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2 RTP Media Congestion Avoidance D. Hayes, Ed.
3 Techniques University of Oslo
4 Internet-Draft S. Ferlin
5 Intended status: Experimental Simula Research Laboratory
6 Expires: September 4, 2015 M. Welzl
7 University of Oslo
8 March 3, 2015
10 Shared Bottleneck Detection for Coupled Congestion Control for RTP
11 Media.
12 draft-hayes-rmcat-sbd-02
14 Abstract
16 This document describes a mechanism to detect whether end-to-end data
17 flows share a common bottleneck. It relies on summary statistics
18 that are calculated by a data receiver based on continuous
19 measurements and regularly fed to a grouping algorithm that runs
20 wherever the knowledge is needed. This mechanism complements the
21 coupled congestion control mechanism in draft-welzl-rmcat-coupled-cc.
23 Status of this Memo
25 This Internet-Draft is submitted in full conformance with the
26 provisions of BCP 78 and BCP 79.
28 Internet-Drafts are working documents of the Internet Engineering
29 Task Force (IETF). Note that other groups may also distribute
30 working documents as Internet-Drafts. The list of current Internet-
31 Drafts is at http://datatracker.ietf.org/drafts/current/.
33 Internet-Drafts are draft documents valid for a maximum of six months
34 and may be updated, replaced, or obsoleted by other documents at any
35 time. It is inappropriate to use Internet-Drafts as reference
36 material or to cite them other than as "work in progress."
38 This Internet-Draft will expire on September 4, 2015.
40 Copyright Notice
42 Copyright (c) 2015 IETF Trust and the persons identified as the
43 document authors. All rights reserved.
45 This document is subject to BCP 78 and the IETF Trust's Legal
46 Provisions Relating to IETF Documents
47 (http://trustee.ietf.org/license-info) in effect on the date of
48 publication of this document. Please review these documents
49 carefully, as they describe your rights and restrictions with respect
50 to this document. Code Components extracted from this document must
51 include Simplified BSD License text as described in Section 4.e of
52 the Trust Legal Provisions and are provided without warranty as
53 described in the Simplified BSD License.
55 Table of Contents
57 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
58 1.1. The signals . . . . . . . . . . . . . . . . . . . . . . . 3
59 1.1.1. Packet Loss . . . . . . . . . . . . . . . . . . . . . 3
60 1.1.2. Packet Delay . . . . . . . . . . . . . . . . . . . . . 3
61 1.1.3. Path Lag . . . . . . . . . . . . . . . . . . . . . . . 4
62 2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
63 2.1. Parameter Values . . . . . . . . . . . . . . . . . . . . . 5
64 3. Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 6
65 3.1. Key metrics and their calculation . . . . . . . . . . . . 7
66 3.1.1. Mean delay . . . . . . . . . . . . . . . . . . . . . . 7
67 3.1.2. Skewness Estimate . . . . . . . . . . . . . . . . . . 8
68 3.1.3. Variance Estimate . . . . . . . . . . . . . . . . . . 9
69 3.1.4. Oscillation Estimate . . . . . . . . . . . . . . . . . 9
70 3.1.5. Packet loss . . . . . . . . . . . . . . . . . . . . . 10
71 3.2. Flow Grouping . . . . . . . . . . . . . . . . . . . . . . 10
72 3.2.1. Flow Grouping Algorithm . . . . . . . . . . . . . . . 10
73 3.2.2. Using the flow group signal . . . . . . . . . . . . . 12
74 3.3. Removing Noise from the Estimates . . . . . . . . . . . . 12
75 3.3.1. Oscillation noise . . . . . . . . . . . . . . . . . . 12
76 3.3.2. Clock drift . . . . . . . . . . . . . . . . . . . . . 13
77 3.3.3. Bias in the skewness measure . . . . . . . . . . . . . 14
78 3.4. Reducing lag and Improving Responsiveness . . . . . . . . 14
79 3.4.1. Improving the response of the skewness estimate . . . 15
80 3.4.2. Improving the response of the variance estimate . . . 15
81 4. Measuring OWD . . . . . . . . . . . . . . . . . . . . . . . . 16
82 4.1. Time stamp resolution . . . . . . . . . . . . . . . . . . 16
83 5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 16
84 6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16
85 7. Security Considerations . . . . . . . . . . . . . . . . . . . 16
86 8. Change history . . . . . . . . . . . . . . . . . . . . . . . . 17
87 9. References . . . . . . . . . . . . . . . . . . . . . . . . . . 17
88 9.1. Normative References . . . . . . . . . . . . . . . . . . . 17
89 9.2. Informative References . . . . . . . . . . . . . . . . . . 17
90 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 18
92 1. Introduction
94 In the Internet, it is not normally known if flows (e.g., TCP
95 connections or UDP data streams) traverse the same bottlenecks. Even
96 flows that have the same sender and receiver may take different paths
97 and share a bottleneck or not. Flows that share a bottleneck link
98 usually compete with one another for their share of the capacity.
