Toward a Framework for Defining Internet Performance Metrics

Vern Paxson <>
Network Research Group
Lawrence Berkeley National Laboratory, USA

1. Introduction

The Internet is a huge collection of interoperating networks, with 9.5 million computers at last count. As such, it represents a triumph of standardization, since it is only through standardization that so many different networks using so many different designs can smoothly exchange data. The standardization of Internet measurement, however, has not matched the explosive growth of the network as a whole. Even such basic notions as how to measure the throughput or delay along an Internet path lack a standardized framework.

To address this shortcoming, the Benchmarking Methodology Working Group of the Internet Engineering Task Force has created an IP Provider Metrics (IPPM) effort aimed at developing such a framework. One of the main goals of this effort is to provide a basis for evaluating the performance of different Internet components, particularly "IP clouds" that provide Internet connectivity to external networks in an opaque fashion. Such standardized performance evaluations can serve a number of needs:

In this paper we outline how such a measurement framework might be developed. The discussion is necessarily heavy on terminology, since a large element of successful standardization is unambiguous descriptions of the standards. We also emphasize that the discussion here is preliminary. This version of the paper is only a draft of the underlying ideas, and does not reflect any standardization produced by the IPPM, though the intent is to influence that standardization process. If interested in specific details, the reader is urged to refer instead to the revised version of this paper, which will be available by the end of June 1996.

The discussion proceeds as follows. We first define a number of terms, both those concerning the Internet in general (Section 2) and those concerning the measurement framework (Section 3). In the latter we develop the key notions of metric as the fundamental property we wish to measure, methodology as a way to attempt to measure the property, and measurement as the result of a specific application of a methodology.

We then develop a basic contrast between formal metrics (Section 4), which emphasize viewing network properties in analytic terms, and empirical metrics (Section 5), which correspond to properties that are generally too complex to discuss analytically but still very important for practical measurement. Each has its place in the framework (Section 6).

Section 7 discusses the notion of "composition"--how a property we wish to measure might be fruitfully viewed in terms of a collection of simpler, underlying properties. We follow this with a discussion in Section 8 of the crucial issues of measurement errors and uncertainties; these in general cannot be avoided and must instead be quantified whenever possible if we are to make sound measurements. We then turn to a high-level discussion of different measurement "strategies" (Section 9) including a taxonomy of methodologies as "passive" or "active," and whether they require "soft" or "hard" cooperation (or no cooperation at all). These facets of methodologies directly influence some of the errors inherent in the methodology, and also the ease of deploying the methodology in the Internet. This discussion leads in turn to a proposal for how one might architect a "measurement infrastructure" for the Internet (Section 10), which could provide for sophisticated measurements in a relatively inexpensive (and realistic) fashion. We also discuss what sort of factors could lead to widespread deployment of the infrastructure.

We finish with proposed formalisms for defining Internet metrics and methodologies (Section 11), illustrated with an example of defining an "Internet route" metric and accompanying methodology based on traceroute . As with the rest of this paper, these are intended not as final standards but to stimulate further discussion.

2. Some basic terms

In this and the next section we define terminology used in the remainder of the discussion. This section covers basic networking terms not specific to measurement, and the next session covers measurement-specific terms.

Internet host or host

A computer capable (if all is working properly) of communicating using the Internet protocols. Includes routers.


The link-level abstraction of a "virtual direct connection" between two or more Internet hosts. Often thought of in terms of a single underlying physical connection, though it need not be so realized.


An Internet host that facilitates communication between other Internet hosts by forwarding packets from one link to another.

Internet path or path

The network-level abstraction of a "virtual link" from Internet host A to Internet Host B. That is, the Internet Protocol (IP) makes it appear to higher levels as though A has a direct connection to B. This apparent direct connection is a "path." When used in the context "the path from A to B" it is a unidirectional concept. When used in the context "the path between A and B" it is a bidirectional concept.


A sequence of links and routers comprising a unidirectional Internet path.

IP Cloud or Cloud

A collection of routers viewed as a "black box." Packets enter the cloud at well-defined entry points and later exit at well-defined exit points, if they were not dropped by the cloud.

Autonomous System or AS

A collection of Internet hosts and routers controlled by a single administrative authority that is responsible for internal routing (between any hosts within the AS).

3. Some measurement terms

We begin by defining the general notion of an "Internet component" as any element of an Internet network whose properties we wish to quantify. An Internet component may for example be a single computer such as a router, a large collection such as the links and routers comprising an IP cloud, or a service group such as a Network Operations Center (NOC).

We refer to different properties of an Internet component as metrics, with the term implying the use of standardized units when quantifying the metric. Quantified values of metrics as termed measurements. So, for example, a metric of a router might be its forwarding rate, defined as the number of packets per second it can receive from one link and send out on another link. A measurement of the forwarding rate metric might be "123,444 packets per second."

Unless care is taken, the notion of "metric" can prove quite slippery. For example, the above definition of forwarding rate neglects to mention the size of the packets being forwarded. This may or may not be relevant. If the router copies each incoming packet to a temporary buffer, then that copying might dominate its processing time, and the size of the packets is relevant. If however the router copies incoming packets but does so in parallel with the forwarding lookup, and if the lookup takes longer than copying a maximal packet, then the packet size does not matter.

