Request for Comments: 8382
Category: Experimental
ISSN: 20701721
S. Ferlin
Simula Research Laboratory
M. Welzl
K. Hiorth
University of Oslo
June 2018
Shared Bottleneck Detection for Coupled Congestion Control for RTP Media
Abstract

This document describes a mechanism to detect whether endtoend data flows share a common bottleneck. This mechanism relies on summary statistics that are calculated based on continuous measurements and used as input to a grouping algorithm that runs wherever the knowledge is needed.
Status of This Memo

This document is not an Internet Standards Track specification; it is published for examination, experimental implementation, and evaluation.
This document defines an Experimental Protocol for the Internet community. This document is a product of the Internet Engineering Task Force (IETF). It represents the consensus of the IETF community. It has received public review and has been approved for publication by the Internet Engineering Steering Group (IESG). Not all documents approved by the IESG are candidates for any level of Internet Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at https://www.rfceditor.org/info/rfc8382.
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Table of Contents

1. Introduction ....................................................4 1.1. The Basic Mechanism ........................................4 1.2. The Signals ................................................4 1.2.1. Packet Loss .........................................4 1.2.2. Packet Delay ........................................5 1.2.3. Path Lag ............................................5 2. Definitions .....................................................6 2.1. Parameters and Their Effects ...............................7 2.2. Recommended Parameter Values ...............................8 3. Mechanism .......................................................9 3.1. SBD Feedback Requirements .................................10 3.1.1. Feedback When All the Logic Is Placed at the Sender .........................................10 3.1.2. Feedback When the Statistics Are Calculated at the Receiver and SBD Is Performed at the Sender ........11 3.1.3. Feedback When Bottlenecks Can Be Determined at Both Senders and Receivers ......................11 3.2. Key Metrics and Their Calculation .........................12 3.2.1. Mean Delay .........................................12 3.2.2. Skewness Estimate ..................................12 3.2.3. Variability Estimate ...............................13 3.2.4. Oscillation Estimate ...............................13 3.2.5. Packet Loss ........................................14 3.3. Flow Grouping .............................................14 3.3.1. FlowGrouping Algorithm ............................14 3.3.2. Using the Flow Group Signal ........................18 4. Enhancements to the Basic SBD Algorithm ........................18 4.1. Reducing Lag and Improving Responsiveness .................18 4.1.1. Improving the Response of the Skewness Estimate ....19 4.1.2. Improving the Response of the Variability Estimate ...........................................20 4.2. Removing Oscillation Noise ................................21 5. Measuring OWD ..................................................21 5.1. Timestamp Resolution ......................................21 5.2. Clock Skew ................................................22 6. Expected Feedback from Experiments .............................22 7. IANA Considerations ............................................22 8. Security Considerations ........................................22 9. References .....................................................23 9.1. Normative References ......................................23 9.2. Informative References ....................................23 Acknowledgments ...................................................25 Authors' Addresses ................................................25
1. Introduction

In the Internet, it is not normally known whether 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 may or may not share a bottleneck. 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 that communicate simultaneously with multiple peers (such as multiparty video). For RTP media applications such as RTCWEB, [RTPCOUPLEDCC] describes a scheme that combines the congestion controllers of flows in order to honor their priorities and avoid unnecessary packet loss as well as delay. This mechanism relies on some form of Shared Bottleneck Detection (SBD); here, a measurement based SBD approach is described.
1.1. The Basic Mechanism

The mechanism groups flows that have similar statistical characteristics together. Section 3.3.1 describes a simple method for achieving this; however, a major part of this document is concerned with collecting suitable statistics for this purpose.
1.2. The Signals

The current Internet is unable to explicitly inform endpoints as to which flows share bottlenecks, so endpoints need to infer this from whatever information is available to them. The mechanism described here currently utilizes packet loss and packet delay but is not restricted to these. As Explicit Congestion Notification (ECN) becomes more prevalent, it too will become a valuable base signal that can be correlated to detect shared bottlenecks.
1.2.1. Packet Loss