99 This competition has the potential to increase packet loss and
100 delays. This is especially relevant for interactive applications
101 that communicate simultaneously with multiple peers (such as multi-
102 party video). For RTP media applications such as RTCWEB,
103 [I-D.welzl-rmcat-coupled-cc] describes a scheme that combines the
104 congestion controllers of flows in order to honor their priorities
105 and avoid unnecessary packet loss as well as delay. This mechanism
106 relies on some form of Shared Bottleneck Detection (SBD); here, a
107 measurement-based SBD approach is described.
109 1.1. The signals
111 The current Internet is unable to explicitly inform endpoints as to
112 which flows share bottlenecks, so endpoints need to infer this from
113 whatever information is available to them. The mechanism described
114 here currently utilises packet loss and packet delay, but is not
115 restricted to these.
117 1.1.1. Packet Loss
119 Packet loss is often a relatively rare signal. Therefore, on its own
120 it is of limited use for SBD, however, it is a valuable supplementary
121 measure when it is more prevalent.
123 1.1.2. Packet Delay
125 End-to-end delay measurements include noise from every device along
126 the path in addition to the delay perturbation at the bottleneck
127 device. The noise is often significantly increased if the round-trip
128 time is used. The cleanest signal is obtained by using One-Way-Delay
129 (OWD).
131 Measuring absolute OWD is difficult since it requires both the sender
132 and receiver clocks to be synchronised. However, since the
133 statistics being collected are relative to the mean OWD, a relative
134 OWD measurement is sufficient. Clock drift is not usually
135 significant over the time intervals used by this SBD mechanism (see
136 [RFC6817] A.2 for a discussion on clock drift and OWD measurements).
137 However, in circumstances where it is significant, Section 3.3.2
138 outlines a way of adjusting the calculations to cater for it.
140 Each packet arriving at the bottleneck buffer may experience very
141 different queue lengths, and therefore different waiting times. A
142 single OWD sample does not, therefore, characterize the path well.
143 However, multiple OWD measurements do reflect the distribution of
144 delays experienced at the bottleneck.
146 1.1.3. Path Lag
148 Flows that share a common bottleneck may traverse different paths,
149 and these paths will often have different base delays. This makes it
150 difficult to correlate changes in delay or loss. This technique uses
151 the long term shape of the delay distribution as a base for
152 comparison to counter this.
154 2. Definitions
156 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
157 "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
158 document are to be interpreted as described in RFC 2119 [RFC2119].
160 Acronyms used in this document:
162 OWD -- One Way Delay
164 PDV -- Packet Delay Variation
166 RTT -- Round Trip Time
168 SBD -- Shared Bottleneck Detection
170 Conventions used in this document:
172 T -- the base time interval over which measurements are
173 made.
175 N -- the number of base time, T, intervals used in some
176 calculations.
178 sum_T(...) -- summation of all the measurements of the variable
179 in parentheses taken over the interval T
181 sum(...) -- summation of terms of the variable in parentheses
183 sum_N(...) -- summation of N terms of the variable in parentheses
184 sum_NT(...) -- summation of all measurements taken over the
185 interval N*T
187 E_T(...) -- the expectation or mean of the measurements of the
188 variable in parentheses over T
190 E_N(...) -- The expectation or mean of the last N values of the
191 variable in parentheses
193 E_M(...) -- The expectation or mean of the last M values of the
194 variable in parentheses, where M <= N.