Equally important when attempting to measure metrics is the surrounding context of the measurement. We define a measurement's context as those elements of the complete system used to make the measurement that are in addition to the component being measured.

For example, a router fed by a single T1 circuit will never be able to forward faster than 1.544 Mbps, since that is the upper bound on the rate of arriving packets. If the minimal sized packet is 64 bytes, then the measured forwarding rate will be at most 3,015 packets per second, regardless of the speed of the router's internals. Furthermore, suppose the router does forwarding lookups in parallel, as discussed above, and that these lookups exceed the cost of the internal copying. The measured forwarding rate will still vary according to the packet sizes, because the incoming T1 circuit can deliver fewer large packets per second than small ones. In general, understanding the effects of context is crucial for making accurate measurements.

We term a process for quantifying a measurement for a metric as a "methodology." A methodology is only sound if it takes into account all the relevant effects of the measurement context. We refer to a methodology that produces erroneous results as "unsound." Sometimes the methodology is not at fault but instead the definition of the metric, such as the example above concerning the need (sometimes) to include packet size in the router forwarding rate metric. When a metric's definition fails to include pertinent factors like these, we refer to it as being "ill-defined."

It is valuable to distinguish between two basic types of metrics, "formal" and "empirical." Formal metrics refer to those defined in terms of the theoretical, abstract properties of the components. These are the properties used to analyze the component mathematically. Empirical metrics, on the other hand, refer to properties directly defined by measurement. Each type of metric plays an important role in network measurement, and each type has its advantages and disadvantages. We expand on both types of metric in the next two sections.

4. Formal metrics

As discussed above, formal metrics are those that view a component in terms of its abstract, mathematical properties. Some examples are:

Physical propagation time of a link

The time in seconds required for a signal to travel on a physical link from host X to host Y. For a fiber optic link, this would for example be the time required for a photon to travel from X's network interface to Y's.

Propagation time of a link

The time difference in seconds between when host X on the link begins sending 1 bit to host Y, and when host Y has received the bit. Different from the physical propagation time because it may take more than one "signal" to convey a bit.

Transmission time of a link

The time required to transmit B bits from X to Y, as opposed to 1 bit.

Bandwidth of a physical link

A link's maximum data-carrying capacity, measured in bits per second.

Bandwidth of a network link

A network link's data-carrying capacity, measured in bits per second, where "data" does not include those bits needed solely for link-layer headers. For an ATM link using AAL5 encapsulation, this metric would be 48 bytes/cell times the cell rate. For links with variable sized transmission units, this metric is ill-defined unless a transmission size is also specified.

Bottleneck link bandwidth of a network path

For a given Internet path from A to B, the bandwidth of the slowest link in the chain of links comprising the path. Note that here we have defined a fairly "high-level" metric, but it remains a formal metric because it is an analytic property of the component (the network path).

Instantaneous route of a network path

The sequence of links and routers comprising the path from A to B at a given instant in time. Here the "units" might be IP addresses of the traversed router interfaces, with the links between them being implicit (but see Section 11 for problems with this definition).

Hop count of a route

How many routers a packet from A to B will visit along a particular route. (We might want to know this number in order to compute store-and-forward delays, for example.)

Buffer size of a router

How many bytes the router has available for buffering queued packets. In practice, the buffering might be specific to the outgoing interface, or it might be shared between the different interfaces, or it might be different for different flows or types of flows. These differences are often crucial, and illustrate some of the difficulties of devising well-defined formal metrics.

Instantaneous queue size of a router interface

At a given moment, the number of bytes consumed at a router by packets queued for transmission on a particular interface.

Maximum flow capacity of an IP cloud

For a given entry and exit point, the greatest transmission rate achievable, in bytes per second, if we had all of the links and routers in the cloud at our disposal (so we could send our data along multiple, parallel routes).

Hurst parameter of a network link

The instantaneous value of the Hurst parameter measuring the strength of correlations in the link's aggregate traffic over time (see [LTWW94]). Note that, theoretically, this value is not supposed to be merely instantaneous but in fact to remain invariant over time scales of minutes to hours, but we would not want to build that assumption into the definition of its metric.

Instantaneous connectivity of an Internet path

Whether at a particular instant host A is able to send IP datagrams to host B. (The units here are Boolean.)

Epoch connectivity of an Internet path

Whether over a given interval of S seconds starting at time T host A is able to send any IP datagrams to host B and have B receive one or more with non-zero probability. Note that this is significantly different from the previous definition. At any given moment, if an Internet path includes a router with completely full buffers, then there is no instantaneous connectivity along that path; but there may well be epoch connectivity during an interval containing that instant.

Maximum jitter along an Internet path

The maximum amount of variation, measured in seconds, that packets sent from A to B might experience in their end-to-end transmission time.

Availability of an Internet path

The unconditional probability that for any T second interval host A will have epoch connectivity to host B. Clearly a function of T. Since the metric is defined as a probability, it is unit-less.

