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Source-Level Traffic Generation

While packet-level traffic generation based on a set of statistical properties is convenient for the experimenter, and attractive from a mathematical point of view, it fails to preserve an essential property of Internet traffic. As Floyd and Paxson [PF95] point out, packet-level traffic generation is open-loop, in the sense that it does not preserve the feedback loop that exists between the sources of the traffic (the endpoints) and the network. This feedback loop comes from the fact that endpoints react to network conditions, and this reaction itself can change these conditions, and therefore trigger further changes in the behavior of the endpoints. For example, TCP traffic reacts to congestion by lowering its sending rate, which in turn decreases congestion. A trace of packet arrivals collected at some given link is therefore specific to the characteristics of this link, the time of the tracing paths of the connections that traversed it, etc. Therefore, any changes that the experimenter makes to the experimental conditions make the packet-level traffic invalid since the traffic generation process is insensitive to these changes (unlike real Internet traffic). For example, packet-level replay of TCP traffic does not react to congestion in any manner.

The solution is to model the sources of traffic, i.e., to model the network behavior of the applications running on the endpoints that communicate using network flows. Source-level models are then used to drive network stacks which do implement flow and congestion control mechanisms, and therefore react to changes in network conditions as real Internet endpoints do. As a result, the generated traffic is closed-loop, which is far more realistic for a wide range of experiments.

The simplest source-level model is the infinite source model. The starting point of the infinite source model is the availability of an infinite amount of data to be communicated from one endpoint to another. Generating traffic according to this model means that a traffic generator opens one or more transport connections, and constantly provides them with data to be transferred. This means that, for each connection, one of the endpoints is constantly writing (sending data packets) while the other endpoint is constantly reading (receiving data packets). The sources are never the bottleneck in this model. The only process that limits the rate at which the endpoints transmit data is the network, broadly defined to include any mechanism below the sources, such as TCP's maximum receiver window.

The infinite source model is very attractive for several reasons, which make it rather popular in both theoretical and experimental studies [FJ93b,KHR02,AKM04,SBDR05]. First, the infinite source model has no parameters and hence it is easy to understand and amenable to formal analysis. It was, for example, the foundation for the work on the mathematical analysis of steady-state TCP throughput [PFTK98,BHCKS04]. Second, its underlying assumption is that the largest flows on the network, which account for the majority of the packets and the bytes, ``look like'' infinite sources. For example, an infinite source provides a convenient approximation to a multi-gigabyte file download using FTP. Third, infinite sources are well-behaved, in the sense that, if driving TCP connections, they try to consume as much bandwidth as possible. They also result in the ideal case for bandwidth sharing. This makes them useful for experiments in the area of congestion control, since infinite sources can easily congest network links.

Despite their convenience, infinite sources are unrealistic and do not provide a solid foundation for networking experiments, or even for understanding the behavior and performance of the Internet. The pioneering work by Cáceres et al. [CDJM91], published as early as 1991, provided a first insight into the substantial difference between infinite sources and real application traffic. These authors examined packet header traces from three sites (the University of California at Berkeley, the University of Southern California, and Bellcore in New Jersey) using the concept of application-level conversations. An application-level conversation was defined as the set of packets exchanged between two network endpoints. These conversations could include one or more ``associations'' (TCP connections and UDP streams). A general problem when studying traffic for extended periods is the need to separate traffic into independent units of activity, which in this case correspond to conversations. Endpoints may exchange traffic regularly, say every day, but that does not mean that they are engaged in the same conversation for days. Danzig et al. separated conversations between the same endpoints by identifying long periods without any traffic exchange, which are generally referred to as idle times or quiet times in the literature. In their study, they used a threshold of 20 minutes to differentiate between two conversations. The authors examined conversations from 13 different applications, characterizing them with the help of empirical cumulative distribution functions (empirical CDFs). The results include empirical CDFs for the number of bytes in each conversation, the directionality of the flow of data (i.e., whether the two endpoints sent a similar amount of data), the distribution of packet sizes, the popularity of different networks, etc. Danzig and Jamin [DJ91] used these distributions in their traffic generation tool, tcplib. The results from this work are further discussed in Section 2.2.2.

