--- 1/draft-ietf-rmcat-sbd-03.txt 2016-03-21 08:22:08.288160310 -0700 +++ 2/draft-ietf-rmcat-sbd-04.txt 2016-03-21 08:22:08.332161403 -0700 @@ -1,115 +1,124 @@ RTP Media Congestion Avoidance Techniques D. Hayes, Ed. Internet-Draft University of Oslo Intended status: Experimental S. Ferlin -Expires: April 21, 2016 Simula Research Laboratory +Expires: September 22, 2016 Simula Research Laboratory M. Welzl K. Hiorth University of Oslo - October 19, 2015 + March 21, 2016 Shared Bottleneck Detection for Coupled Congestion Control for RTP Media. - draft-ietf-rmcat-sbd-03 + draft-ietf-rmcat-sbd-04 Abstract This document describes a mechanism to detect whether end-to-end 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 draft-welzl-rmcat-coupled-cc. + coupled congestion control mechanism in draft-ietf-rmcat-coupled-cc. Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at http://datatracker.ietf.org/drafts/current/. Internet-Drafts 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 Internet-Drafts as reference material or to cite them other than as "work in progress." - This Internet-Draft will expire on April 21, 2016. + This Internet-Draft will expire on September 22, 2016. Copyright Notice - Copyright (c) 2015 IETF Trust and the persons identified as the + Copyright (c) 2016 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/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as 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. Parameters and their Effect . . . . . . . . . . . . . . . 6 - 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. Oscillation noise . . . . . . . . . . . . . . . . . . 14 - 3.3.2. Clock skew . . . . . . . . . . . . . . . . . . . . . 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 variability estimate 17 - 4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 17 - 4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 17 - 5. Implementation status . . . . . . . . . . . . . . . . . . . . 18 - 6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 18 - 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 18 - 8. Security Considerations . . . . . . . . . . . . . . . . . . . 18 - 9. Change history . . . . . . . . . . . . . . . . . . . . . . . 18 - 10. References . . . . . . . . . . . . . . . . . . . . . . . . . 19 - 10.1. Normative References . . . . . . . . . . . . . . . . . . 19 - 10.2. Informative References . . . . . . . . . . . . . . . . . 19 - Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 20 + 2.1. Parameters and their Effect . . . . . . . . . . . . . . . 7 + 2.2. Recommended Parameter Values . . . . . . . . . . . . . . 8 + 3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 8 + 3.1. SBD feedback requirements . . . . . . . . . . . . . . . . 9 + 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 at + the sender . . . . . . . . . . . . . . . . . . . . . 10 + 3.1.3. Feedback when bottlenecks can be + determined at both senders and + receivers . . . . . . . . . . . . . . . . . . . . . . 11 + 3.2. Key metrics and their calculation . . . . . . . . . . . . 11 + 3.2.1. Mean delay . . . . . . . . . . . . . . . . . . . . . 11 + 3.2.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 11 + 3.2.3. Variability Estimate . . . . . . . . . . . . . . . . 12 + 3.2.4. Oscillation Estimate . . . . . . . . . . . . . . . . 12 + 3.2.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 13 + 3.3. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 13 + 3.3.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 13 + 3.3.2. Using the flow group signal . . . . . . . . . . . . . 15 + 3.4. Removing Noise from the Estimates . . . . . . . . . . . . 15 + 3.4.1. Oscillation noise . . . . . . . . . . . . . . . . . . 15 + 3.4.2. Clock skew . . . . . . . . . . . . . . . . . . . . . 16 + 3.5. Reducing lag and Improving Responsiveness . . . . 16 + 3.5.1. Improving the response of the skewness estimate . 17 + 3.5.2. Improving the response of the variability estimate 19 + 4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 19 + 4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 19 + 5. Implementation status . . . . . . . . . . . . . . . . . . . . 20 + 6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 20 + 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 20 + 8. Security Considerations . . . . . . . . . . . . . . . . . . . 20 + 9. Change history . . . . . . . . . . . . . . . . . . . . . . . 20 + 10. References . . . . . . . . . . . . . . . . . . . . . . . . . 21 + 10.1. Normative References . . . . . . . . . . . . . . . . . . 21 + 10.2. Informative References . . . . . . . . . . . . . . . . . 