Mean NOC turn-around time

The expectation of the amount of time in seconds between when a trouble ticket is submitted to a NOC and when the problem is resolved. An empirical version of this metric would be "the average amount of time historically taken by a NOC to resolve a trouble ticket."Note that as phrased above, this metric is not conditioned on the type of trouble ticket. Clearly certain types of problems will be resolved more quickly than others. This shortcoming highlights the great degree of latitude that must be surveyed before standardizing performance metrics--as it is defined above, this metric is probably almost worthless.

These examples illustrate the wide scope of formal metrics. Indeed, any parameter in a mathematical analysis of an Internet component immediately lends itself to a formal metric. We are not advocating any of the above as necessarily good formal metrics, just as examples, and it is important to keep in mind the utility of having a minimal set of useful metrics rather than trying to define every possible interesting metric. Similarly, while the emphasis in this section is on precise, analytic notions of metrics, we do not want the framework for Internet performance metrics to become bogged down in unwieldy formalism. But we believe that an emphasis on formal metrics can play a crucial role in shaping measurement methodologies so that they achieve both generality and relevance for the future.

5. Empirical metrics

In contrast to formal metrics are empirical measurements, which are defined directly in terms of a measurement methodology. One example was given in the previous section, a metric of "the average amount of time historically taken by a NOC to resolve a trouble ticket." Here the measurement methodology is to record a running average of the time taken by the NOC to resolve each trouble ticket, and this number is also the empirical metric.

Another example is "the output of the traceroute program run on host A with the argument B." One might initially think that this empirical metric is the same as "Instantaneous route of a network path" discussed in the previous section, but it is not: The empirical metric is defined by a particular program (methodology), which actually does not measure the instantaneous route of a network path but only a close approximation to it. In a situation like this we would argue that it is better to think of the traceroute program as a measurement methodology for assessing a formal metric, rather than as directly producing an empirical metric, as that way the notion of "close approximation but not exact" is preserved (see Section 11).

These first two examples of empirical metrics might appear to merely reflect pedantic hair-splitting. If these examples were all that one could do in terms of empirical metrics, the notion of empirical metric would not be worth developing. To illustrate its utility, we now turn to a more complex example.

Consider the very pragmatic question of how much throughput a user can expect to achieve across an IP cloud. Here we mean that the user has a data source transmitting at one of the cloud's entry points, and a data sink at an exit point, and wants to know how fast they can move data from the source to the sink.

We might initially think that this problem fits well into the formal metric framework: The formal metric of interest is "IP cloud throughput," and it is defined as "the number of bytes per second that can be transmitted from a given entry point A of an IP cloud to a given exit point B." While this metric appears to capture the desired notion (by definition), it turns out this metric is not a useful concept, when expressed as a formal metric. The reasons are as follows.

We assume that the user wants reliable transmission across the IP cloud, as is often the case. Generally, the way to do this is to use the TCP protocol. TCP, quite wisely, has built into it notions of congestion avoidance, in which it effectively adapts its transmission rate to current network conditions. The exact algorithms used for the adaptation are subtle and vary between different implementations. Thus, the throughput the user can expect to see across the IP cloud is heavily shaped by both the TCP software they are using (at source and sink; these two may be different) and the current network conditions in the cloud. Predicting how the cloud's network conditions will interact with the TCP implementations remains an open research area.

Thus we are in a quandary if we want to tackle throughput measurement using formal metrics: The definition of the formal throughput metric does not give us any insight into how to accurately measure it.

Other measurement communities, when confronted with the need to measure complex systems, have used the benchmark approach, in which some standardized applications are used to stress the system, and the system's performance in executing the benchmark is then used as a metric for how well the system performs in general. With benchmarks, two key difficulties are the relevance of the benchmark to what the user really wants to know (that is, how their application will perform), and the related possibility of systems being artificially tuned to perform well on specific benchmarks without a subsequent gain for more general applications. Nevertheless, when a more analytic description of a system's performance metrics remains elusive, benchmarks can provide valuable tools.

For measuring user throughput, one fruitful approach might be to use a "state-of-the-art" TCP implementation to transfer data across an IP cloud and carefully measure the resulting throughput (and the factors that contributed to it). Here "state of the art" refers to the best current practices both for high-performance TCP and for congestion control. An example would be the treno tool, which attempts to do just this, and is described in [MM96]. We could then define an empirical throughput metric as something like "the throughput reported by version X.Y of the treno tool when run with the following arguments . . ." (Even this definition is problematic because, for example, the tool could produce different measurements under identical network conditions due to other context factors such as host clock resolution.)

Thus, in our taxonomy, empirical metrics correspond to benchmarks, while formal metrics reflect an attempt to evaluate the system in more analytically fundamental terms.

6. Formal vs. empirical metrics

Formal metrics have a number of appealing properties. Foremost of these is that they include the possibility of developing an analytic framework for understanding different aspects of Internet behavior. Such a framework should not be sold short: Without it, keeping the Internet functioning, improving its performance, and extending it for future traffic become seat-of-the-pants engineering problems, likely to prove disastrous when applied to such a huge system. Empirical metrics are much more likely to prove difficult to compose (see below) or to generalize how they will be affected by changes in network parameters.