Cáceres et al. pointed out a number of substantial differences between their results and the assumptions of earlier works. First, the majority of connections carried very small amounts of data, less than 10 KB in 75-90% of the cases. This is true for both interactive applications (e.g., telnet and rlogin) and bulk transfer applications (e.g., FTP, SMTP). This is in sharp contrast to the infinite availability of data to be transferred assumed in the infinite source model. The dynamics of such short data transfers are completely different from those of infinite sources, which for example have time to fully employ congestion control mechanisms. The second difference was that traffic from most applications was shown to be strongly bidirectional, and it included at least one request/response phase, i.e., an alteration in the role of the endpoints as senders of data. The infinite source model is inherently unidirectional, with one of the endpoints always acting as the sender, and the other endpoint always acting as the receiver. Third, the authors observed a wide range of packet sizes, and a large fraction of the data packets were small, even for bulk applications. Data packets from an infinite source are necessarily full size, since there is by definition enough data to completely fill new packets.

These measurement results highlighted a substantial difference between infinite sources and real traffic, and later experimental studies demonstrated the perils of using traffic from infinite sources in the evaluating of network mechanisms. Joo et al. [JRF$^+$99,JRF$^+$01] demonstrated that infinite TCP sources tend to become synchronized, so they increase or decrease their transmission rate at the same time. This pattern is completely absent from more realistic experiments in which the majority of the sources have small and diverse amounts of data to send. As a result, loss patterns, queue lengths and other characteristics are strikingly different when more realistic synthetic traffic is used. Joo et al. also studied the difference between open-loop and closed-loop traffic generation.

The area of active queue management has provided several illustrations of the misleading results obtained with the unrealistic infinite sources. The first scheme, , was presented by Floyd and Jacobson in [FJ93b], and evaluated using infinite sources. Their results showed that RED significantly outperformed , the usual router queuing mechanism. Later work by Christiansen et al. [CJOS00] demonstrated that RED offers very little benefit, if any, when exposed to more realistic traffic where sources are not infinite. In particular, they used a model of web-like traffic, which is discussed later in this chapter.

Paxson's analysis [Pax94] of packet header traces from seven different network links provided further support for the conclusions of Cáceres et al. In addition, Paxson considered the parsimonious modeling of traffic from different applications. He characterized four prominent applications, telnet, NNTP, SMTP and FTP, using analytic models to fit the empirical distributions. Analytic models are more commonly known as parametric models in the statistical literature, and correspond to classical distributions, such as the Pareto distribution, that can be fully characterized with a mathematical expression and only one or a few parameters. As Paxson pointed out, the use of analytic models results in a concise description of network applications that can be easily communicated and compared, and are often mathematically tractable. His methodology has had a lasting influence in application-level modeling. He clearly demonstrated that analytic fits (i.e., parametric models) of the observed distributions can closely approximate the characteristics of real applications. However, it is important to remember that traffic is not necessarily more realistic when generated by analytic models as opposed to empirical models. Empirical CDFs, derived from network measurement of sufficient size, provide a perfectly valid foundation for traffic generators. Furthermore, finding analytic fits of complex random variables that do not match well-known statistical distributions is a daunting task.

Web Traffic Modeling

Modeling web traffic has received substantial attention since the sudden emergence of the World Wide Web in the mid-nineties. Arlitt and Williamson [AW95] proposed an early model for generating web traffic2.1, based on packet header traces collected at the University of Saskatchewan. The model was centered around the concept of a conversation, as proposed by Cáceres et al. [CDJM91]. In this case, a conversation was the set of connections observed between a web browser and a web server. These authors were the first to consider questions such as the distribution of the number of bytes in requests and responses, the arrival rates of connections, etc. In general, the proposed model has parameters that are quite different from those of later works. For example, an Erlang model of response sizes was used, which is in sharp contrast to the heavy-tailness observed by other authors. While Arlitt and Williamson did not provide any details on the statistical methods they employed, it is likely that the small sample size (less than 10,000 TCP connections) made it difficult to develop a more statistically representative model.

