 1/draftietfrmcatsbd00.txt 20150701 09:15:00.375180820 0700
+++ 2/draftietfrmcatsbd01.txt 20150701 09:15:00.415181804 0700
@@ 1,22 +1,22 @@
RTP Media Congestion Avoidance D. Hayes, Ed.
Techniques University of Oslo
InternetDraft S. Ferlin
Intended status: Experimental Simula Research Laboratory
Expires: November 9, 2015 M. Welzl
+Expires: January 2, 2016 M. Welzl
University of Oslo
 May 8, 2015
+ July 1, 2015
Shared Bottleneck Detection for Coupled Congestion Control for RTP
Media.
 draftietfrmcatsbd00
+ draftietfrmcatsbd01
Abstract
This document describes a mechanism to detect whether endtoend data
flows share a common bottleneck. It relies on summary statistics
that are calculated by a data receiver based on continuous
measurements and regularly fed to a grouping algorithm that runs
wherever the knowledge is needed. This mechanism complements the
coupled congestion control mechanism in draftwelzlrmcatcoupledcc.
@@ 28,21 +28,21 @@
InternetDrafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as InternetDrafts. The list of current Internet
Drafts is at http://datatracker.ietf.org/drafts/current/.
InternetDrafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use InternetDrafts as reference
material or to cite them other than as "work in progress."
 This InternetDraft will expire on November 9, 2015.
+ This InternetDraft will expire on January 2, 2016.
Copyright Notice
Copyright (c) 2015 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(http://trustee.ietf.org/licenseinfo) in effect on the date of
publication of this document. Please review these documents
@@ 53,48 +53,49 @@
described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3
1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . . 3
1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . . 4
2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
 2.1. Parameter Values . . . . . . . . . . . . . . . . . . . . . 5
 3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 6
 3.1. Key metrics and their calculation . . . . . . . . . . . . 7
 3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . . 7
 3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 8
 3.1.3. Variance Estimate . . . . . . . . . . . . . . . . . . 9
 3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 9
 3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 10
 3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 10
 3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 10
 3.2.2. Using the flow group signal . . . . . . . . . . . . . 12
 3.3. Removing Noise from the Estimates . . . . . . . . . . . . 12
 3.3.1. Oscillation noise . . . . . . . . . . . . . . . . . . 12
 3.3.2. Clock drift . . . . . . . . . . . . . . . . . . . . . 13
 3.3.3. Bias in the skewness measure . . . . . . . . . . . . . 14
 3.4. Reducing lag and Improving Responsiveness . . . . . . . . 14
 3.4.1. Improving the response of the skewness estimate . . . 15
 3.4.2. Improving the response of the variance estimate . . . 15
 4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 16
 4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 16
 5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 16
 6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16
 7. Security Considerations . . . . . . . . . . . . . . . . . . . 16
 8. Change history . . . . . . . . . . . . . . . . . . . . . . . . 17
 9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 17
 9.1. Normative References . . . . . . . . . . . . . . . . . . . 17
 9.2. Informative References . . . . . . . . . . . . . . . . . . 17
 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 18
+ 2.1. Parameters and their Effect . . . . . . . . . . . . . . . 5
+ 2.2. Recommended Parameter Values . . . . . . . . . . . . . . . 7
+ 3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 7
+ 3.1. Key metrics and their calculation . . . . . . . . . . . . 9
+ 3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . . 9
+ 3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 9
+ 3.1.3. Variability Estimate . . . . . . . . . . . . . . . . . 10
+ 3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 11