Formal metrics are also easier to define than empirical metrics. The latter necessarily can only be specified using a program, with all the attendant possibilities of hard-to-spot bugs in the definition, while the former can be specified in analytic terms.

A key problem with formal metrics, however, relates to exactly this easier form of definition. At first blush it might seem straightforward to define a formal metric, but often the underlying notions prove slippery. Consider the following subtleties associated with the formal metric defined above:

Transmission time of a link

The time required to transmit B bits from X to Y.

What if the link uses data compression (such as some modems do)? Then the transmission time for B bits depends on the bits being sent. The formal metric might be redefined as "B zero bits," for example. This new definition is more well defined, but still perhaps not too useful for measuring modem links, because the zero bits might compress extremely well, while "real-world" data might not. Another definition would be "B random bits," where presumably the randomization defeats any compression. This metric might better reflect real-world data (or might not, depending on the sophistication of the compression scheme). It is however a more cumbersome definition, since we must now introduce a suitable notion of "random."

Related to this example, formal metrics present two distinct problems. The first is whether the metric is well defined, meaning that its definition includes all of the relevant particulars necessary for capturing the notion of interest. The second is that, even if it is well defined, it may be significantly removed from the real-world notion of interest. It is easy to overlook that an analytic notion does not match a real-world notion, such as transmission time in the face of possible compression. If a formal metric fails in either of these ways, then not only is the metric not useful, but it may well mislead us because its failure is subtle.

Empirical metrics can suffer from these defects, but in a different way. An empirical metric being ill defined occurs if the program defining the metric has a bug in it. Given that nontrivial programs in general always contain bugs, we perhaps must resign ourselves to the fact that our empirical metrics will generally be ill defined, though we hope only in ways that are for the most part insignificant. Empirical metrics can also be misleading if they do not match the real-world notions of interest (see the discussion of benchmarking above), but this flaw is likely to be less common than in the case of formal metrics, because formal metrics are in general further removed from the real world.

A final problem with formal metrics is that even when the metric is well defined and correctly reflects a "real-world" notion, it may be difficult to measure the formal metric. Consider the formal metric defined above:

Instantaneous route of a network path

The sequence of links and routers comprising the path from A to B at a given instant in time.

The best-known methodology for measuring this metric is the traceroute utility [Ja89]. traceroute, however, elicits the network route one hop at a time. If, while hop N is being discovered, hop K changes, then this change will generally be missed, and the sequence of hops reported by traceroute will not reflect the instantaneous route as it was when the measurement began (or even, possibly, when it ended). Thus, this formal metric is hard to measure using current tools, limiting its practical utility. (See Section 11 for further discussion.) Empirical metrics do not have this problem, since the measurement methodology by definition accurately measures the metric.

7. Composing metrics

Many interesting metrics can be viewed as compositions of simpler metrics. For example:

Transmission time of a link

The time required to transmit B bits from X to Y

is often viewed as a combination of:

Propagation time of a link

The time difference in seconds between when host X on the link begins sending 1 bit to host Y, and when host Y has received the bit


Bandwidth of a physical link

A link's data-carrying capacity measured in bits per second.

If the propagation time is P and the bandwidth is rho then the transmission time T for B bits is viewed as T = P + B/rho.

A significant advantage of formal metrics is the possibility of defining such compositions. With empirical metrics, it is more difficult to know how two separate measurements compose, because the underlying meaning of the measurements is more complex.

With composition comes the possibility of introducing errors. As illustrated in the previous section, if the network link of interest uses compression, then the definition of T above is erroneous. Instead, we have to think of T as a function of both B and of the pattern of those B bits. Note that neither P nor rho offer an opportunity to introduce the notion of compression: P doesn't because it's defined in terms of a single bit, and rho doesn't because it's defined in terms of physical data-carrying capacity, while compression occurs at a higher layer.

Related to the possibility of introducing errors, composition also offers an opportunity for detecting problems in the definition of a metric. If, when defining a new metric, one attempts as much as possible to define it as a composition of existing metrics, one can often see problems with the new definition (or perhaps the existing ones), because the composition doesn't quite work. Thus, thinking in terms of composition provides a valuable self-consistency check that we would like to always apply to a suite of metrics to keep it sound.

Errors and uncertainties

In general we do not want to discard a measurement methodology because of imperfections, because measurement is almost always imperfect, even in a digital world. Instead, we ask that a methodology analyze its sources of measurement error and quantify the corresponding effects.

For example, many methodologies include a measurement of elapsed time, necessitating the use of some form of clock. All clocks have imperfections. The clocks in computers are sometimes poorly kept simply due to operating system deficiencies or hardware shortcomings. Thus for any measurement involving a clock we would like to know how the clock's inaccuracies affect the measurement. In general this analysis best takes a form like: "A clock resolution of +/- t microseconds translates into a measurement error of +/- x%."

Another common source of timing error, more difficult to quantify, is that arising from measurement overhead occurring on the computer making the measurement, as opposed to delays due to the network component being measured. The former is a measurement error, while the latter is generally related to the metric of interest. We note that one technique often valuable in reducing host-related measurement errors is the use of a packet filter (ideally running on a separate machine) that records all of the pertinent network traffic with high timing accuracy. The trace produced by the filter can then be analyzed to assess exactly when the network traffic occurred, minimizing the effect of measurement host delays, or at least allowing them to be accounted for.

