rfc2330.txt

著名的RFC文档,其中有一些文档是已经翻译成中文的的.

   more important, as it can induce systematic trends in packet transit
   times measured by comparing timestamps produced by the two clocks.

   These distinctions arise because for Internet measurement what is
   often most important are differences in time as computed by comparing
   the output of two clocks.  The process of computing the difference
   removes any error due to clock inaccuracies with respect to true
   time; but it is crucial that the differences themselves accurately
   reflect differences in true time.

   Measurement methodologies will often begin with the step of assuring
   that two clocks are synchronized and have minimal skew and drift.
   {Comment: An effective way to assure these conditions (and also clock
   accuracy) is by using clocks that derive their notion of time from an
   external source, rather than only the host computer's clock.  (These
   latter are often subject to large errors.) It is further preferable
   that the clocks directly derive their time, for example by having
   immediate access to a GPS (Global Positioning System) unit.}

   Two important concerns arise if the clocks indirectly derive their
   time using a network time synchronization protocol such as NTP:

 +    First, NTP's accuracy depends in part on the properties
      (particularly delay) of the Internet paths used by the NTP peers,
      and these might be exactly the properties that we wish to measure,
      so it would be unsound to use NTP to calibrate such measurements.
 +    Second, NTP focuses on clock accuracy, which can come at the
      expense of short-term clock skew and drift.  For example, when a
      host's clock is indirectly synchronized to a time source, if the
      synchronization intervals occur infrequently, then the host will
      sometimes be faced with the problem of how to adjust its current,
      incorrect time, Ti, with a considerably different, more accurate
      time it has just learned, Ta.  Two general ways in which this is



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      done are to either immediately set the current time to Ta, or to
      adjust the local clock's update frequency (hence, its skew) so
      that at some point in the future the local time Ti' will agree
      with the more accurate time Ta'.  The first mechanism introduces
      discontinuities and can also violate common assumptions that
      timestamps are monotone increasing.  If the host's clock is set
      backward in time, sometimes this can be easily detected.  If the
      clock is set forward in time, this can be harder to detect.  The
      skew induced by the second mechanism can lead to considerable
      inaccuracies when computing differences in time, as discussed
      above.

   To illustrate why skew is a crucial concern, consider samples of
   one-way delays between two Internet hosts made at one minute
   intervals.  The true transmission delay between the hosts might
   plausibly be on the order of 50 ms for a transcontinental path.  If
   the skew between the two clocks is 0.01%, that is, 1 part in 10,000,
   then after 10 minutes of observation the error introduced into the
   measurement is 60 ms.  Unless corrected, this error is enough to
   completely wipe out any accuracy in the transmission delay
   measurement.  Finally, we note that assessing skew errors between
   unsynchronized network clocks is an open research area.  (See [Pa97]
   for a discussion of detecting and compensating for these sorts of
   errors.) This shortcoming makes use of a solid, independent clock
   source such as GPS especially desirable.


10.2. The Notion of "Wire Time"

   Internet measurement is often complicated by the use of Internet
   hosts themselves to perform the measurement.  These hosts can
   introduce delays, bottlenecks, and the like that are due to hardware
   or operating system effects and have nothing to do with the network
   behavior we would like to measure.  This problem is particularly
   acute when timestamping of network events occurs at the application
   level.

   In order to provide a general way of talking about these effects, we
   introduce two notions of "wire time".  These notions are only defined
   in terms of an Internet host H observing an Internet link L at a
   particular location:

 +    For a given packet P, the 'wire arrival time' of P at H on L is
      the first time T at which any bit of P has appeared at H's
      observational position on L.






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 +    For a given packet P, the 'wire exit time' of P at H on L is the
      first time T at which all the bits of P have appeared at H's
      observational position on L.

   Note that intrinsic to the definition is the notion of where on the
   link we are observing.  This distinction is important because for
   large-latency links, we may obtain very different times depending on
   exactly where we are observing the link.  We could allow the
   observational position to be an arbitrary location along the link;
   however, we define it to be in terms of an Internet host because we
   anticipate in practice that, for IPPM metrics, all such timing will
   be constrained to be performed by Internet hosts, rather than
   specialized hardware devices that might be able to monitor a link at
   locations where a host cannot.  This definition also takes care of
   the problem of links that are comprised of multiple physical
   channels.  Because these multiple channels are not visible at the IP
   layer, they cannot be individually observed in terms of the above
   definitions.

   It is possible, though one hopes uncommon, that a packet P might make
   multiple trips over a particular link L, due to a forwarding loop.
   These trips might even overlap, depending on the link technology.
   Whenever this occurs, we define a separate wire time associated with
   each instance of P seen at H's position on the link.  This definition
   is worth making because it serves as a reminder that notions like
   *the* unique time a packet passes a point in the Internet are
   inherently slippery.

   The term wire time has historically been used to loosely denote the
   time at which a packet appeared on a link, without exactly specifying
   whether this refers to the first bit, the last bit, or some other
   consideration.  This informal definition is generally already very
   useful, as it is usually used to make a distinction between when the
   packet's propagation delays begin and cease to be due to the network
   rather than the endpoint hosts.