196 max_T(...) -- the maximum recorded measurement of the variable in
197 parentheses taken over the interval T
199 min_T(...) -- the minimum recorded measurement of the variable in
200 parentheses taken over the interval T
202 num_T(...) -- the count of measurements of the variable in
203 parentheses taken in the interval T
205 num_VM(...) -- the count of valid values of the variable in
206 parentheses given M records
208 PC -- a boolean variable indicating the particular flow was
209 identified as experiencing congestion in the previous
210 interval T (i.e. Previously Congested)
212 CD_T -- an estimate of the effect of Clock Drift on the mean
213 OWD per T
215 CD_Adj(...) -- Mean OWD adjusted for clock drift
217 p_l, p_f, p_pdv, c_s, c_h, p_s, p_d, p_v -- various thresholds
218 used in the mechanism.
220 N, M, and F -- number of values (calculated over T).
222 2.1. Parameter Values
224 Reference [Hayes-LCN14] uses T=350ms, N=50, p_l = 0.1. The other
225 parameters have been tightened to reflect minor enhancements to the
226 algorithm outlined in Section 3.3: c_s = -0.01, p_f = p_s = p_d =
227 0.1, p_pdv = 0.2, p_v = 0.2. M=50, F=10, and c_h = 0.3 are
228 additional parameters defined in the document. These are values that
229 seem to work well over a wide range of practical Internet conditions,
230 but are the subject of ongoing tests.
232 3. Mechanism
234 The mechanism described in this document is based on the observation
235 that the distribution of delay measurements of packets from flows
236 that share a common bottleneck have similar shape characteristics.
237 These shape characteristics are described using 3 key summary
238 statistics:
240 variance (estimate var_est, see Section 3.1.3)
242 skewness (estimate skew_est, see Section 3.1.2)
244 oscillation (estimate freq_est, see Section 3.1.4)
246 with packet loss (estimate pkt_loss, see Section 3.1.5) used as a
247 supplementary statistic.
249 Summary statistics help to address both the noise and the path lag
250 problems by describing the general shape over a relatively long
251 period of time. This is sufficient for their application in coupled
252 congestion control for RTP Media. They can be signalled from a
253 receiver, which measures the OWD and calculates the summary
254 statistics, to a sender, which is the entity that is transmitting the
255 media stream. An RTP Media device may be both a sender and a
256 receiver. SBD can be performed at either Sender or receiver or both.
258 +----+
259 | H2 |
260 +----+
261 |
262 | L2
263 |
264 +----+ L1 | L3 +----+
265 | H1 |------|------| H3 |
266 +----+ +----+
268 A network with 3 hosts (H1, H2, H3) and 3 links (L1, L2, L3).
270 Figure 1
272 In Figure 1, there are two possible cases for shared bottleneck
273 detection: a sender-based and a receiver-based case.
275 1. Sender-based: consider a situation where host H1 sends media
276 streams to hosts H2 and H3, and L1 is a shared bottleneck. H2
277 and H3 measure the OWD and calculate summary statistics, which
278 they send to H1 every T. H1, having this knowledge, can determine
279 the shared bottleneck and accordingly control the send rates.
281 2. Receiver-based: consider that H2 is also sending media to H3, and
282 L3 is a shared bottleneck. If H3 sends summary statistics to H1
283 and H2, neither H1 nor H2 alone obtain enough knowledge to detect
284 this shared bottleneck; H3 can however determine it by combining
285 the summary statistics related to H1 and H2, respectively. This
286 case is applicable when send rates are controlled by the
287 receiver; then, the signal from H3 to the senders contains the
288 sending rate.
290 A discussion of the required signalling for the receiver-based case
291 is beyond the scope of this document. For the sender-based case, the
292 messages and their data format will be defined here in future
293 versions of this document. We envision that an initialization
294 message from the sender to the receiver could specify which key
295 metrics are requested out of a possibly extensible set (pkt_loss,
296 var_est, skew_est, freq_est). The grouping algorithm described in
297 this document requires all four of these metrics, and receivers MUST
298 be able to provide them, but future algorithms may be able to exploit
299 other metrics (e.g. metrics based on explicit network signals).
300 Moreover, the initialization message could specify T, N, and the
301 necessary resolution and precision (number of bits per field).
303 3.1. Key metrics and their calculation
305 Measurements are calculated over a base interval, T. T should be long
306 enough to provide enough samples for a good estimate of skewness, but
307 short enough so that a measure of the oscillation can be made from N
308 of these estimates. Reference [Hayes-LCN14] uses T = 350ms and
309 N=M=50, which are values that seem to work well over a wide range of
310 practical Internet conditions.