Finally, just as defining metrics by composition (Section 7) provides an opportunity to debug the metric's definition, it also provides an opportunity for considering how errors and uncertainties compose. Naturally a goal is then to choose a definition that minimizes the propagation and escalation of errors. The general question of how errors propagate during composition has been long studied by numerical analysts in general, and proponents of interval arithmetic in particular--see [Kn81] for an overview.

9. Measurement strategies

In this section we discuss different types of measurement methodologies. We term each type a "measurement strategy" because the different types take different high-level approaches to measurement.

Many methodologies are active, meaning that part of the measurement process is to generate new network traffic. This traffic might be to elicit a special response from network components (e.g., traceroute), or to see what sort of performance the network provides for the traffic (e.g., treno). There are two drawbacks to active methodologies: They add potentially burdensome load to the network, especially if the methodology is not carefully designed to minimize the amount of traffic generated, and they can suffer from "Heisenberg" effects, in which the additional traffic perturbs the network and biases the resulting analysis. For example, if the methodology for measuring bottleneck link bandwidth within an IP cloud is to time huge ttcp transfers, then the resulting additional traffic may congest the path through the cloud to the point where packets are dropped, and the measured throughput is appreciably lower than the bottleneck link bandwidth (we have, unfortunately, observed researchers doing this).

An alternative type of methodology is to make passive measurements, in which existing network traffic is recorded and analyzed. Because packet filters can capture network traffic without any perturbative effects (if they have local disks for recording the traffic), it is possible using passive techniques to completely eliminate both additional traffic load and Heisenberg effects. These are major advantages, leading us to prefer passive techniques to active ones. On the other hand, for many metrics it is exceedingly difficult to see how one might measure them passively: for example, determining the route taken by packets. The benefits of passive monitoring are in some cases sufficiently large, however, that it behooves us to think carefully before eliminating passive approaches as an option. For example, if what we care about are not complete Internet routes but merely inter-AS routes, then we actually can measure the routes passively if we are able to monitor traffic between two BGP peers, since over time that traffic contains full inter-AS routing information.

For other types of measurements, passive techniques are directly preferred due to the nature of the metric. For example, the Hurst parameter mentioned above (Section 4) relates to the correlations in a link's traffic over time. To measure such a metric using active techniques would be both hard (because the metric is a property of the aggregate traffic on the link) and error-prone (because of Heisenberg effects), while measuring it passively is relatively straight-forward.

One drawback to passive techniques that is vital to address is ensuring privacy and security [Ce91]. Because most Internet traffic is sent unencrypted, passive measurement programs will often have the potential to capture sensitive traffic. One major step in addressing these concerns is the recently written tcpdpriv program [Mi96], which takes packets captured by the popular tcpdump program [JLM89] and removes or scrambles a configurable amount of the information present in them, including source and destination hosts and ports, and packet contents. By incorporating tcpdpriv into passive measurement methodologies, the privacy and security concerns can largely be addressed satisfactorily.

Another axis of measurement strategy concerns measurement location. Some measurements can be done at a single point, without any external cooperation by other network components: For example, to estimate a Hurst parameter a program running on a single computer could passively monitor the traffic on a link and perform the necessary computations to form its estimate.

Most measurements, however, and certainly all active measurements, require at least some form of participation by multiple network components. For example, the ping program for estimating the round-trip time from host A to B requires that host B respond to ICMP "ECHO request" messages [Po81a,Po81b].

Several forms of such cooperation are already present and widely deployed in the Internet, such as response to certain ICMP requests, generation of "Time exceeded" messages by Internet routers, and the near ubiquity of "anonymous" FTP servers, which allow throughput measurements between a host A and a remote site S (even if not allowing a throughput measurement to a particular host B at site S). We will term such cooperation as "soft cooperation," indicating that it is easy to come by.

Other forms of cooperation are not widespread and in general require special arrangements to perform the corresponding measurements. For example, to estimate the route corresponding to the Internet path from A to B requires only soft cooperation, namely that the intervening routers support the generation of "Time exceeded" messages, and that the endpoint B supports the generation of "UDP port unreachable" messages. But to determine whether the route is symmetric, that is, the same from B to A as from A to B, requires making a routing measurement from A to B and then making one from B to A. The cooperation of host B is "hard:" If host B does not provide the means for making the second measurement, and in general it does not without prior agreement with the administrator of B, then the measurement cannot be made.

Methodologies requiring only soft cooperation are naturally to be preferred to those requiring hard cooperation, because the former can be applied on a much wider basis than the latter.

We should note, however, that sometimes a metric can be measured using two different methodologies, one requiring only soft cooperation and one requiring hard cooperation, with the tradeoff being ease of widespread applicability vs. accuracy of measurement. For example, one way to estimate the one-way transmission time T1 from host A to host B is to measure the round-trip time T from A to B and back to A, using a tool such as ping, and then to apply the assumption of symmetric routing and divide the result by two. This technique introduces considerable uncertainty into the measurement of T1, because of the symmetric routing assumption (and, in reality, routes tend not to be symmetric--see [Pa96a]). Another technique, requiring hard cooperation, is to time the departure of the packet from A and its arrival at B using synchronized clocks, and then using that direct measurement (plus the error due to imperfect synchronization of the clocks) in estimating T1.