Packet loss is often a relatively infrequent indication that a flow traverses a bottleneck. Therefore, on its own it is of limited use for SBD; however, it is a valuable supplementary measure when it is more prevalent (refer to [RFC7680], Section 2.5 for measuring packet loss).
1.2.2. Packet Delay

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 OneWay Delay (OWD) (refer to [RFC7679], Section 3 for a definition of OWD).
Measuring absolute OWD is difficult, since it requires both the sender and receiver clocks to be synchronized. However, since the statistics being collected are relative to the mean OWD, a relative OWD measurement is sufficient. Clock skew is not usually significant over the time intervals used by this SBD mechanism (see [RFC6817], Appendix A.2 for a discussion on clock skew and OWD measurements). However, in circumstances where it is significant, Section 5.2 outlines a way of adjusting the calculations to cater to 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.
1.2.3. Path Lag

Flows that share a common bottleneck may traverse different paths, and these paths will often have different base delays. This makes it difficult to correlate changes in delay or loss. This technique uses the longterm shape of the delay distribution as a base for comparison to counter this.
2. Definitions

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.
Acronyms used in this document:

OWD  OneWay Delay
MAD  Mean Absolute Deviation
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 calculations M the number of base time, T, intervals used in some calculations, where M <= N sum(...) summation of terms of the variable in parentheses sum_T(...) summation of all the measurements of the variable in parentheses taken over the interval T sum_NT(...) summation of all measurements taken over the interval N*T sum_MT(...) summation of all measurements taken over the interval M*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 variable in parentheses E_M(...) the expectation or mean of the last M values of the variable in parentheses num_T(...) the count of measurements of the variable in parentheses taken in the interval T num_MT(...) the count of measurements of the variable in parentheses taken in the interval M*T PB a boolean variable indicating that the particular flow was identified transiting a bottleneck in the previous interval T (i.e., "Previously Bottleneck") skew_est a measure of skewness in an OWD distribution skew_base_T a variable used as an intermediate step in calculating skew_est var_est a measure of variability in OWD measurements var_base_T a variable used as an intermediate step in calculating var_est freq_est a measure of lowfrequency oscillation in the OWD measurements pkt_loss a measure of the proportion of packets lost p_l, p_f, p_mad, c_s, c_h, p_s, p_d, p_v various thresholds used in the mechanism M and F number of values related to N

2.1. Parameters and Their Effects

T T should be long enough so that there are enough packets received during T for a useful estimate of the shortterm mean OWD and variation statistics. Making T too large can limit the efficacy of freq_est. It will also increase the response time of the mechanism. Making T too small will make the metrics noisier. N and M N should be large enough to provide a stable estimate of oscillations in OWD. Often, M=N is just fine, though having M<N may be beneficial in certain circumstances. M*T needs to be long enough to provide stable estimates of skewness and MAD. F F determines the number of intervals over which statistics are considered to be equally weighted. When F=M, recent and older measurements are considered equal. Making F<M can increase the responsiveness of the SBD mechanism. If F is too small, statistics will be too noisy. c_s c_s is the threshold in skew_est used for determining whether a flow is transiting a bottleneck or not. Lower values of c_s require bottlenecks to be more congested to be considered for grouping by the mechanism. c_s should be set within the range of +0.2 to 0.1  low enough so that lightly loaded paths do not give a false indication. p_l p_l is the threshold in pkt_loss used for determining whether a flow is transiting a bottleneck or not. When pkt_loss is high, it becomes a better indicator of congestion than skew_est. c_h c_h adds hysteresis to the bottleneck determination. It should be large enough to avoid constant switching in the determination but low enough to ensure that grouping is not attempted when there is no bottleneck and the delay and loss signals cannot be relied upon. p_v p_v determines the sensitivity of freq_est to noise. Making it smaller will yield higher but noisier values for freq_est. Making it too large will render it ineffective for determining groups. p_* Flows are separated when the skew_estvar_estfreq_estpkt_loss measure is greater than p_sp_madp_fp_d. Adjusting these is a compromise between false grouping of flows that do not share a bottleneck and false splitting of flows that do. Making them larger can help if the measures are very noisy, but reducing the noise in the statistical measures by adjusting T and NM may be a better solution.
2.2. Recommended Parameter Values