312 3.1.1. Mean delay
314 The mean delay is not a useful signal for comparisons between flows
315 since flows may traverse quite different paths and clocks will not
316 necessarily be synchronized. However, it is a base measure for the 3
317 summary statistics. The mean delay, E_T(OWD), is the average one way
318 delay measured over T.
320 To facilitate the other calculations, the last N E_T(OWD) values will
321 need to be stored in a cyclic buffer along with the moving average of
322 E_T(OWD):
324 mean_delay = E_M(E_T(OWD)) = sum_M(E_T(OWD)) / M
326 where M <= N. Generally M=N, setting M to be less than N allows the
327 mechanism to be more responsive to changes, but potentially at the
328 expense of a higher error rate (see Section 3.4 for a discussion on
329 improving the responsiveness of the mechanism.)
331 3.1.2. Skewness Estimate
333 Skewness is difficult to calculate efficiently and accurately.
334 Ideally it should be calculated over the entire period (M * T) from
335 the mean OWD over that period. However this would require storing
336 every delay measurement over the period. Instead, an estimate is
337 made over T using the previous calculation of mean_delay.
338 Comparisons are made using the mean of M skew estimates (an
339 alternative that removes bias in the mean is given in Section 3.3.3).
341 The skewness is estimated using two counters, counting the number of
342 one way delay samples (OWD) above and below the mean:
344 skew_est_T = (sum_T(OWD < mean_delay)
346 - sum_T(OWD > mean_delay)) / num_T(OWD)
348 where
350 if (OWD < mean_delay) 1 else 0
352 if (OWD > mean_delay) 1 else 0
354 skew_est_T is a number between -1 and 1
356 skew_est = E_M(skew_est_T) = sum_M(skew_est_T) / M
358 For implementation ease, mean_delay does not include the mean of the
359 current T interval.
361 Note: Care must be taken when implementing the comparisons to ensure
362 that rounding does not bias skew_est. It is important that the mean
363 is calculated with a higher precision than the samples.
365 3.1.3. Variance Estimate
367 Packet Delay Variation (PDV) ([RFC5481] and [ITU-Y1540]) is used as
368 an estimator of the variance of the delay signal. We define PDV as
369 follows:
371 PDV = PDV_max = max_T(OWD) - E_T(OWD)
373 var_est = E_M(PDV) = sum_M(PDV) / M
375 This modifies PDV as outlined in [RFC5481] to provide a summary
376 statistic version that best aids the grouping decisions of the
377 algorithm (see [Hayes-LCN14] section IVB).
379 The use of PDV = PDV_min = E_T(OWD) - min_T(OWD) is currently being
380 investigated as an alternative that is less sensitive to noise. The
381 drawback of using PDV_min is that it does not distinguish between
382 groups of flows with similar values of skew_est as well as PDV_max
383 (see [Hayes-LCN14] section IVB).
385 3.1.4. Oscillation Estimate
387 An estimate of the low frequency oscillation of the delay signal is
388 calculated by counting and normalising the significant mean,
389 E_T(OWD), crossings of mean_delay:
391 freq_est = number_of_crossings / N
393 Where
395 we define a significant mean crossing as a crossing that
396 extends p_v * var_est from mean_delay. In our experiments we
397 have found that p_v = 0.2 is a good value.
399 Freq_est is a number between 0 and 1. Freq_est can be approximated
400 incrementally as follows:
402 With each new calculation of E_T(OWD) a decision is made as to
403 whether this value of E_T(OWD) significantly crosses the current
404 long term mean, mean_delay, with respect to the previous
405 significant mean crossing.
407 A cyclic buffer, last_N_crossings, records a 1 if there is a
408 significant mean crossing, otherwise a 0.
410 The counter, number_of_crossings, is incremented when there is a
411 significant mean crossing and subtracted from when a non-zero
412 value is removed from the last_N_crossings.
414 This approximation of freq_est was not used in [Hayes-LCN14], which
415 calculated freq_est every T using the current E_N(E_T(OWD)). Our
416 tests show that this approximation of freq_est yields results that
417 are almost identical to when the full calculation is performed every
418 T.
420 3.1.5. Packet loss
422 The proportion of packets lost is used as a supplementary measure:
424 pkt_loss = sum_NT(lost packets) / sum_NT(total packets)
426 Note: When pkt_loss is small it is very variable, however, when
427 pkt_loss is high it becomes a stable measure for making grouping
428 decisions.