The line between soft and hard cooperation is not fixed but fluid over time, as new network services become more widely deployed. For example, we are presently advocating for the addition to routers of a "fast timestamp" option, in which upon receiving a particular ICMP request, the router generates a high-precision timestamp which it sends in response. If such functionality becomes widely deployed, then a number of throughput measurements presently requiring hard cooperation for high accuracy will become possible with soft cooperation. If the routers were to run with globally synchronized clocks (not so outrageous to contemplate today as in the past, with the growing availability of inexpensive GPS receivers) then the one-way transmission time measurement discussed above could be done using only soft cooperation.

Finally, the drawbacks of hard-cooperation methodologies would be lessened if "measurement platforms," as described in the next section, become deployed. In that case, even though network components at large might not support a particular methodology, if the much smaller set of measurement platforms does then the methodology effectively requires only soft cooperation.

10. Measurement infrastructure

Many of the most interesting measurements are those of the performance of large IP clouds. These are the measurements that potentially have the largest effect on the most people, by providing the necessary information to help network service providers diagnose faults, understand how their capacity is being used, determine how to plan for future growth, and assess their performance versus that of their competitors (as well as allowing third parties to do so).

Another very interesting class of measurements concerns those of traffic over what have historically been referred to as "backbone links": highly aggregated high-speed links used to carry large volumes of Internet traffic over large distances. Such traffic measurements can prove bountiful for answering key network research questions.

For example, the case for "self-similar" network traffic, which has profound implications for networking performance, has been made most solidly for local area networks and for links connecting "stub" networks to the Internet backbone. It has proven harder to make for "backbone links" (where it would have the most serious implications, but also where conditions might be sufficiently different from the other locations that the phenomenon might instead be diminished), because of the great difficulty in attaining measurements from backbone links.

Another example concerns Internet performance in the post-NSFNET world. There is widespread anecdotal evidence that Internet performance in the large has significantly degraded since the National Science Foundation ceased to manage the core of the network (measurement evidence that routing has degraded appears in [Pa96a]), and widespread speculation that the degradation is principally due to the growth of the World Wide Web. Traffic traces from backbone links would shed invaluable light on this issue, which has major implications for Internet engineering.

We envision providing for both IP cloud measurement and backbone link measurements by developing a "measurement infrastructure," something like the following. The infrastructure would consist of a number of cooperating "measurement platforms." Each platform would be a single host, or perhaps a pair of hosts (to address some of the measurement inaccuracies discussed in Section 8), dedicated to performing Internet measurements. Access to the platforms would be controlled by the administrative entity hosting the platform, which would also own the platform and define the usage and privacy policies for making measurements using the platform. This entity might, for example, be a network service provider, hosting a platform at its border with another provider; or a government entity such as the NSF, as part of a contract to buy service from a provider.

The platforms would be designed to support a wide variety of network measurement (again controlled by the administrative entity). Some of the measurement might be passive, for example support for the network research questions discussed above. Other measurement might be active, both for soft-cooperation methodologies such as traceroute's to arbitrary Internet locations, and hard-cooperation methodologies, usually involving other measurement platforms. Two cooperating platforms on either side of an IP cloud would serve admirably for performing a number of different measurements of the cloud's performance.

A key point concerning the measurement infrastructure is the "N squared" effect: Every time a new platform is added to the infrastructure, the number of Internet paths now conducive to sophisticated, hard-cooperation measurement methodologies grows by N, the total number of platforms (or 2N, since each path can be measured in two different directions). Overall, if the infrastructure is comprised of N platforms, on the order of N*N different paths can be probed. This sort of scaling property has an attractive "snowball" effect: As more sites add platforms, the utility of the overall infrastructure increases superlinearly, making it considerably more appealing for still more sites to participate.

Most likely such a "snowball" can only come about using both "carrot" and "stick" approaches: Network service providers need to find hosting the measurement platforms both to their own benefit, because of the utility of being able to better analyze their networks, and an economic necessity, because customers will demand the ability to better measure the service they are receiving. The ability for customers to compare the service offered by different providers in turn will create the necessary market incentives for the providers to optimize their networks.

See [Pa96a] for a discussion of a measurement experiment carried out using a crude measurement infrastructure approach, which for N=34 was able to probe a significant number of different routes through the Internet.

11. Formalisms

We finish with a proposal for a formal structure for defining new metrics and measurement methodologies. As mentioned in the introduction, this paper reflects work-in-progress, so this proposal is both incomplete and subject to change. We present it here as a starting point to encourage discussion.

The definition of a performance metric should include:

The definition of a methodology should include:

We envision performance metric and methodology definitions undergoing the same standardization process as other Internet standards: That is, formal definitions are developed using the "RFC" process [HG94].