[HayesLCN14] uses T=350ms and N=50. The other parameters have been tightened to reflect minor enhancements to the algorithm outlined in Section 4: c_s=0.1, p_f=p_d=0.1, p_s=0.15, p_mad=0.1, p_v=0.7. M=30, F=20, and c_h=0.3 are additional parameters defined in that document. These are values that seem to work well over a wide range of practical Internet conditions.
3. Mechanism

The mechanism described in this document is based on the observation that when flows traverse a common bottleneck, each flow's distribution of packet delay measurements has similar shape characteristics. These shape characteristics are described using three key summary statistics 
1. variability estimate (var_est; see Section 3.2.3) 2. skewness estimate (skew_est; see Section 3.2.2) 3. oscillation estimate (freq_est; see Section 3.2.4)
 with packet loss (pkt_loss; see Section 3.2.5) used as a supplementary statistic.
Summary statistics help to address both the noise and the path lag problems by describing the general shape over a relatively long period of time. Each summary statistic portrays a "view" of the bottleneck link characteristics, and when used together, they provide a robust discrimination for grouping flows. An RTP media device may be both a sender and a receiver. SBD can be performed at either a sender or a receiver, or both.
In Figure 1, there are two possible locations for shared bottleneck detection: the sender side and the receiver side.
++  H2  ++   L2  ++ L1  L3 ++  H1  H3  ++ ++
A network with three hosts (H1, H2, H3) and three links (L1, L2, L3)
Figure 1

 Sender side: Consider a situation where host H1 sends media streams to hosts H2 and H3, and L1 is a shared bottleneck. H2 and H3 measure the OWD and packet loss and periodically send either this raw data or the calculated summary statistics to H1 every T. H1, having this knowledge, can determine the shared bottleneck and accordingly control the send rates.
 Receiver side: Consider that H2 is also sending media to H3, and L3 is a shared bottleneck. If H3 sends summary statistics to H1 and H2, neither H1 nor H2 alone obtains enough knowledge to detect this shared bottleneck; H3 can, however, determine it by combining the summary statistics related to H1 and H2, respectively.
3.1. SBD Feedback Requirements

There are three possible scenarios, each with different feedback requirements:
 Both summary statistic calculations and SBD are performed at senders only. When senderbased congestion control is implemented, this method is RECOMMENDED.
 Summary statistics are calculated on the receivers, and SBD is performed at the senders.
 Summary statistic calculations are performed on receivers, and SBD is performed at both senders and receivers (beyond the scope of this document, but allows cooperative detection of bottlenecks).
All three possibilities are discussed for completeness in this document; however, it is expected that feedback will take the form of scenario 1 and operate in conjunction with senderbased congestion control mechanisms.
3.1.1. Feedback When All the Logic Is Placed at the Sender

Having the sender calculate the summary statistics and determine the shared bottlenecks based on them has the advantage of placing most of the functionality in one place  the sender.
For every packet, the sender requires accurate relative OWD measurements of adequate precision, along with an indication of lost packets (or the proportion of packets lost over an interval). A method to provide such measurement data with the RTP Control Protocol (RTCP) is described in [RTCPCCFEEDBACK].
Sums, var_base_T, and skew_base_T are calculated incrementally as relative OWD measurements are determined from the feedback messages. When the mechanism has received sufficient measurements to cover the base time interval T for all flows, the summary statistics (see Section 3.2) are calculated for that T interval and flows are grouped (see Section 3.3.1). The exact timing of these calculations will depend on the frequency of the feedback message.
3.1.2. Feedback When the Statistics Are Calculated at the Receiver and
SBD Is Performed at the Sender

This scenario minimizes feedback but requires receivers to send selected summary statistics at an agreedupon regular interval. We envisage the following exchange of information to initialize the system:
 An initialization message from the sender to the receiver will contain the following information:

 A list of which key metrics should be collected and relayed back to the sender out of a possibly extensible set (pkt_loss, var_est, skew_est, and freq_est). The grouping algorithm described in this document requires all four of these metrics, and receivers MUST be able to provide them, but future algorithms may be able to exploit other metrics (e.g., metrics based on explicit network signals).
 The values of T, N, and M, and the necessary resolution and precision of the relayed statistics.
 A response message from the receiver acknowledges this message with a list of key metrics it supports (subset of the sender's list) and is able to relay back to the sender.
This initialization exchange may be repeated to finalize the set of metrics that will be used. All agreedupon metrics need to be supported by all receivers. It is also recommended that an identifier for the SBD algorithm version be included in the initialization message from the sender, so that potential advances in SBD technology can be easily deployed. For reference, the mechanism outlined in this document has the identifier "SBD=01".
After initialization, the agreedupon summary statistics are fed back to the sender (nominally every T).
3.1.3. Feedback When Bottlenecks Can Be Determined at Both Senders and
Receivers

This type of mechanism is currently beyond the scope of the SBD algorithm described in this document. It is mentioned here to ensure that sender/receiver cooperative shared bottleneck determination mechanisms that are more advanced remain possible in the future.
It is envisaged that such a mechanism would be initialized in a manner similar to that described in Section 3.1.2.
After initialization, both summary statistics and shared bottleneck determinations should be exchanged, nominally every T.
3.2. Key Metrics and Their Calculation

Measurements are calculated over a base interval (T) and summarized over N or M such intervals. All summary statistics can be calculated incrementally.
3.2.1. Mean Delay

The mean delay is not a useful signal for comparisons between flows, since flows may traverse quite different paths and clocks will not necessarily be synchronized. However, it is a base measure for the three summary statistics. The mean delay, E_T(OWD), is the average OWD measured over T.
To facilitate the other calculations, the last N E_T(OWD) values will need to be stored in a cyclic buffer along with the moving average of E_T(OWD):
mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M
where M <= N. Setting M to be less than N allows the mechanism to be more responsive to changes, but potentially at the expense of a higher error rate (see Section 4.1 for a discussion on improving the responsiveness of the mechanism).
3.2.2. Skewness Estimate

Skewness is difficult to calculate efficiently and accurately. Ideally, it should be calculated over the entire period (M*T) from the mean OWD over that period. However, this would require storing every delay measurement over the period. Instead, an estimate is made over M*T based on a calculation every T using the previous T's calculation of mean_delay.
The base for the skewness calculation is estimated using a counter initialized every T. It increments for OWD samples below the mean and decrements for OWD above the mean. So, for each OWD sample:
if (OWD < mean_delay) skew_base_T++ if (OWD > mean_delay) skew_base_T
mean_delay does not include the mean of the current T interval to enable it to be calculated iteratively.
skew_est = sum_MT(skew_base_T) / num_MT(OWD)

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.

3.2.3. Variability Estimate

Mean Absolute Deviation (MAD) is a robust variability measure that copes well with different send rates. 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)

3.2.4. Oscillation Estimate

An estimate of the lowfrequency oscillation of the delay signal is calculated by counting and normalizing 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 extends p_v * var_est from mean_delay. In our experiments, we have found that p_v = 0.7 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 longterm 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, it records a 0.
 The counter, number_of_crossings, is incremented when there is a 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.

3.2.5. Packet Loss

The proportion of packets lost over the period NT is used as a supplementary measure:
pkt_loss = sum_NT(lost packets) / sum_NT(total packets)
Note: When pkt_loss is low, it is very variable; however, when pkt_loss is high, it becomes a stable measure for making grouping decisions.
3.3. Flow Grouping
3.3.1. FlowGrouping Algorithm