+ 3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 11
+ 3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 12
+ 3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 12
+ 3.2.2. Using the flow group signal . . . . . . . . . . . . . 13
+ 3.3. Removing Noise from the Estimates . . . . . . . . . . . . 13
+ 3.3.1. PDV noise . . . . . . . . . . . . . . . . . . . . . . 14
+ 3.3.2. Oscillation noise . . . . . . . . . . . . . . . . . . 14
+ 3.3.3. Clock skew . . . . . . . . . . . . . . . . . . . . . . 15
+ 3.4. Reducing lag and Improving Responsiveness . . . . . . . . 15
+ 3.4.1. Improving the response of the skewness estimate . . . 16
+ 3.4.2. Improving the response of the variability estimate . . 16
+ 4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 17
+ 4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 17
+ 5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 17
+ 6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17
+ 7. Security Considerations . . . . . . . . . . . . . . . . . . . 17
+ 8. Change history . . . . . . . . . . . . . . . . . . . . . . . . 18
+ 9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 18
+ 9.1. Normative References . . . . . . . . . . . . . . . . . . . 18
+ 9.2. Informative References . . . . . . . . . . . . . . . . . . 18
+ Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 19
1. Introduction
In the Internet, it is not normally known if flows (e.g., TCP
connections or UDP data streams) traverse the same bottlenecks. Even
flows that have the same sender and receiver may take different paths
and share a bottleneck or not. Flows that share a bottleneck link
usually compete with one another for their share of the capacity.
This competition has the potential to increase packet loss and
delays. This is especially relevant for interactive applications
@@ 124,25 +125,25 @@
Endtoend delay measurements include noise from every device along
the path in addition to the delay perturbation at the bottleneck
device. The noise is often significantly increased if the roundtrip
time is used. The cleanest signal is obtained by using OneWayDelay
(OWD).
Measuring absolute OWD is difficult since it requires both the sender
and receiver clocks to be synchronised. However, since the
statistics being collected are relative to the mean OWD, a relative
 OWD measurement is sufficient. Clock drift is not usually
 significant over the time intervals used by this SBD mechanism (see
 [RFC6817] A.2 for a discussion on clock drift and OWD measurements).
 However, in circumstances where it is significant, Section 3.3.2
 outlines a way of adjusting the calculations to cater for it.
+ OWD measurement is sufficient. Clock skew is not usually significant
+ over the time intervals used by this SBD mechanism (see [RFC6817] A.2
+ for a discussion on clock skew and OWD measurements). However, in
+ circumstances where it is significant, Section 3.3.3 outlines a way
+ of adjusting the calculations to cater for it.
Each packet arriving at the bottleneck buffer may experience very
different queue lengths, and therefore different waiting times. A
single OWD sample does not, therefore, characterize the path well.
However, multiple OWD measurements do reflect the distribution of
delays experienced at the bottleneck.
1.1.3. Path Lag
Flows that share a common bottleneck may traverse different paths,
@@ 156,20 +157,22 @@
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
Acronyms used in this document:
OWD  One Way Delay
PDV  Packet Delay Variation
+ MAD  Mean Absolute Deviation
+
RTT  Round Trip Time
SBD  Shared Bottleneck Detection
Conventions used in this document:
T  the base time interval over which measurements are
made.
N  the number of base time, T, intervals used in some
@@ 172,95 +175,143 @@
T  the base time interval over which measurements are
made.
N  the number of base time, T, intervals used in some
calculations.
sum_T(...)  summation of all the measurements of the variable
in parentheses taken over the interval T
sum(...)  summation of terms of the variable in parentheses