   When appropriate, metrics should be defined in terms of wire times
   rather than host endpoint times, so that the metric's definition
   highlights the issue of separating delays due to the host from those
   due to the network.

   We note that one potential difficulty when dealing with wire times
   concerns IP fragments.  It may be the case that, due to
   fragmentation, only a portion of a particular packet passes by H's
   location.  Such fragments are themselves legitimate packets and have
   well-defined wire times associated with them; but the larger IP
   packet corresponding to their aggregate may not.




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   We also note that these notions have not, to our knowledge, been
   previously defined in exact terms for Internet traffic.
   Consequently, we may find with experience that these definitions
   require some adjustment in the future.

   {Comment: It can sometimes be difficult to measure wire times.  One
   technique is to use a packet filter to monitor traffic on a link.
   The architecture of these filters often attempts to associate with
   each packet a timestamp as close to the wire time as possible.  We
   note however that one common source of error is to run the packet
   filter on one of the endpoint hosts.  In this case, it has been
   observed that some packet filters receive for some packets timestamps
   corresponding to when the packet was *scheduled* to be injected into
   the network, rather than when it actually was *sent* out onto the
   network (wire time).  There can be a substantial difference between
   these two times.  A technique for dealing with this problem is to run
   the packet filter on a separate host that passively monitors the
   given link.  This can be problematic however for some link
   technologies.  See [Pa97] for a discussion of the sorts of errors
   packet filters can exhibit.  Finally, we note that packet filters
   will often only capture the first fragment of a fragmented IP packet,
   due to the use of filtering on fields in the IP and transport
   protocol headers.  As we generally desire our measurement
   methodologies to avoid the complexity of creating fragmented traffic,
   one strategy for dealing with their presence as detected by a packet
   filter is to flag that the measured traffic has an unusual form and
   abandon further analysis of the packet timing.}


11. Singletons, Samples, and Statistics

   With experience we have found it useful to introduce a separation
   between three distinct -- yet related -- notions:

 +    By a 'singleton' metric, we refer to metrics that are, in a sense,
      atomic.  For example, a single instance of "bulk throughput
      capacity" from one host to another might be defined as a singleton
      metric, even though the instance involves measuring the timing of
      a number of Internet packets.
 +    By a 'sample' metric, we refer to metrics derived from a given
      singleton metric by taking a number of distinct instances
      together.  For example, we might define a sample metric of one-way
      delays from one host to another as an hour's worth of
      measurements, each made at Poisson intervals with a mean spacing
      of one second.






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 +    By a 'statistical' metric, we refer to metrics derived from a
      given sample metric by computing some statistic of the values
      defined by the singleton metric on the sample.  For example, the
      mean of all the one-way delay values on the sample given above
      might be defined as a statistical metric.

   By applying these notions of singleton, sample, and statistic in a
   consistent way, we will be able to reuse lessons learned about how to
   define samples and statistics on various metrics.  The orthogonality
   among these three notions will thus make all our work more effective
   and more intelligible by the community.

   In the remainder of this section, we will cover some topics in
   sampling and statistics that we believe will be important to a
   variety of metric definitions and measurement efforts.


11.1. Methods of Collecting Samples

   The main reason for collecting samples is to see what sort of
   variations and consistencies are present in the metric being
   measured.  These variations might be with respect to different points
   in the Internet, or different measurement times.  When assessing
   variations based on a sample, one generally makes an assumption that
   the sample is "unbiased", meaning that the process of collecting the
   measurements in the sample did not skew the sample so that it no
   longer accurately reflects the metric's variations and consistencies.

   One common way of collecting samples is to make measurements
   separated by fixed amounts of time: periodic sampling.  Periodic
   sampling is particularly attractive because of its simplicity, but it
   suffers from two potential problems:

 +    If the metric being measured itself exhibits periodic behavior,
      then there is a possibility that the sampling will observe only
      part of the periodic behavior if the periods happen to agree
      (either directly, or if one is a multiple of the other).  Related
      to this problem is the notion that periodic sampling can be easily
      anticipated.  Predictable sampling is susceptible to manipulation
      if there are mechanisms by which a network component's behavior
      can be temporarily changed such that the sampling only sees the
      modified behavior.
 +    The act of measurement can perturb what is being measured (for
      example, injecting measurement traffic into a network alters the
      congestion level of the network), and repeated periodic
      perturbations can drive a network into a state of synchronization
      (cf. [FJ94]), greatly magnifying what might individually be minor
      effects.



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   A more sound approach is based on "random additive sampling": samples
   are separated by independent, randomly generated intervals that have
   a common statistical distribution G(t) [BM92].  The quality of this
   sampling depends on the distribution G(t).  For example, if G(t)
   generates a constant value g with probability one, then the sampling
   reduces to periodic sampling with a period of g.

   Random additive sampling gains significant advantages.  In general,
   it avoids synchronization effects and yields an unbiased estimate of
   the property being sampled.  The only significant drawbacks with it
   are:

 +    it complicates frequency-domain analysis, because the samples do
      not occur at fixed intervals such as assumed by Fourier-transform
      techniques; and
 +    unless G(t) is the exponential distribution (see below), sampling
      still remains somewhat predictable, as discussed for periodic
      sampling above.


11.1.1. Poisson Sampling