21 + Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 22 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 that communicate simultaneously with multiple peers (such as multi- party video). For RTP media applications such as RTCWEB, - [I-D.welzl-rmcat-coupled-cc] describes a scheme that combines the + [I-D.ietf-rmcat-coupled-cc] 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 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 @@ -129,21 +138,21 @@ device. The noise is often significantly increased if the round-trip time is used. The cleanest signal is obtained by using One-Way-Delay (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 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 3.3.2 outlines a way + circumstances where it is significant, Section 3.4.2 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 @@ -170,20 +179,23 @@ 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. + 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 @@ -275,39 +287,39 @@ 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 N|M may be a better solution. 2.2. Recommended Parameter Values Reference [Hayes-LCN14] uses T=350ms, N=50, p_l=0.1. The other parameters have been tightened to reflect minor enhancements to the - algorithm outlined in Section 3.3: c_s=-0.01, p_f=p_d=0.1, p_s=0.15, + algorithm outlined in Section 3.4: c_s=-0.01, 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 the 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 the distribution of delay measurements of packets that traverse a common bottleneck have similar shape characteristics. These shape characteristics are described using 3 key summary statistics: - variability (estimate var_est, see Section 3.1.3) + variability (estimate var_est, see Section 3.2.3) - skewness (estimate skew_est, see Section 3.1.2) + skewness (estimate skew_est, see Section 3.2.2) - oscillation (estimate freq_est, see Section 3.1.4) + oscillation (estimate freq_est, see Section 3.2.4) - with packet loss (estimate pkt_loss, see Section 3.1.5) used as a + with packet loss (estimate 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. They can be signalled from a receiver, which measures the OWD and calculates the summary statistics, to a sender, which is the entity that is transmitting the media stream. An RTP Media device may be both a sender and a @@ -321,45 +333,71 @@ | L2 | +----+ L1 | L3 +----+ | H1 |------|------| H3 | +----+ +----+ A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3). Figure 1 - In Figure 1, there are two possible cases for shared bottleneck - detection: a sender-based and a receiver-based case. + In Figure 1, there are two possible locations for shared bottleneck + detection: sender-side and receiver-side. - 1. Sender-based: consider a situation where host H1 sends media + 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 calculate summary statistics, which - they send to H1 every T. H1, having this knowledge, can - determine the shared bottleneck and accordingly control the send - rates. + and H3 measure the OWD and packet loss and either send back this + raw data, or the calculated summary statistics, periodically to + H1 every T. H1, having this knowledge, can determine the shared + bottleneck and accordingly control the send rates. - 2. Receiver-based: consider that H2 is also sending media to H3, and + 2. 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 obtain enough knowledge to detect this shared bottleneck; H3 can however determine it by combining - the summary statistics related to H1 and H2, respectively. This - case is applicable when send rates are controlled by the - receiver; then, the signal from H3 to the senders contains the - sending rate. + the summary statistics related to H1 and H2, respectively. - A discussion of the required signalling for the receiver-based case - is beyond the scope of this document. For the sender-based case, the - messages and their data format will be defined here in future - versions of this document. +3.1. SBD feedback requirements - We envisige the following exchange during initialisation: + There are three possible scenarios each with different feedback + requirements: + + 1. Both summary statistic calculations and SBD are performed at + senders only. + + 2. Summary statistics calculated on the receivers and SBD at the + senders. + + 3. Summary statistic calculations on receivers, and SBD performed at + both senders and receivers (beyond the current scope, but allows + cooperative detection of bottlenecks). + +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. + + The sender requires precise accurate OWD measurements for every + packet, along with the proportion of packets lost over the interval + T, to be sent from the receivers to the senders every T. + + An initialisation message may be required to agree on the feedback + interval. + +3.1.2. Feedback when the statistics are calculated at the receiver and + SBD at the sender + + This scenario minimises feedback, but requires receivers to send + selected summary statistics at an agreed regular interval. We + envisage the following