430 3.2. Flow Grouping
432 3.2.1. Flow Grouping Algorithm
434 The following grouping algorithm is RECOMMENDED for SBD in the RMCAT
435 context and is sufficient and efficient for small to moderate numbers
436 of flows. For very large numbers of flows (e.g. hundreds), a more
437 complex clustering algorithm may be substituted.
439 Since no single metric is precise enough to group flows (due to
440 noise), the algorithm uses multiple metrics. Each metric offers a
441 different "view" of the bottleneck link characteristics, and used
442 together they enable a more precise grouping of flows than would
443 otherwise be possible.
445 Flows determined to be experiencing congestion are successively
446 divided into groups based on freq_est, var_est, and skew_est.
448 The first step is to determine which flows are experiencing
449 congestion. This is important, since if a flow is not experiencing
450 congestion its delay based metrics will not describe the bottleneck,
451 but the "noise" from the rest of the path. Skewness, with proportion
452 of packets loss as a supplementary measure, is used to do this:
454 1. Grouping will be performed on flows where:
456 skew_est < c_s
458 || ( skew_est < c_h && PC )
460 || pkt_loss > p_l
462 The parameter c_s controls how sensitive the mechanism is in
463 detecting congestion. C_s = 0.0 was used in [Hayes-LCN14]. A value
464 of c_s = 0.05 is a little more sensitive, and c_s = -0.05 is a little
465 less sensitive. C_h controls the hysteresis on flows that were
466 grouped as experiencing congestion last time.
468 These flows, flows experiencing congestion, are then progressively
469 divided into groups based on the freq_est, PDV, and skew_est summary
470 statistics. The process proceeds according to the following steps:
472 2. Group flows whose difference in sorted freq_est is less than a
473 threshold:
475 diff(freq_est) < p_f
477 3. Group flows whose difference in sorted E_N(PDV) (highest to
478 lowest) is less than a threshold:
480 diff(var_est) < (p_pdv * var_est)
482 The threshold, (p_pdv * var_est), is with respect to the highest
483 value in the difference.
485 4. Group flows whose difference in sorted skew_est or pkt_loss is
486 less than a threshold:
488 if pkt_loss < p_l
490 diff(skew_est) < p_s
492 otherwise
494 diff(pkt_loss) < (p_d * pkt_loss)
496 The threshold, (p_d * pkt_loss), is with respect to the
497 highest value in the difference.
499 This procedure involves sorting estimates from highest to lowest. It
500 is simple to implement, and efficient for small numbers of flows,
501 such as are expected in RTCWEB.
503 3.2.2. Using the flow group signal
505 A grouping decisions is made every T from the second T, though they
506 will not attain their full design accuracy until after the N'th T
507 interval.
509 Network conditions, and even the congestion controllers, can cause
510 bottlenecks to fluctuate. A coupled congestion controller MAY decide
511 only to couple groups that remain stable, say grouped together 90% of
512 the time, depending on its objectives. Recommendations concerning
513 this are beyond the scope of this draft and will be specific to the
514 coupled congestion controllers objectives.
516 3.3. Removing Noise from the Estimates
518 The following describe small changes to the calculation of the key
519 metrics that help remove noise from them. Currently these "tweaks"
520 are described separately to keep the main description succinct. In
521 future revisions of the draft these enhancements may replace the
522 original key metric calculations.
524 3.3.1. Oscillation noise
526 When a path has no congestion, the PDV will be very small and the
527 recorded significant mean crossings will be the result of path noise.
528 Thus up to N-1 meaningless mean crossings can be a source of error at
529 the point a link becomes a bottleneck and flows traversing it begin
530 to be grouped.
532 To remove this source of noise from freq_est:
534 1. Set the current PDV to PDV = NaN (a value representing an invalid
535 record, ie Not a Number) for flows that are deemed to not be
536 experiencing congestion by the first skew_est based grouping test
537 (see Section 3.2.1).
539 2. Then var_est = sum_M(PDV != NaN) / num_VM(PDV)
541 3. For freq_est, only record a significant mean crossing if flow is
542 experiencing congestion.
544 These three changes will remove the non-congestion noise from
545 freq_est.
547 3.3.2. Clock drift
549 Generally sender and receiver clock drift will be too small to cause
550 significant errors in the estimators. Skew_est is most sensitive to
551 this type of noise. In circumstances where clock drift is high,
552 making M < N can reduce this error.