One of the major efforts in the area of web traffic modeling oriented toward traffic generation took place at Boston University. Cunha et al. [CBC95] examined client traces collected by instrumenting browsers at the Department of Computer Science. Unlike the packet header traces used in Arlitt and Williamson, client traces include application information such as the exact URL of each web object requested and downloaded in each TCP connection. The authors made use of this information to study page and server popularity, which are relevant for web caching studies. In addition, the authors proposed the use of powerlaws for constructing a parametric model of web traffic. They relied on the Pareto distribution for modeling the sizes of downloaded objects, and the parameterless Zipf's law for modeling the popularity of specific pages. Crovella and Bestavros [CB96] used these findings to explain the long-range dependence observed in the packet arrivals of web traffic. Their explanation was derived from earlier work by Willinger et al. [WTSW97], which showed that the multiplexing of heavy-tailed ON/OFF sources results in long-range dependent traffic. Crovella and Bestavros demonstrated that the underlying distributions of web object sizes, the effects of caching and user preference in file transferring, the effect of user ``think time'', and the superimposition of many web transfers precisely creates the multiplexing process hypothesized by Willinger et al.

Crovella and Bestavros also showed that the explanation behind the suitability of powerlaws for describing the sizes of web objects is that the sizes of files are well described by powerlaws. This refined previous studies of file-system characteristics (e.g., [BHK$^+$91]), which observed long-tailed distributions of file sizes (but did not propose powerlaw models).

Powerlaw modeling has had a lasting impact on traffic modeling, which is natural given that the transfer of files is one of the most common uses of many application protocols. Countless studies have confirmed the usefulness of powerlaws for modeling application traffic. The eloquent term ``mice and elephants'' [GM01,MHCS02,EV03], often applied to Internet traffic, precisely refers to the basic characteristic of powerlaws: a majority of values are small (mice) but the uncommon large values (elephants) are so large that they account for a large fraction of the total value. For example, web traffic usually shows around 90% of web objects below 10 KB, but larger objects often account for 90% of the total bytes. Researchers have used this general finding of powerlaw sizes to develop a generic, and mostly ad hoc, source-level model. Traffic generated according to this model consists of a collection of TCP connections that transfer a single file, such that the distribution of file sizes follows a powerlaw. Researchers often refer to this kind of synthetic traffic as ``mice-and-elephants-like'' or ``web-like'' traffic [MGT00,KHR02]. This simple approach is rather convenient for traffic generation, but it ignores the more complex patterns of connection usage (e.g., bidirectionality, quiet times, etc.), and the differences among applications present in real Internet traffic.

It is important to note that recent work on the characterization of web traffic has improved our understanding of powerlaw/heavy-tailed modeling. Downey revisited the modeling of file sizes in [Dow01b] and of flow sizes in [Dow01a], suggesting that lognormal distributions are more appropriate than powerlaws (or heavy-tailed distributions). The historical survey by Mitzenmacher [Mit04] uncovered similar controversies in other fields, such as economics and biology. Hernández-Campos et al. demonstrated that lognormal distributions and powerlaws offer similar results in the regions of the distribution for which enough samples are available, specifically in the body and in the ``moderate'' tail. Beyond these regions, in the ``far'' tail, the lack of samples makes it impossible to choose between different models. This is because, for a fixed set of parameters and a fixed sample size equal to the original number of observations, some samplings of the lognormal and the powerlaw models match the original distribution, while other samplings do not. Hernández-Campos et al. also proposed the use of a mixture model (i.e., a combination of several classical models), the double Pareto lognormal, which enables far more accurate fits than those achieved with Pareto or lognormal models. The inherently more flexible double Pareto lognormal model can capture the systematic deviations from simpler models that are commonly observed in the tails of the distributions of web object sizes. Nuzman et al. [NSSW02] modeled HTTP connection arrivals using the biPareto distribution, which provides a simpler but powerful alternative to mixture models. A Pareto distribution appears linear in a log-log scale, while the biPareto distribution shows two linear regions and a smooth transition between them. The biPareto distribution is therefore a generalization of the Pareto distribution.

The modeling efforts at Boston University culminated with the development of the SURGE model of web traffic [BC98]. The SURGE model described the behavior of each user as a sequence of web page downloads and think times between them. Each web page download consisted of one or more web objects downloaded from the same server. Barford and Crovella provided parametric fits for each of the components of the SURGE model, heavily relying on powerlaws and other long-tailed distributions. They also studied how SURGE traffic stressed web servers, and found SURGE's high burstiness far more demanding in terms of server CPU performance than that of less elaborate web traffic generators, such as the commercial WebStone.

A model of web traffic contemporary to SURGE was also presented by Mah [Mah97]. It described web traffic using empirical CDFs, which were derived from the analysis of packet header traces. As in the case of the SURGE model, the data came from the population of users in a computer science department. The two models were compared by Hernández-Campos et al. [HCJS03], showing substantial consistency.