exchange of information to initialise the + system: o An initialization message from the sender to the receiver will contain the following information: * A protocol identifier (SBD=01). This is to future proof the message exchange so that potential advances in SBD technology can be easily deployed. All following initialisation elements relate to the mechanism outlined in this document which will have the identifier SBD=01. @@ -371,49 +409,66 @@ may be able to exploit other metrics (e.g. metrics based on explicit network signals). * The values of T, N, M, and the necessary resolution and precision of the relayed statistics. o A response message from the receiver acknowledges this message with a list of key metrics it supports (subset of the senders list) and is able to relay back to the sender. - o This initialisation exchange may be repeated to finalize the - agreed metrics should not all be supported by all receivers. + This initialisation exchange may be repeated to finalize the agreed + metrics should not all be supported by all receivers. -3.1. Key metrics and their calculation + After initialisation the agreed summary statistics will be fed back + to the sender 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 SBD in RMCAT. + It is mentioned here to ensure more advanced sender/receiver + cooperative shared bottleneck determination mechanisms remain + possible in the future. + + It is envisaged that such a mechanism would be initialised in a + similar manner to that described in Section 3.1.2. + + After initialisation both summary statistics and shared bottleneck + determinations will need to be exchanged 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. -3.1.1. Mean delay +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 3 summary statistics. The mean delay, E_T(OWD), is the average one way delay 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 3.4 for a discussion on improving the + higher error rate (see Section 3.5 for a discussion on improving the responsiveness of the mechanism.) -3.1.2. Skewness Estimate +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 initialised every T. It increments for one way delay samples (OWD) @@ -428,41 +483,41 @@ 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. -3.1.3. Variability Estimate +3.2.3. Variability Estimate Mean Absolute Deviation (MAD) delay 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) For calculation of freq_est p_v=0.7 For the grouping threshold p_mad=0.1 -3.1.4. Oscillation Estimate +3.2.4. 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 extends p_v * var_est from mean_delay. In our experiments we have found that p_v = 0.7 is a good value. @@ -481,34 +536,34 @@ The counter, number_of_crossings, is incremented when there is a significant mean crossing and decremented when a non-zero value is removed from the last_N_crossings. This approximation of freq_est was not used in [Hayes-LCN14], 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 +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 small it is very variable, however, when pkt_loss is high it becomes a stable measure for making grouping decisions. -3.2. Flow Grouping +3.3. Flow Grouping -3.2.1. Flow Grouping Algorithm +3.3.1. 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. 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 @@ -566,79 +621,80 @@ 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 (up to 10-20). -3.2.2. Using the flow group signal +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*N'th T interval. We recommend that grouping decisions are not 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 draft and will be specific to the coupled congestion controllers objectives. -3.3. Removing Noise from the Estimates +3.4. 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 +3.4.1. 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 N-1 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 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 skew_est based grouping test - (see Section 3.2.1). + (see Section 3.3.1). 2. Then var_est = sum_MT(var_base_T != NaN) / num_MT(OWD) 3. For freq_est, only record a significant mean crossing if flow deemed to be transiting a bottleneck. These three changes can help to remove the non-bottleneck noise from freq_est. -3.3.2. Clock skew +3.4.2. Clock skew 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 skew is high, - basing skew_est only on the previous T's mean provides a noisier but - reliable signal. + 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 provides a noisier but reliable signal. - 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. + 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 [Zhang-Infocom02]. -3.4. Reducing lag and Improving Responsiveness +3.5. 