The following grouping algorithm is RECOMMENDED for the use of SBD with coupled congestion control for RTP media [RTPCOUPLEDCC] 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.
Since no single metric is precise enough to group flows (due to noise), the algorithm uses multiple metrics. Each metric offers a different "view" of the bottleneck link characteristics, and used together they enable a more precise grouping of flows than would otherwise be possible.
Flows determined to be transiting a bottleneck are successively divided into groups based on freq_est, var_est, skew_est, and pkt_loss.
The first step is to determine which flows are transiting a bottleneck. This is important, since if a flow is not transiting a bottleneck its delaybased metrics will not describe the bottleneck but will instead describe the "noise" from the rest of the path. Skewness, with the proportion of packet loss as a supplementary measure, is used to do this:
 Grouping will be performed on flows that are inferred to be traversing a bottleneck by:
skew_est < c_s
 ( skew_est < c_h & PB )  pkt_loss > p_l


The parameter c_s controls how sensitive the mechanism is in detecting a bottleneck. c_s = 0.0 was used in [HayesLCN14]. A value of c_s = 0.1 is a little more sensitive, and c_s = 0.1 is a little less sensitive. c_h controls the hysteresis on flows that were grouped as transiting a bottleneck the previous time. If the test result is TRUE, PB=TRUE; otherwise, PB=FALSE.
These flows (i.e., flows transiting a bottleneck) are then progressively divided into groups based on the freq_est, var_est, and skew_est summary statistics. The process proceeds according to the following steps:
 Group flows whose difference in sorted freq_est is less than a threshold:
diff(freq_est) < p_f
 Subdivide the groups obtained in step 2 by grouping flows whose difference in sorted E_M(var_est) (highest to lowest) is less than a threshold:
diff(var_est) < (p_mad * var_est)

The threshold, (p_mad * var_est), is with respect to the highest value in the difference.
 Subdivide the groups obtained in step 3 by grouping flows whose difference in sorted skew_est is less than a threshold:
diff(skew_est) < p_s
 When packet loss is high enough to be reliable (pkt_loss > p_l), subdivide the groups obtained in step 4 by grouping flows whose difference is less than a threshold:
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 is efficient for small numbers of flows (up to 1020). Figure 2 illustrates this algorithm.
********* * Flows * ***.**.** / ' / '. / \ .v. .v. 1. Flows traversing  Cong   UnCong  a bottleneck '..' '' / \ / \ / \ .v. v. 2. Divide by  g_1  ...  g_n  freq_est '.. '.. / \ / \ / '. v '. / \ \ .v. .v. .v. 3. Divide by  g_1a  ...  g_1z  ...  g_nz  var_est '..' '.. '..' / \ / \ /  / '. v v v  / \  .v. .v. .v. 4. Divide by  g_1ai  ...  g_1ax  ...  g_nzx  skew_est '..' '.. '..' / \ / \ /  / '. v v v  / \  .v. .v. .v. 5. Divide by  g_1aiA  ...  g_1aiZ  ...  g_nzxZ  pkt_loss '' '' '' (when applicable)

Simple grouping algorithm
Figure 2
3.3.2. Using the Flow Group Signal

Grouping decisions can be made every T from the second T; however, they will not attain their full design accuracy until after the 2*Nth T interval. We recommend that grouping decisions not be made until 2*M T intervals.
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 the time, depending on its objectives. Recommendations concerning this are beyond the scope of this document and will be specific to the coupled congestion controller's objectives.
4. Enhancements to the Basic SBD Algorithm

The SBD algorithm as specified in Section 3 was found to work well for a broad variety of conditions. The following enhancements to the basic mechanisms have been found to significantly improve the algorithm's performance under some circumstances and SHOULD be implemented. These "tweaks" are described separately to keep the main description succinct.
4.1. Reducing Lag and Improving Responsiveness

This section describes how to improve the responsiveness of the basic algorithm.
Measurementbased 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:
 Reducing N and/or M, but at the expense of having metrics that are less accurate, and/or
 Exploiting the fact that measurements that are more recent are more valuable than older measurements and weighting them accordingly.
Although measurements that are more recent 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 declining weighted moving average also does not provide enough weight to the measurements that are most recent. 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; this allows incremental calculation without introducing rounding errors.
4.1.1. Improving the Response of the Skewness Estimate