The introduction of persistent connections in HTTP motivated further work on web traffic modeling. Barford et al. studied the performance implications of persistent connections [BC99], and modified the SURGE model to incorporate persistency [BBBC99]. The analysis of persistent connections was also a major topic in Smith et al. [SHCJO01] and Hernández-Campos et al. [HCJS03]. These studies were far larger in scope, focusing on the web traffic of an entire university rather than of a single department. These latter two works provided the starting point for the analysis method presented in this dissertation.

Many experimental studies made use of synthetic traffic generated according to one of the aforementioned web traffic models. For example, Christiansen et al. [CJOS00] made use of the Mah model, while Le et al. [LAJS03] used the Smith et al. model. The popular NS-2 [BEF$^+$00] network simulator also supports web traffic generation using models that are structurally similar to the SURGE model. This feature of NS was used in Joo et al. [JRF$^+$99,JRF$^+$01] to compare web traffic and infinite sources, and by Feldmann et al. [FGHW99] to study the impact of different parameters of the web traffic model on the burstiness of the generated traffic. Another web traffic generator available in NS-2 was developed by Cao et al. [CCG$^+$04]. Unlike other web traffic models, it was connection-oriented rather than user-oriented, and included non-source-level characteristics, such as packet sizes.

An important effort in web traffic analysis and generation was ``Monkey See, Monkey Do'' method, developed by Cheng et al. [CHC$^+$04a]. The method involved recording source-level and network-level characteristics for each observed connection, and reproducing these characteristics using a synthetic workload generator. This idea is similar to the one developed in this dissertation, although we tackle the modeling and generation of entire traffic mixes and not just web traffic. In addition, their measurement methods were optimized for monitoring traffic near Google's web servers. The authors assumed independent short flows, data acquisition close to well-provisioned web servers, and no congestion in the client-to-server direction (which was plausible in the context of requests that were far smaller than responses).

Non-Web Traffic Source-level Modeling

Two prominent source-level modeling efforts took place before the invention of the World Wide Web. Danzig and Jamin [DJ91] developed tcplib, a collection of source-level descriptions of traffic. It included descriptions of the following applications:

Tcplib also included a model of phone conversations with two random variables, talk spurt duration and quiet time (i.e., pause) duration, borrowed from [Bra65]. Each random variable was specified using an empirical CDF. Traffic generation involved using the inverse transformation method [Jai91] to sample each empirical CDF independently.

In general, the application models in tcplib were rather simplistic, but they represented a giant step forward from the non-measurement-derived models of the early 90s. However, the use and capabilities of the modeled applications has dramatically changed since the development of tcplib. For example, the size of attachments in SMTP connections has dramatically increased due to the widespread implementation of . In addition, newer applications have become prominent or replaced the ones in tcplib. For example, the Telnet protocol has been mostly replaced by the protocol. SSH is an encrypted protocol, so it requires more bytes per message. It also supports port forwarding, wherein other applications can communicate through SSH connections.

Paxson [Pax94] studied the same four applications as in tcplib, developing parametric models for each of them. Paxson also discussed how application characteristics change over time and across sites. This inherent variability motivated the use of parametric models, which are necessarily approximations of the empirical data. This approximation is not worse than the variability observed over time and across sites, so the author argued that parametric models were as accurate as empirical ones, but with the added benefits of being mathematically tractable and parsimonious. His analysis showed that bulk-transfer sizes were generally well-modeled by the log-normal distribution. Another of his findings was that connection inter-arrivals (except those of NNTP connections) were consistent with non-homogeneous Poisson arrivals, with fixed hourly rates.

The methodological contribution in Paxson's work is substantial. He demonstrated the difficulty of providing statistically valid parametric models of the distributions associated with Internet traffic. He consistently observed parametric fits that were clearly adequate when examined graphically, but that failed traditional goodness-of-fit tests. This was caused by the massive sample sizes, an endemic characteristics of traffic measurement datasets. As an alternative to the statistical tests, Paxson proposed the use of a goodness-of-fit metric, which provides a quantitative assessment of the distance between the empirical data and the parametric model. His proposed metric is however insensitive to deviations in the tails, casting doubt on the approach due to the ubiquitous finding of heavy-tailed phenomena in network traffic.