554 A better method is to estimate the effect the clock drift is having
555 on the E_N(E_T(OWD)), and then adjust mean_delay accordingly. A
556 simple method of doing this follows:
558 First divide the N E_T(OWD) values into two halves (N/2 in each)
559 -- old and new.
561 Calculate a mean of the old half:
563 Older_mean = E_old(E_T(OWD)) / N/2
565 Calculate a mean of the new (most recent) half:
567 Newer_mean = E_new(E_T(OWD)) / N/2
569 A linear estimate of the Clock Drift per T estimates is:
571 CD_T = (Newer_mean - Older_mean)/N/2
573 An adjusted mean estimate then is:
575 mean_delay = CD_Adj(E_M(E_T(OWD))) = E_M(E_T(OWD)) + CD_T * M/2
577 CD_Adj can be thought of as a prediction of what the long term mean
578 will be in the current measurement period T. It is used as the basis
579 for skew_est and freq_est.
581 3.3.3. Bias in the skewness measure
583 If successive calculations of skew_est are made with very different
584 numbers of samples (num_T(OWD)), the simple calculation of
585 E_M(skew_est) used for grouping decisions will be biased by the
586 intervals that have few samples samples. This bias can be corrected
587 if necessary as follows.
589 skew_base_T = (sum_T(OWD < mean_delay
591 - sum_T(OWD > mean_delay)
593 skew_est = sum_MT(skew_base_T)/num_MT(OWD)
595 This calculation requires slightly more state, since an
596 implementation will need to maintain two cyclic buffers storing
597 skew_base_T and num_T(OWD) respectively to manage the rolling
598 summations (note only one cyclic buffer is needed for the calculation
599 of skew_est outlined previously).
601 3.4. Reducing lag and Improving Responsiveness
603 Measurement based shared bottleneck detection makes decisions in the
604 present based on what has been measured in the past. This means that
605 there is always a lag in responding to changing conditions. This
606 mechanism is based on summary statistics taken over (N*T) seconds.
607 This mechanism can be made more responsive to changing conditions by:
609 1. Reducing N and/or M -- but at the expense of less accurate
610 metrics, and/or
612 2. Exploiting the fact that more recent measurements are more
613 valuable than older measurements and weighting them accordingly.
615 Although more recent measurements are more valuable, older
616 measurements are still needed to gain an accurate estimate of the
617 distribution descriptor we are measuring. Unfortunately, the simple
618 exponentially weighted moving average weights drop off too quickly
619 for our requirements and have an infinite tail. A simple linearly
620 declining weighted moving average also does not provide enough weight
621 to the most recent measurements. We propose a piecewise linear
622 distribution of weights, such that the first section (samples 1:F) is
623 flat as in a simple moving average, and the second section (samples
624 F+1:M) is linearly declining weights to the end of the averaging
625 window. We choose integer weights, which allows incremental
626 calculation without introducing rounding errors.
628 3.4.1. Improving the response of the skewness estimate
630 The weighted moving average for skew_est, based on skew_est in
631 Section 3.3.3, can be calculated as follows:
633 skew_est = ((M-F+1)*sum(skew_base_T(1:F))
635 + sum([(M-F):1].*skew_base_T(F+1:M)))
637 / ((M-F+1)*sum(numsampT(1:F))
639 + sum([(M-F):1].*numsampT(F+1:M)))
641 where numsampT is an array of the number of OWD samples in each T (ie
642 num_T(OWD)), and numsampT(1) is the most recent; skew_base_T(1) is
643 the most recent calculation of skew_base_T; 1:F refers to the integer
644 values 1 through to F, and [(M-F):1] refers to an array of the
645 integer values (M-F) declining through to 1; and ".*" is the array
646 scalar dot product operator.
648 3.4.2. Improving the response of the variance estimate
650 The weighted moving average for var_est can be calculated as follows:
652 var_est = ((M-F+1)*sum(PDV(1:F)) + sum([(M-F):1].*PDV(F+1:M)))
654 / (F*(M-F+1) + sum([(M-F):1])
656 where 1:F refers to the integer values 1 through to F, and [(M-F):1]
657 refers to an array of the integer values (M-F) declining through to
658 1; and ".*" is the array scalar dot product operator. When removing
659 oscillation noise (see Section 3.3.1) this calculation must be
660 adjusted to allow for invalid PDV records.