The weighted moving average for skew_est, based on skew_est as defined in Section 3.2.2, 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 (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.
To calculate this weighted skew_est incrementally:
Notation: F_ = flat portion, D_ = declining portion, W_ = weighted component Initialize: sum_skewbase = 0, F_skewbase = 0, W_D_skewbase = 0



skewbase_hist = buffer of length M, initialized to 0
numsampT = buffer of length M, initialized to 0


Steps per iteration:
1. old_skewbase = skewbase_hist(M) 2. old_numsampT = numsampT(M) 3. cycle(skewbase_hist) 4. cycle(numsampT) 5. numsampT(1) = num_T(OWD) 6. skewbase_hist(1) = skew_base_T 7. F_skewbase = F_skewbase + skew_base_T  skewbase_hist(F+1) 8. W_D_skewbase = W_D_skewbase + (MF)*skewbase_hist(F+1)  sum_skewbase 9. W_D_numsamp = W_D_numsamp + (MF)*numsampT(F+1)  sum_numsamp + F_numsamp 10. F_numsamp = F_numsamp + numsampT(1)  numsampT(F+1) 11. sum_skewbase = sum_skewbase + skewbase_hist(F+1)  old_skewbase 12. sum_numsamp = sum_numsamp + numsampT(1)  old_numsampT 13. skew_est = ((MF+1)*F_skewbase + W_D_skewbase) / ((MF+1)*F_numsamp+W_D_numsamp)
where cycle(...) refers to the operation on a cyclic buffer where the start of the buffer is now the next element in the buffer.

4.1.2. Improving the Response of the Variability Estimate

Similarly, the weighted moving average for var_est can be calculated as follows:
var_est = ((MF+1)*sum(var_base_T(1:F)) + sum([(MF):1].*var_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 (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. When removing oscillation noise (see Section 4.2), this calculation must be adjusted to allow for invalid var_base_T records.
var_est can be calculated incrementally in the same way as skew_est as shown in Section 4.1.1. However, note that the buffer numsampT is used for both calculations, so the operations on it should not be repeated.
4.2. Removing Oscillation Noise

When a path has no bottleneck, 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 where a link becomes a bottleneck and flows traversing it begin to be grouped.
To remove this source of noise from freq_est:
 Set the current var_base_T = NaN (a value representing an invalid record, i.e., Not a Number) for flows that are deemed to not be transiting a bottleneck by the first grouping test that is based on skew_est (see Section 3.3.1).
 Then, var_est = sum_MT(var_base_T != NaN) / num_MT(OWD).
 For freq_est, only record a significant mean crossing if a given flow is deemed to be transiting a bottleneck.
These three changes can help to remove the nonbottleneck noise from freq_est.
5. Measuring OWD

This section discusses the OWD measurements required for this algorithm to detect shared bottlenecks.
The SBD mechanism described in this document relies on differences between OWD measurements to avoid the practical problems with measuring absolute OWD (see [HayesLCN14], Section III.C). Since all summary statistics are relative to the mean OWD and sender/receiver clock offsets should be approximately constant over the measurement periods, the offset is subtracted out in the calculation.
5.1. Timestamp Resolution

The SBD mechanism requires timing information precise enough to be able to make comparisons. As a rule of thumb, the time resolution should be less than one hundredth of a typical path's range of delays. In general, the coarser the time resolution, the more care that needs to be taken to ensure that rounding errors do not bias the skewness calculation. Frequent timing information in millisecond resolution as described by [RTCPCCFEEDBACK] should be sufficient for the sender to calculate relative OWD.
5.2. Clock Skew

Generally, sender and receiver clock skew will be too small to cause significant errors in the estimators. skew_est and freq_est are the most sensitive to this type of noise due to their use of a mean OWD calculated over a longer interval. In circumstances where clock skew is high, basing skew_est only on the previous T's mean and ignoring freq_est provide a noisier but reliable signal.
A more sophisticated method is to estimate the effect the clock skew is having on the summary statistics and then adjust statistics accordingly. There are a number of techniques in the literature, including [ZhangInfocom02].
6. Expected Feedback from Experiments