Web traffic quickly dominated most traffic mixes after its emergence in 1995, and remained the most prominent traffic type until file-sharing applications surpassed it in recent years. This motivated a large body of work on web traffic characterization, and little attention was paid to other traffic. The models developed by Danzig, Jamin and Paxson, were not improved or updated by other researchers.

File-sharing applications currently rival or frequently surpass web traffic in terms of traffic volume. They also represent a harder modeling problem than web traffic. The number of file-sharing applications is large and they use widely different communication strategies. Furthermore, the set of popular file-sharing applications is constantly changing. There is a growing body of traffic modeling literature focusing on file-sharing applications, but no traffic generator is yet available. Two prominent modeling studies were conducted at the University of Washington. Sariou et al. [SGG02] studied Napster and Gnutella traffic, and Gummadi et al. [GDS$^+$03] studied Kazaa traffic. Karagiannis et al. [KBBkc03] examined a larger set of file-sharing applications in backbone links.

Modeling of multimedia traffic has also received some attention. Variable bit-rate video was studied in Garret et al. and Knightly et al. [GW94,KZ97]. Real Audio traffic was studied by Mena and Heidemann [MH00], providing a first source-level view of streaming-media, mostly on UDP flows.

There are commercial synthetic traffic generation products such as Chariot [Inc] and IXIA but these generators are typically based on a limited number of application source types. Moreover, it is not clear that any are based on empirical measurements of Internet traffic.

Beyond Single Application Modeling

The need for more representative traffic generation has motivated research on methods that can tackle the modeling of the entire suite of applications using an Internet link. The work in this dissertation lies in this area. Our preliminary steps were an extension of the methods used to model web traffic in Smith et al. [SHCJO01] to model other applications, as described in Hernández-Campos et al. [HCJS$^+$01]. The same kind of analysis of TCP header sequence numbers, acknowledgment numbers and connection quiet times applied to web traffic was used to populate models of SMTP and NNTP traffic. These models were derived from packet header traces collected at the University of North Carolina at Chapel Hill, and consisted of empirical distributions capturing different source-level characteristics of these protocols, such as object sizes. Lan and Heidemann [LH02] conducted a related effort, reusing the same techniques and software tools for data acquisition. Their RAMP tool populated models of web and FTP traffic directly from packet header traces, and generate traffic accordingly.

Harpoon [SB04] also tackled the same problem that is the focus of this dissertation. They considered the problem of analyzing entire traffic mixes and generating traffic accordingly. Their measurement methods were far less elaborate. Rather than the detailed models of the ADU exchange in TCP connections used in our work, Harpoon focused on modeling flows. Flows are defined as sets of packets with the same source and the same destination. As a consequence, Harpoon modeled each TCP connection as two unidirectional flows. Another difference with our approach is that Harpoon did not incorporate the notion of bidirectional data exchange, neither sequential nor concurrent, essentially treating multiple ADUs (as defined in the a-b-t model) as a single ADU. Idle times within connections were not part of the Harpoon traffic model either. In addition, any measured flow (i.e., one side of a connection) with only a small amount of data or with only acknowledgment packets was not used for traffic generation. This substantially simplified the modeling, but it eliminated the rich packet-level dynamics observed in TCP connections, and demonstrated in later chapters of this dissertation. In addition to this, network-level parameters were not part of the data acquisition, so round-trip times and maximum receiver window sizes were arbitrarily chosen. Harpoon could also generate UDP traffic. The underlying model was to send packets at a constant bit rate, with either fixed or exponentially distributed interval arrivals. These models were not populated from measurement. Another novel feature of Harpoon was the ability to generate traffic that reproduced IP address structure according to a measured distribution of address frequency. Their study included a comparison between Harpoon's closed-loop traffic and traffic from a commercial (open-loop) packet-level traffic generator, demonstrating substantial differences. For example, closed-loop sources were shown to back off as congestion increases, while open-loop source did not. Like the work in this dissertation and Lan and Heidemann, Harpoon provided an automated method to acquire data and use it to generate traffic, which Sommers and Barford eloquently called ``self-tuning'' traffic generation. We could say that there is a growing consensus in the field of traffic generation regarding the need to develop tools that combine measurement and generation to tackle the wide variability over time and across links found in real Internet traffic.

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Next: Scaling Offered Load Up: Related Work Previous: Packet-Level Traffic Generation   Contents

Doctoral Dissertation: Generation and Validation of Empirically-Derived TCP Application Workloads
© 2006 Félix Hernández-Campos