sum_N(...)  summation of N terms of the variable in parentheses
+
sum_NT(...)  summation of all measurements taken over the
interval N*T
E_T(...)  the expectation or mean of the measurements of the
variable in parentheses over T
 E_N(...)  The expectation or mean of the last N values of the
+ E_N(...)  the expectation or mean of the last N values of the
variable in parentheses
 E_M(...)  The expectation or mean of the last M values of the
+ E_M(...)  the expectation or mean of the last M values of the
variable in parentheses, where M <= N.
max_T(...)  the maximum recorded measurement of the variable in
parentheses taken over the interval T
min_T(...)  the minimum recorded measurement of the variable in
parentheses taken over the interval T
num_T(...)  the count of measurements of the variable in
parentheses taken in the interval T
num_VM(...)  the count of valid values of the variable in
parentheses given M records
 PC  a boolean variable indicating the particular flow was
 identified as experiencing congestion in the previous
 interval T (i.e. Previously Congested)
+ PC  a boolean variable indicating the particular flow
+ was identified as experiencing congestion in the
+ previous interval T (i.e. Previously Congested)
 CD_T  an estimate of the effect of Clock Drift on the mean
 OWD per T
+ skew_est  a measure of skewness in a OWD distribution.
 CD_Adj(...)  Mean OWD adjusted for clock drift
+ var_est  a measure of variability in OWD measurements.
 p_l, p_f, p_pdv, c_s, c_h, p_s, p_d, p_v  various thresholds
 used in the mechanism.
+ freq_est  a measure of low frequency oscillation in the OWD
+ measurements.
 N, M, and F  number of values (calculated over T).
+ p_l, p_f, p_pdv, p_mad, c_s, c_h, p_s, p_d, p_v  various
+ thresholds used in the mechanism
2.1. Parameter Values
+ M and F  number of values related to N
+
+2.1. Parameters and their Effect
+ T T should be long enough so that there are enough packets
+ received during T for a useful estimate of short term mean
+ OWD and variation statistics. Making T too large can limit
+ the efficacy of PDV and freq_est. It will also increase the
+ response time of the mechanism. Making T too small will make
+ the metrics noisier.
+
+ N & M N should be large enough provide a stable estimate of
+ oscillations in OWD and average PDV. Usually M=N, though
+ having M mean_delay)) / num_T(OWD)
+ skew_base_T = sum_T(OWD < mean_delay)  sum_T(OWD > mean_delay)
where
if (OWD < mean_delay) 1 else 0
if (OWD > mean_delay) 1 else 0
 skew_est_T is a number between 1 and 1
+ and mean_delay does not include the mean of the current T
+ interval.
 skew_est = E_M(skew_est_T) = sum_M(skew_est_T) / M
+ skew_est = sum_MT(skew_base_T)/num_MT(OWD)
 For implementation ease, mean_delay does not include the mean of the
 current T interval.
+ where skew_est is a number between 1 and 1
Note: Care must be taken when implementing the comparisons to ensure
that rounding does not bias skew_est. It is important that the mean
is calculated with a higher precision than the samples.
3.1.3. Variance Estimate
+3.1.3. Variability Estimate
Packet Delay Variation (PDV) ([RFC5481] and [ITUY1540]) is used as
 an estimator of the variance of the delay signal. We define PDV as
 follows:
+ an estimator of the variability of the delay signal. We define PDV
+ as follows:
PDV = PDV_max = max_T(OWD)  E_T(OWD)
var_est = E_M(PDV) = sum_M(PDV) / M
This modifies PDV as outlined in [RFC5481] to provide a summary
statistic version that best aids the grouping decisions of the
algorithm (see [HayesLCN14] section IVB).
 The use of PDV = PDV_min = E_T(OWD)  min_T(OWD) is currently being
 investigated as an alternative that is less sensitive to noise. The
 drawback of using PDV_min is that it does not distinguish between
 groups of flows with similar values of skew_est as well as PDV_max
 (see [HayesLCN14] section IVB).
+ Generally the maximum is sampled well during congestion, though it is
+ more sensitive to path and operating system noise. The use of PDV =
+ PDV_min = E_T(OWD)  min_T(OWD) would be less sensitive to this
+ noise, but is not well sampled during congestion at the bottleneck
+ and therefore not recommended.
3.1.4. Oscillation Estimate
An estimate of the low frequency oscillation of the delay signal is
calculated by counting and normalising the significant mean,
E_T(OWD), crossings of mean_delay:
freq_est = number_of_crossings / N
 Where