The algorithm described in this memo has so far been evaluated using simulations and smallscale experiments. Real network tests using RTP Media Congestion Avoidance Techniques (RMCAT) congestion control algorithms will help confirm the default parameter choice. For example, the time interval T may need to be made longer if the packet rate is very low. Implementers and testers are invited to document their findings in an InternetDraft.
7. IANA Considerations

This document has no IANA actions.
8. Security Considerations

The security considerations of RFC 3550 [RFC3550], RFC 4585 [RFC4585], and RFC 5124 [RFC5124] are expected to apply.
Nonauthenticated RTCP packets carrying OWD measurements, shared bottleneck indications, and/or summary statistics could allow attackers to alter the bottlenecksharing characteristics for private gain or disruption of other parties' communication. When using SBD for coupled congestion control as described in [RTPCOUPLEDCC], the security considerations of [RTPCOUPLEDCC] apply.
9. References
9.1. Normative References

[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997, <https://www.rfceditor.org/info/rfc2119>. [RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017, <https://www.rfceditor.org/info/rfc8174>.
9.2. Informative References

[HayesLCN14]
Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive Shared Bottleneck Detection using Shape Summary Statistics", Proc. IEEE Local Computer Networks (LCN), pp. 150158, DOI 10.1109/LCN.2014.6925767, September 2014, <http://heim.ifi.uio.no/davihay/ hayes14__pract_passiv_shared_bottl_detecabstract.html>. [RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V. Jacobson, "RTP: A Transport Protocol for RealTime Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550, July 2003, <https://www.rfceditor.org/info/rfc3550>. [RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey, "Extended RTP Profile for Realtime Transport Control Protocol (RTCP)Based Feedback (RTP/AVPF)", RFC 4585, DOI 10.17487/RFC4585, July 2006, <https://www.rfceditor.org/info/rfc4585>. [RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for Realtime Transport Control Protocol (RTCP)Based Feedback (RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124, February 2008, <https://www.rfceditor.org/info/rfc5124>. [RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind, "Low Extra Delay Background Transport (LEDBAT)", RFC 6817, DOI 10.17487/RFC6817, December 2012, <https://www.rfceditor.org/info/rfc6817>. [RFC7679] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton, Ed., "A OneWay Delay Metric for IP Performance Metrics (IPPM)", STD 81, RFC 7679, DOI 10.17487/RFC7679, January 2016, <https://www.rfceditor.org/info/rfc7679>. [RFC7680] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton, Ed., "A OneWay Loss Metric for IP Performance Metrics (IPPM)", STD 82, RFC 7680, DOI 10.17487/RFC7680, January 2016, <https://www.rfceditor.org/info/rfc7680>.
[RTCPCCFEEDBACK]
Sarker, Z., Perkins, C., Singh, V., and M. Ramalho, "RTP Control Protocol (RTCP) Feedback for Congestion Control", Work in Progress, draftietfavtcorecc feedbackmessage01, March 2018.
[RTPCOUPLEDCC]
Islam, S., Welzl, M., and S. Gjessing, "Coupled congestion control for RTP media", Work in Progress, draftietf rmcatcoupledcc07, September 2017.
[ZhangInfocom02]
Zhang, L., Liu, Z., and H. Xia, "Clock synchronization algorithms for network measurements", Proc. IEEE International Conference on Computer Communications (INFOCOM), pp. 160169, DOI 10.1109/INFCOM.2002.1019257, September 2002.
Acknowledgments

This work was partially funded by the European Community under its Seventh Framework Programme through the Reducing Internet Transport Latency (RITE) project (ICT317700). The views expressed are solely those of the authors.
Authors' Addresses

David Hayes (editor) Simula Research Laboratory P.O. Box 134 Lysaker 1325 Norway
Email:
davidh@simula.no Simone Ferlin Simula Research Laboratory P.O. Box 134 Lysaker 1325 Norway
Email:
simone@ferlin.io
Michael Welzl
University of Oslo
P.O. Box 1080 Blindern
Oslo N0316
NorwayEmail: michawe@ifi.uio.no
Kristian Hiorth
University of Oslo
P.O. Box 1080 Blindern
Oslo N0316
NorwayEmail: kristahi@ifi.uio.no