Here is an example of a formally defined performance metric:


"Instantaneous route of a network path"

Underlying notion

Whenever packets are sent from an Internet host A to another host B, each packet traverses on its journey a sequence of Internet links and routers. We term this sequence the route from A to B. The particular route affects the time required for the journey; the bandwidth available at the moment from A to B; the congestion levels and available queuing buffer encountered by the packets; and the administrative domains encountered by the packets (with implications for billing, troubleshooting, privacy, and security).

We are interested in defining the route at the IP network layer, so our definition should not allow the notion that the component bits of a single IP packet might encounter different routes--at the network layer, the smallest routable quantity is a full IP packet.

The route from A to B may change over time, on time scales as short as from one packet to the next, so it is important to introduce the notion of an "instantaneous" snapshot of the route. Care must be taken with this notion, however. If "instantaneous" is taken to mean: "view the routing tables at a single instant in time and compute the route taken by a packet from A to B," then the definition is flawed as follows. It may be that at a single instant in time the route from A to B is comprised of the interface addresses A, R1, B, but that if a packet were actually sent from A to R1, by the time it completely reaches R1, the route may have changed to be A, R2, R1, R3, B. Thus the true route the packet will take is A, R1, R3, B, different from the instantaneous routes both when it was sent and when it reached R1.

Instead we use the following definition: "The route taken by a packet sent at time T from A to B." It may be that the packet does not reach B, either for connectivity reasons (no route available to B at that instant), or for buffer reasons (an intermediary router has a full queue). We define the first case as a "connectivity failure," and the second as a "congestive failure," and the route is then defined as the IP addresses traversed up to the point of failure. If the packet fails to reach B due to damage in transit, we define the failure as "transmission failure," and for any other reason as "unspecified failure."

Note that by considering failure modes in more detail, we encounter the notion that the packet from A to B should be "well formed"--its checksum should be correct, and it should have a sufficient TTL to reach B. This latter notion becomes suggestive for the "Traceroute" methodology defined below.

Related metrics

Any end-to-end metric needs to consider the possible effects upon the metric of the instantaneous route. For example, a definition of end-to-end throughput only makes sense in the context of the route taken by the packets being sent. Because this route can change rapidly, all such end-to-end metrics must consider a notion of the time interval over which the metric is defined, and how the metric can be defined in the presence of multiple routes.

Expressed as a composition

The route could be viewed as the concatenation of a number of "hops" from A to B. Viewing the route in this fashion highlights how end-to-end routes are implicitly embedded in a global snapshot of routing tables distributed throughout the Internet.


A route is expressed as a series of IP addresses, corresponding to the inbound and outbound router interfaces visited by the packet whose travel defines the route. Includes the outbound interface of the sending host and the inbound interface of the receiving host.

Measurement issues

While the metric includes the notion of "instantaneous," in practice the route generally cannot be determined for an instant but only for a particular window in time. This uncertainty leads to the possibility of ambiguities, such as discussed above, where the measured route differs from the true route. The difference may also be difficult to detect.

Because routes can change frequently [Pa96a], one must take care with the common presumption that a measurement at time T is likely to serve also for time T+S for small S.

It may be difficult to determine the addresses of both the inbound and outbound interfaces visited. For many situations, a single address corresponding to the router with the interfaces suffices.

Corresponding methodology

See the "Traceroute" methodology below.

Known problems

As presently defined, the definition does not include the possibility that different types of packets will be routed differently.

Future work

A related metric of the instantaneous route from A to B for a packet of type T (see previous item); metrics for the stability of routes over time (see [Pa96a] for a discussion of defining "stability"); metrics for quantifying connectivity outages.

Here is an example of an accompanying, formally defined methodology:



Corresponding metric

"Instantaneous route of a network path," defined above, corresponding to the IP addresses of the inbound and outbound interfaces visited by a packet sent at time T from host A to host B.


The first assumption below is required for the methodology to generate any useful information. The remaining assumptions are required for the methodology to return a single, consistent, end-to-end route. If they are violated, the metric will still be partially measured.

Routers generate ICMP "Time exceeded" messages [Po81b] identifying the router when they receive an IP packet with an expired TTL (time to live).

Routers decrement TTLs once per hop and not additionally once per second of queuing, per [Ba95].

Packets addressed to a random UDP port on host B with sufficient TTL will be conveyed all the way to B, and will there elicit a "UDP port unreachable" ICMP message from B.

Routes do not change on time scales of seconds to a few minutes.

The Internet diameter does not exceed 30 hops.

How the methodology works

The heart of "Traceroute" is the requirement that Internet routers generate ICMP "Time exceeded" messages when they receive a packet with an expired TTL. The router identifies itself in these messages using one of its IP addresses (preferably the one associated with the interface on which the packet was received). By sending a stream of packets from A to B with TTL 1, 2, ..., n, the sending host should receive a series of ICMP replies from the hop-1, hop-2, ..., hop-n routers. The ICMP replies include the first 8 data bytes of the IP packet that elicited the reply. The replies can be paired to the original TTL by encoding that TTL in a UDP header (as the destination port). Since the UDP header is only 8 bytes long, it will be returned in the ICMP reply. This allows the methodology to unambiguously associate replies from different routers to different TTLs (hence hop counts).