 we define a significant mean crossing as a crossing that
+ where we define a significant mean crossing as a crossing that
extends p_v * var_est from mean_delay. In our experiments we
have found that p_v = 0.2 is a good value.
Freq_est is a number between 0 and 1. Freq_est can be approximated
incrementally as follows:
With each new calculation of E_T(OWD) a decision is made as to
whether this value of E_T(OWD) significantly crosses the current
long term mean, mean_delay, with respect to the previous
significant mean crossing.
A cyclic buffer, last_N_crossings, records a 1 if there is a
significant mean crossing, otherwise a 0.
The counter, number_of_crossings, is incremented when there is a
 significant mean crossing and subtracted from when a nonzero
 value is removed from the last_N_crossings.
+ significant mean crossing and decremented when a nonzero value is
+ removed from the last_N_crossings.
This approximation of freq_est was not used in [HayesLCN14], which
calculated freq_est every T using the current E_N(E_T(OWD)). Our
tests show that this approximation of freq_est yields results that
are almost identical to when the full calculation is performed every
T.
3.1.5. Packet loss
The proportion of packets lost is used as a supplementary measure:
pkt_loss = sum_NT(lost packets) / sum_NT(total packets)
Note: When pkt_loss is small it is very variable, however, when
pkt_loss is high it becomes a stable measure for making grouping
 decisions.
+ decisions..
3.2. Flow Grouping
3.2.1. Flow Grouping Algorithm
The following grouping algorithm is RECOMMENDED for SBD in the RMCAT
context and is sufficient and efficient for small to moderate numbers
of flows. For very large numbers of flows (e.g. hundreds), a more
complex clustering algorithm may be substituted.
@@ 490,22 +536,22 @@
diff(skew_est) < p_s
otherwise
diff(pkt_loss) < (p_d * pkt_loss)
The threshold, (p_d * pkt_loss), is with respect to the
highest value in the difference.
This procedure involves sorting estimates from highest to lowest. It
 is simple to implement, and efficient for small numbers of flows,
 such as are expected in RTCWEB.
+ is simple to implement, and efficient for small numbers of flows (up
+ to 1020).
3.2.2. Using the flow group signal
A grouping decisions is made every T from the second T, though they
will not attain their full design accuracy until after the N'th T
interval.
Network conditions, and even the congestion controllers, can cause
bottlenecks to fluctuate. A coupled congestion controller MAY decide
only to couple groups that remain stable, say grouped together 90% of
@@ 514,105 +560,97 @@
coupled congestion controllers objectives.
3.3. Removing Noise from the Estimates
The following describe small changes to the calculation of the key
metrics that help remove noise from them. Currently these "tweaks"
are described separately to keep the main description succinct. In
future revisions of the draft these enhancements may replace the
original key metric calculations.
3.3.1. Oscillation noise
+3.3.1. PDV noise
 When a path has no congestion, the PDV will be very small and the
+ Usually during congestion the max_T(OWD) is quite well sampled as the
+ delay distribution is skewed toward the maximum. However max_T(OWD)
+ is subject to delay noise from other queues along the path as well as
+ the host operating system. Min_T(OWD) is less prone to noise along
+ the path and from the host operating system, but is not well sampled
+ during congestion (i.e. when there is a bottleneck). Flows with very
+ different packet send rates exacerbate the problem.
+
+ An alternative delay variation measure that is less sensitive to
+ extreme values and different send rates is Mean Absolute Deviation
+ (MAD). It can be implemented in an online manner as follows:
+
+ var_base_T = sum_T(OWD  E_T(OWD))
+
+ where
+
+ x is the absolute value of x
+
+ E_T(OWD) is the mean OWD calculated in the previous T
+
+ var_est = MAD_MT = sum_MT(var_base_T)/num_MT(OWD)
+
+ For calculation of freq_est p_v=0.7 (MAD is a smaller number than
+ PDV)
+
+ For the grouping threshold p_mad=0.1 instead of p_pdv (MAD is less
+ noisy so the test can be tighter)
+
+ Note that the method for improving responsiveness of MAD_MT is the
+ same as that described in Section 3.4.1 for skew_est.
+
+3.3.2. Oscillation noise
+
+ When a path has no congestion, var_est will be very small and the
recorded significant mean crossings will be the result of path noise.
Thus up to N1 meaningless mean crossings can be a source of error at
the point a link becomes a bottleneck and flows traversing it begin
to be grouped.
To remove this source of noise from freq_est:
1. Set the current PDV to PDV = NaN (a value representing an invalid
 record, ie Not a Number) for flows that are deemed to not be
+ record, i.e. Not a Number) for flows that are deemed to not be
experiencing congestion by the first skew_est based grouping test
(see Section 3.2.1).
2. Then var_est = sum_M(PDV != NaN) / num_VM(PDV)
3. For freq_est, only record a significant mean crossing if flow is
experiencing congestion.
These three changes will remove the noncongestion noise from
 freq_est.
+ freq_est. A similar adjustment can be made for MAD based var_est.
3.3.2. Clock drift
+3.3.3. Clock skew
 Generally sender and receiver clock drift will be too small to cause
+ Generally sender and receiver clock skew will be too small to cause
significant errors in the estimators. Skew_est is most sensitive to
 this type of noise. In circumstances where clock drift is high,
+ this type of noise. In circumstances where clock skew is high,
making M < N can reduce this error.
 A better method is to estimate the effect the clock drift is having
 on the E_N(E_T(OWD)), and then adjust mean_delay accordingly. A
 simple method of doing this follows:

 First divide the N E_T(OWD) values into two halves (N/2 in each)
  old and new.

 Calculate a mean of the old half:

 Older_mean = E_old(E_T(OWD)) / N/2

 Calculate a mean of the new (most recent) half:

 Newer_mean = E_new(E_T(OWD)) / N/2

 A linear estimate of the Clock Drift per T estimates is:

 CD_T = (Newer_mean  Older_mean)/N/2

 An adjusted mean estimate then is:

 mean_delay = CD_Adj(E_M(E_T(OWD))) = E_M(E_T(OWD)) + CD_T *
 (M/2 + 0.5)

 CD_Adj can be thought of as a prediction of what the long term mean
 will be in the current measurement period T. It is used as the basis
 for skew_est and freq_est.

3.3.3. Bias in the skewness measure

 If successive calculations of skew_est are made with very different
 numbers of samples (num_T(OWD)), the simple calculation of
 E_M(skew_est) used for grouping decisions will be biased by the
 intervals that have few samples samples. This bias can be corrected
 if necessary as follows.

 skew_base_T = sum_T(OWD < mean_delay)  sum_T(OWD > mean_delay)

 skew_est = sum_MT(skew_base_T)/num_MT(OWD)

 This calculation requires slightly more state, since an
 implementation will need to maintain two cyclic buffers storing
 skew_base_T and num_T(OWD) respectively to manage the rolling
 summations (note only one cyclic buffer is needed for the calculation
 of skew_est outlined previously).
+ A better method is to estimate the effect the clock skew is having on
+ the summary statistics, and then adjust statistics accordingly. A
+ simple online method of doing this based on min_T(OWD) will be
+ described here in a subsequent version of the draft.
3.4. Reducing lag and Improving Responsiveness
Measurement based shared bottleneck detection makes decisions in the
present based on what has been measured in the past. This means that
there is always a lag in responding to changing conditions. This
mechanism is based on summary statistics taken over (N*T) seconds.
This mechanism can be made more responsive to changing conditions by:
 1. Reducing N and/or M  but at the expense of less accurate
+ 1. Reducing N and/or M  but at the expense of having less accurate
metrics, and/or
2. Exploiting the fact that more recent measurements are more
valuable than older measurements and weighting them accordingly.
Although more recent measurements are more valuable, older
measurements are still needed to gain an accurate estimate of the
distribution descriptor we are measuring. Unfortunately, the simple
exponentially weighted moving average weights drop off too quickly
for our requirements and have an infinite tail. A simple linearly
@@ 620,49 +658,49 @@
to the most recent measurements. We propose a piecewise linear
distribution of weights, such that the first section (samples 1:F) is
flat as in a simple moving average, and the second section (samples
F+1:M) is linearly declining weights to the end of the averaging
window. We choose integer weights, which allows incremental
calculation without introducing rounding errors.
3.4.1. Improving the response of the skewness estimate
The weighted moving average for skew_est, based on skew_est in
 Section 3.3.3, can be calculated as follows:
+ Section 3.1.2, can be calculated as follows:
skew_est = ((MF+1)*sum(skew_base_T(1:F))
+ sum([(MF):1].*skew_base_T(F+1:M)))
/ ((MF+1)*sum(numsampT(1:F))
+ sum([(MF):1].*numsampT(F+1:M)))
 where numsampT is an array of the number of OWD samples in each T (ie
 num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1) is
 the most recent calculation of skew_base_T; 1:F refers to the integer
 values 1 through to F, and [(MF):1] refers to an array of the
 integer values (MF) declining through to 1; and ".*" is the array
 scalar dot product operator.
+ where numsampT is an array of the number of OWD samples in each T
+ (i.e. num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1)
+ is the most recent calculation of skew_base_T; 1:F refers to the
+ integer values 1 through to F, and [(MF):1] refers to an array of
+ the integer values (MF) declining through to 1; and ".*" is the
+ array scalar dot product operator.
3.4.2. Improving the response of the variance estimate
+3.4.2. Improving the response of the variability estimate
The weighted moving average for var_est can be calculated as follows:
var_est = ((MF+1)*sum(PDV(1:F)) + sum([(MF):1].*PDV(F+1:M)))
/ (F*(MF+1) + sum([(MF):1])
where 1:F refers to the integer values 1 through to F, and [(MF):1]
refers to an array of the integer values (MF) declining through to
1; and ".*" is the array scalar dot product operator. When removing
 oscillation noise (see Section 3.3.1) this calculation must be
+ oscillation noise (see Section 3.3.2) this calculation must be
adjusted to allow for invalid PDV records.
4. Measuring OWD
This section discusses the OWD measurements required for this
algorithm to detect shared bottlenecks.
The SBD mechanism described in this draft relies on differences
between OWD measurements to avoid the practical problems with
measuring absolute OWD (see [HayesLCN14] section IIIC). Since all
@@ 700,26 +738,35 @@
Nonauthenticated RTCP packets carrying shared bottleneck indications
and summary statistics could allow attackers to alter the bottleneck
sharing characteristics for private gain or disruption of other
parties communication.
8. Change history
Changes made to this document:
 02>WG00 : Fixed missing 0.5 in 3.3.2 and missing brace in 3.3.3
+ WG00>WG01 : Moved unbiased skew section to replace skew
+ estimate, more robust variability estimator, the
+ term variance replaced with variability, clock
+ drift term corrected to clock skew, revision to
+ clock skew section with a place holder, description
+ of parameters.
 01>02 : New section describing improvements to the key metric
 calculations that help to remove noise, bias, and
 reduce lag. Some revisions to the notation to make
 it clearer. Some tightening of the thresholds.
+ 02>WG00 : Fixed missing 0.5 in 3.3.2 and missing brace in
+ 3.3.3
+
+ 01>02 : New section describing improvements to the key
+ metric calculations that help to remove noise,
+ bias, and reduce lag. Some revisions to the
+ notation to make it clearer. Some tightening of
+ the thresholds.
00>01 : Revisions to terminology for clarity
9. References
9.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.