662 4. Measuring OWD
664 This section discusses the OWD measurements required for this
665 algorithm to detect shared bottlenecks.
667 The SBD mechanism described in this draft relies on differences
668 between OWD measurements to avoid the practical problems with
669 measuring absolute OWD (see [Hayes-LCN14] section IIIC). Since all
670 summary statistics are relative to the mean OWD and sender/receiver
671 clock offsets should be approximately constant over the measurement
672 periods, the offset is subtracted out in the calculation.
674 4.1. Time stamp resolution
676 The SBD mechanism requires timing information precise enough to be
677 able to make comparisons. As a rule of thumb, the time resolution
678 should be less than one hundredth of a typical path's range of
679 delays. In general, the lower the time resolution, the more care
680 that needs to be taken to ensure rounding errors do not bias the
681 skewness calculation.
683 Typical RTP media flows use sub-millisecond timers, which should be
684 adequate in most situations.
686 5. Acknowledgements
688 This work was part-funded by the European Community under its Seventh
689 Framework Programme through the Reducing Internet Transport Latency
690 (RITE) project (ICT-317700). The views expressed are solely those of
691 the authors.
693 6. IANA Considerations
695 This memo includes no request to IANA.
697 7. Security Considerations
699 The security considerations of RFC 3550 [RFC3550], RFC 4585
700 [RFC4585], and RFC 5124 [RFC5124] are expected to apply.
702 Non-authenticated RTCP packets carrying shared bottleneck indications
703 and summary statistics could allow attackers to alter the bottleneck
704 sharing characteristics for private gain or disruption of other
705 parties communication.
707 8. Change history
709 Changes made to this document:
711 01->02 : New section describing improvements to the key metric
712 calculations that help to remove noise, bias, and
713 reduce lag. Some revisions to the notation to make
714 it clearer. Some tightening of the thresholds.
716 00->01 : Revisions to terminology for clarity
718 9. References
720 9.1. Normative References
722 [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
723 Requirement Levels", BCP 14, RFC 2119, March 1997.
725 9.2. Informative References
727 [Hayes-LCN14]
728 Hayes, D., Ferlin, S., and M. Welzl, "Practical Passive
729 Shared Bottleneck Detection using Shape Summary
730 Statistics", Proc. the IEEE Local Computer Networks
731 (LCN) p150-158, September 2014, .
735 [I-D.welzl-rmcat-coupled-cc]
736 Welzl, M., Islam, S., and S. Gjessing, "Coupled congestion
737 control for RTP media", draft-welzl-rmcat-coupled-cc-04
738 (work in progress), October 2014.
740 [ITU-Y1540]
741 ITU-T, "Internet Protocol Data Communication Service - IP
742 Packet Transfer and Availability Performance Parameters",
743 Series Y: Global Information Infrastructure, Internet
744 Protocol Aspects and Next-Generation Networks ,
745 March 2011,
746 .
748 [RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
749 Jacobson, "RTP: A Transport Protocol for Real-Time
750 Applications", STD 64, RFC 3550, July 2003.
752 [RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
753 "Extended RTP Profile for Real-time Transport Control
754 Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585,
755 July 2006.
757 [RFC5124] Ott, J. and E. Carrara, "Extended Secure RTP Profile for
758 Real-time Transport Control Protocol (RTCP)-Based Feedback
759 (RTP/SAVPF)", RFC 5124, February 2008.
761 [RFC5481] Morton, A. and B. Claise, "Packet Delay Variation
762 Applicability Statement", RFC 5481, March 2009.
764 [RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
765 "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
766 December 2012.
768 Authors' Addresses
770 David Hayes (editor)
771 University of Oslo
772 PO Box 1080 Blindern
773 Oslo, N-0316
774 Norway
776 Phone: +47 2284 5566
777 Email: davihay@ifi.uio.no
779 Simone Ferlin
780 Simula Research Laboratory
781 P.O.Box 134
782 Lysaker, 1325
783 Norway
785 Phone: +47 4072 0702
786 Email: ferlin@simula.no
788 Michael Welzl
789 University of Oslo
790 PO Box 1080 Blindern
791 Oslo, N-0316
792 Norway
794 Phone: +47 2285 2420
795 Email: michawe@ifi.uio.no