The Traceroute methodology consists of sending, one packet at a time, three packets with a TTL of one, three with a TTL of two, and so on, collecting ICMP replies, until one set of three packets elicits one or more "UDP port unreachable" ICMP replies, indicating that the corresponding packet possessed a sufficient TTL to reach host B, or until a total of 30 hops have been measured. After each packet is sent, the methodology is to wait up to five seconds for an ICMP reply before sending the next packet.


The Traceroute methodology should elicit a response from each router in the series of hops from A to B, provided that the route from A to B does not change between the time the measurement begins and the time it ends. The methodology cannot control which IP address the routers use to identify themselves in their ICMP replies, so it cannot determine both the inbound and outbound IP address, as defined in the route metric. But the replies should include some sort of valid IP address for the router, allowing an approximation to the metric of the form "the routers visited from A to B."

Measurement errors and uncertainties

The methodology can fail in several ways. The ICMP reply from a particular router might itself be dropped by the network, either due to a congestive failure or a connectivity failure along the reverse path. Such a drop is indistinguishable from a failure along the forward path. Thus, a missing reply is ambiguous. Some routers also limit the rate at which they generate ICMP messages, as allowed by [Ba95], and replies not sent due to rate-limiting are difficult to distinguish from failures. Other routers generate poorly formed replies, which may not be received by host A due to problems such as insufficient TTLs. (See [Pa96b] for further discussion.)

To address these difficulties, the Traceroute methodology sends three packets for each hop being measured, with the hope that a reply will be received for at least one of the three packets. The packets are sent serially; after sending the first packet, it waits up to five seconds for a reply before sending the next packet. If no replies were received for a particular hop, that hop is reported as unknown.

The Traceroute methodology keeps only one packet in flight at any given time. While this has the positive effect of reducing network load, it also widens the time interval over which the measurement is made. If the route changes during that interval, it may or may not be apparent using the methodology. If the change occurs upstream from the hop currently being measured, then a discontinuity may occur. For example, hop five may be reported from the initial route, and hop six from the changed route, and there may actually be no link between the router at hop five and the router at hop six, but the methodology is unable to detect this measurement error.

One way of diminishing errors is to perform two measurements back-to-back. If both report the same measured route, then most likely no change occurred during either measurement interval, and the route is indeed the instantaneous route as defined in the metric. There is no guarantee, however, that this is the case--if the route is rapidly changing between two different sequences of addresses, S1 and S2, then two back-to-back measurements might both observe S1, and the fact that many packets actually take S2 will remain undetected.

Another Traceroute assumption is that routers only decrement TTLs once per hop, and not additionally once per second of forwarding delay, which is allowed (and was originally intended) [Po81a,Ba95]. If a router decrements a TTL by more than one, then the measured route will potentially show that router, or a downstream router, twice. It is believed, however, that current and likely future practice in Internet routers is to never decrement the TTL by more than one [Ba95].

Another assumption is that packets addressed to a random UDP port on host B with sufficient TTL will be conveyed all the way to B. In practice, sometimes Internet "firewalls" will drop such packets (or the corresponding ICMP replies). In such cases, the Traceroute methodology is unable to measure the portion of the route beyond the firewall.

A final assumption is that the Internet diameter is no more than 30 hops. This assumption is incorrect [Pa96a], and when the route being measured exceeds 30 hops, the methodology only elicits the first 30 hops. This failure can be detected by the fact that the 30th hop reported does not correspond to an IP address for host B.

Context issues

If host B happens to have UDP services on the random ports chosen by traceroute for its final set of packets (those with sufficient TTL to reach B), then no ICMP UDP port unreachable reply will be forthcoming, and the methodology will erroneously conclude that host B is unreachable. This sort of failure should be quite rare if the measurement is made with non-reserved UDP ports.


The methodology is active. The measurement can only be done at host A or on another host A' connected to the same shared network as A, with the assumption that packet forwarding for A' is identical to that for A.

The methodology requires only soft cooperation, in the form of ICMP Time exceeded (and UDP port unreachable) messages. If this cooperation is lacking, the corresponding hop(s) cannot be measured.

Insights shed on the formal definition of the metric

The formal definition was written after considerable experience with the Traceroute methodology, which proved essential for introducing the notions of "instantaneous" and the suggestion of a related metric for the route taken by a packet of type T.

New metrics or methodologies suggested

Measurement error could be reduced by sending packets for the different hops out simultaneously rather than serially, though such an approach increases the burstiness of the load generated by the measurement, and so might increase congestive losses and actually lose accuracy. The IP "Record Route" option [Po81a] offers the possibility of recording multiple hops with a single measurement packet. This would reduce load and improve accuracy (by reducing the measurement window), but requires a form of hard cooperation, since Internet routers are not required to process the Record Route option.

Reference implementation

Available from:


Many thanks to Kevin Fall and Sally Floyd for their comments on this draft, and to Guy Almes, Kim Claffy, Jamshid Mahdavi, and Matt Mathis for discussions about that various issues.



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J. Postel, "Internet Control Message Protocol," RFC 792, Network Information Center, SRI International, Menlo Park, California, September 1981.