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 having less accurate metrics, and/or @@ -651,24 +707,24 @@ 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 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 +3.5.1. Improving the response of the skewness estimate The weighted moving average for skew_est, based on skew_est in - Section 3.1.2, can be calculated as follows: + Section 3.2.2, can be calculated as follows: skew_est = ((M-F+1)*sum(skew_base_T(1:F)) + sum([(M-F):1].*skew_base_T(F+1:M))) / ((M-F+1)*sum(numsampT(1:F)) + sum([(M-F):1].*numsampT(F+1:M))) where numsampT is an array of the number of OWD samples in each T @@ -716,44 +772,44 @@ 11. sum_skewbase = sum_skewbase + skewbase_hist(F+1) - old_skewbase 12. sum_numsamp = sum_numsamp + numsampT(1) - old_numsampT 13. skew_est = ((M-F+1)*F_skewbase + W_D_skewbase) / ((M-F+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. -3.4.2. Improving the response of the variability estimate +3.5.2. Improving the response of the variability estimate Similarly the weighted moving average for var_est can be calculated as follows: var_est = ((M-F+1)*sum(var_base_T(1:F)) + sum([(M-F):1].*var_base_T(F+1:M))) / ((M-F+1)*sum(numsampT(1:F)) + sum([(M-F):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 [(M-F):1] refers to an array of the integer values (M-F) 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 adjusted to allow for + (see Section 3.4.1) 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 - in Section 3.4.1. However, note that the buffer numsampT is used for + in Section 3.5.1. However, note that the buffer numsampT is used for both calculations so the operations on it should not be repeated. 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 [Hayes-LCN14] section IIIC). Since all @@ -796,20 +852,25 @@ Non-authenticated 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. 9. Change history Changes made to this document: + WG-03->WG-04 : Add M to terminology table, suggest skew_est based + on previous T and no freq_est in clock skew + section, feedback requirements as a separate sub + section. + WG-02->WG-03 : Correct misspelled author WG-01->WG-02 : Removed ambiguity associated with the term "congestion". Expanded the description of initialisation messages. Removed PDV metric. Added description of incremental weighted metric calculations for skew_est. Various clarifications based on implementation work. Fixed typos and tuned parameters. @@ -829,70 +890,67 @@ notation to make it clearer. Some tightening of the thresholds. 00->01 : Revisions to terminology for clarity 10. References 10.1. 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, + Requirement Levels", BCP 14, RFC 2119, + DOI 10.17487/RFC2119, March 1997, . 10.2. Informative References [Hayes-LCN14] Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive Shared Bottleneck Detection using Shape Summary - Statistics", Proc. the IEEE Local Computer Networks (LCN) - p150-158, September 2014, . - [I-D.welzl-rmcat-coupled-cc] - Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion - control for RTP media", draft-welzl-rmcat-coupled-cc-04 - (work in progress), October 2014. - - [ITU-Y1540] - ITU-T, "Internet Protocol Data Communication Service - IP - Packet Transfer and Availability Performance Parameters", - Series Y: Global Information Infrastructure, Internet - Protocol Aspects and Next-Generation Networks , March - 2011, . + [I-D.ietf-rmcat-coupled-cc] + Islam, S., Welzl, M., and S. Gjessing, "Coupled congestion + control for RTP media", draft-ietf-rmcat-coupled-cc-00 + (work in progress), September 2015. [RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V. Jacobson, "RTP: A Transport Protocol for Real-Time Applications", STD 64, RFC 3550, DOI 10.17487/RFC3550, July 2003, . [RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey, "Extended RTP Profile for Real-time Transport Control - Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585, DOI - 10.17487/RFC4585, July 2006, + Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585, + DOI 10.17487/RFC4585, July 2006, . [RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for Real-time Transport Control Protocol (RTCP)-Based Feedback (RTP/SAVPF)", RFC 5124, DOI 10.17487/RFC5124, February 2008, . - [RFC5481] Morton, A. and B. Claise, "Packet Delay Variation - Applicability Statement", RFC 5481, DOI 10.17487/RFC5481, - March 2009, . - [RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind, "Low Extra Delay Background Transport (LEDBAT)", RFC 6817, DOI 10.17487/RFC6817, December 2012, . + [Zhang-Infocom02] + Zhang, L., Liu, Z., and H. Xia, "Clock synchronization + algorithms for network measurements", Proc. the IEEE + International Conference on Computer Communications + (INFOCOM) pp160-169, September 2002, + . + Authors' Addresses David Hayes (editor) University of Oslo PO Box 1080 Blindern Oslo N-0316 Norway Phone: +47 2284 5566 Email: davihay@ifi.uio.no