of routing loops, erroneous routing, infrastructure failures, and temporary outages. We find that the likelihood of encountering a major routing pathology more than doubled between the end of 1994 and the end of 1995, rising from 1.5 % to 3.3%. For routing stability, we define two separate types of stability, “prevalence, ” meaning the overall likelihood that a particular route is encountered, and “persistence, ” the likelihood that a route remains unchanged over a long period of time. We find that Internet paths are heavily dominated by a single prevalent route, but that the time periods over which routes persist show wide variation, ranging from seconds up to days. About 2/3's of the Internet paths had routes persisting for either days or weeks. For routing symmetry, we look at the likelihood that a path through the Internet visits at least one different city in the two directions. At the end of 1995, this was the case half the time, and at least one different autonomous system was visited 30 % of the time.

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Abstract — Many Internet protocols and operational procedures use measurements to guide future actions. This is an effective strategy if the quantities being measured exhibit a degree of constancy: that is, in some fundamental sense, they are not changing. In this paper we explore three different notions of constancy: mathematical, operational, and predictive. Using a large measurement dataset gathered from the NIMI infrastructure, we then apply these notions to three Internet path properties: loss, delay, and throughput. Our aim is to provide guidance as to when assumptions of various forms of constancy are sound, versus when they might prove misleading. I.

We explore the use of end-to-end multicast traffic as measurement probes to infer network-internal characteristics. We have developed in an earlier paper [2] a Maximum Likelihood Estimator for packet loss rates on individual links based on losses observed by multicast receivers. This technique exploits the inherent correlation between such observations to infer the performance of paths between branch points in the multicast tree spanning the probe source and its receivers. We evaluate through analysis and simulation the accuracy of our estimator under a variety of network conditions. In particular, we report on the error between inferred loss rates and actual loss rates as we vary the network topology, propagation delay, packet drop policy, background traffic mix, and probe traffic type. In all but one case, estimated losses and probe losses agree to within 2 percent on average. We feel this accuracy is enough to reliably identify congested links in a wide-area internetwork. Keywords---Internet performance, end-to-end measurements, Maximum Likelihood Estimator, tomography I.

Telephone call centers are an integral part of many businesses, and their economic role is significant and growing. They are also fascinating socio-technical systems in which the behavior of customers and employees is closely intertwined with physical performance measures. In these environments traditional operational models are of great value – and at the same time fundamentally limited – in their ability to characterize system performance. We review the state of research on telephone call centers. We begin with a tutorial on how call centers function and proceed to survey academic research devoted to the management of their operations. We then outline important problems that have not been addressed and identify promising directions for future research. Acknowledgments The authors thank Lee Schwarz, Wallace Hopp and the editorial board of M&SOM for initiating this project, as well as the referees for their valuable comments. Thanks are also due to L. Brown, A. Sakov, H. Shen, S. Zeltyn and L. Zhao for their approval of importing pieces of [36, 112].

This study seeks to understand how network failures affect the availability of service delivery across wide area networks and to evaluate classes of techniques for improving end-to-end service availability. Using several large-scale connectivity traces, we develop a model of network unavailability that includes key parameters such as failure location and failure duration. We then use trace-based simulation to evaluate several classes of techniques for coping with network unavailability. We find that caching alone is seldom effective at insulating services from failures but that the combination of mobile extension code and prefetching can improve average unavailability by as much as an order of magnitude for classes of service whose semantics support disconnected operation. We find that routing-based techniques may provide significant improvements, but that the improvements of many individual techniques are limited because they do not address all significant categories of network failures. By combining the techniques we examine, some systems may be able to reduce average unavailability by as much as one or two orders of magnitude.

There is much interest in using network measurements for both modeling and operational purposes. In this paper we focus on the fundamental question of the stationarity of such measurements. That is, to what extent are past measurements a good predictor of the future? We used the NIMI infrastructure and a set of public traceroute servers to capture large measurement datasets of three quantities: routing, packet loss, and TCP throughput. We apply statistical tests to attempt to develop sound characterizations of the stationarity of these data sets, and discuss several types of nonstationarity. 1 Introduction In recent years there has been a surge of interest in network measurements. These measurements have deepened our understanding of network behavior and led to more accurate and qualitatively different models of network traffic. Network measurements are also used operationally by various protocols to guide network usage. For instance, RLM [MJV96] and equation-based congestion control...

We measure and analyze the single-hop packet delay through operational routers in a backbone IP network. First we present our delay measurements through a single router. Then we identify stepby -step the factors contributing to single-hop delay. In addition to packet processing, transmission, and queueing delays, we identify the presence of very large delays due to non-work-conserving router behavior. We use a simple output queue model to separate those delay components. Our step-by-step methodology used to obtain the pure queueing delay is easily applicable to any single-hop delay measurements.

In this paper we analyze the packet loss process in a single server queueing system with a finite buffer capacity. The model we use addresses the packet loss probabilities for packets within a block of consecutive sequence of packets. In contrast to other work which used an independence assumption to compute the loss probabilities of packets within a block, we present an analytical approach that yields efficient recursions for the computation of the distribution of the number of lost packets within a block of packets of fixed or variable size for several arrival models and a several number of sessions. Numerical examples are provided to compare the distribution obtained from our analysis with the distribution obtained by using the independence assumption. The results give insight to the following areas related to high-speed networks: (i) Forward error correction schemes become less efficient due to the bursty nature of the packet loss processes. (ii) Real time traffic such as voice and...

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : xii 1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1. The problem : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 2. Overview of thesis : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 3. Contribution of our work : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 2 Taxonomy of traffic characteristics : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 1. Aggregation granularity : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 2. Host versus network centric perspective : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 3. Host centric perspective : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 1. Delay and jitter : : : : : ...