The implementors are encouraged to choose evaluation settings from the following values initially:
Experiments are expected to verify that the congestion control is able to work across a broad range of path characteristics, including challenging situations, for example, over transcontinental and/or satellite links. Tests thus account for the following different latencies:
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Very low latency: 0-1 ms
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Low latency: 50 ms
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High latency: 150 ms
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Extreme latency: 300 ms
Many paths in the Internet today are largely lossless; however, in scenarios featuring interference in wireless networks, sending to and receiving from remote regions, or high/fast mobility, media flows may exhibit substantial packet loss. This variety needs to be reflected appropriately by the tests.
To model a wide range of lossy links, the experiments can choose one of the following loss rates; the fractional loss is the ratio of packets lost and packets sent:
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no loss: 0%
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1%
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5%
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10%
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20%
Routers should be configured to use drop-tail queues in the experiments due to their (still) prevalent nature. Experimentation with Active Queue Management (AQM) schemes is encouraged but not mandatory.
The router queue length is measured as the time taken to drain the FIFO queue. It has been noted in various discussions that the queue length in the currently deployed Internet varies significantly. While the core backbone network has very short queue length, the home gateways usually have larger queue length. Those various queue lengths can be categorized in the following way:
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QoS-aware (or short): 70 ms
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Nominal: 300-500 ms
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Buffer-bloated: 1000-2000 ms
Here the size of the queue is measured in bytes or packets. To convert the queue length measured in seconds to queue length in bytes:
QueueSize (in bytes) = QueueSize (in sec) x Throughput (in bps)/8
Many models for generating packet loss are available: some generate correlated packet losses, others generate independent packet losses. In addition, packet losses can also be extracted from packet traces. As a (simple) minimum loss model with minimal parameterization (i.e., the loss rate), independent random losses must be used in the evaluation.
It is known that independent loss models may reflect reality poorly, and hence more sophisticated loss models could be considered. Suitable models for correlated losses include the Gilbert-Elliot model [
gilbert-elliott] and models that generate losses by modeling a queue with its (different) drop behaviors.
This section defines jitter models for the purposes of this document. When jitter is to be applied to both the congestion-controlled RTP flow and any competing flow (such as a TCP competing flow), the competing flow will use the jitter definition below that does not allow for reordering of packets on the competing flow (see NR-BPDV definition below).
Jitter is an overloaded term in communications. It is typically used to refer to the variation of a metric (e.g., delay) with respect to some reference metric (e.g., average delay or minimum delay). For example in
RFC 3550, jitter is computed as the smoothed difference in packet arrival times relative to their respective expected arrival times, which is particularly meaningful if the underlying packet delay variation was caused by a Gaussian random process.
Because jitter is an overloaded term, we use the term Packet Delay Variation (PDV) instead to describe the variation of delay of individual packets in the same sense as the IETF IP Performance Metrics (IPPM) working group has defined PDV in their documents (e.g.,
RFC 3393) and as the ITU-T SG16 has defined IP Packet Delay Variation (IPDV) in their documents (e.g., Y.1540).
Most PDV distributions in packet network systems are one-sided distributions, the measurement of which with a finite number of measurement samples results in one-sided histograms. In the usual packet network transport case, there is typically one packet that transited the network with the minimum delay; a (large) number of packets transit the network within some (smaller) positive variation from this minimum delay, and a (small) number of the packets transit the network with delays higher than the median or average transit time (these are outliers). Although infrequent, outliers can cause significant deleterious operation in adaptive systems and should be considered in rate adaptation designs for RTP congestion control.
In this section we define two different bounded PDV characteristics, 1) Random Bounded PDV and 2) Approximately Random Subject to No-Reordering Bounded PDV.
The former, 1) Random Bounded PDV, is presented for information only, while the latter, 2) Approximately Random Subject to No-Reordering Bounded PDV, must be used in the evaluation.
The RBPDV probability distribution function (PDF) is specified to be of some mathematically describable function that includes some practical minimum and maximum discrete values suitable for testing. For example, the minimum value, x_min, might be specified as the minimum transit time packet, and the maximum value, x_max, might be defined to be two standard deviations higher than the mean.
Since we are typically interested in the distribution relative to the mean delay packet, we define the zero mean PDV sample, z(n), to be z(n) = x(n) - x_mean, where x(n) is a sample of the RBPDV random variable x and x_mean is the mean of x.
We assume here that s(n) is the original source time of packet n and the post-jitter induced emission time, j(n), for packet n is:
j(n) = {[z(n) + x_mean] + s(n)}.
It follows that the separation in the post-jitter time of packets n and n+1 is {[s(n+1)-s(n)] - [z(n)-z(n+1)]}. Since the first term is always a positive quantity, we note that packet reordering at the receiver is possible whenever the second term is greater than the first. Said another way, whenever the difference in possible zero mean PDV sample delays (i.e., [x_max-x_min]) exceeds the inter-departure time of any two sent packets, we have the possibility of packet reordering.
There are important use cases in real networks where packets can become reordered, such as in load-balancing topologies and during route changes. However, for the vast majority of cases, there is no packet reordering because most of the time packets follow the same path. Due to this, if a packet becomes overly delayed, the packets after it on that flow are also delayed. This is especially true for mobile wireless links where there are per-flow queues prior to base station scheduling. Owing to this important use case, we define another PDV profile similar to the above, but one that does not allow for reordering within a flow.
No Reordering BPDV, NR-BPDV, is defined similarly to the above with one important exception. Let serial(n) be defined as the serialization delay of packet n at the lowest bottleneck link rate (or other appropriate rate) in a given test. Then we produce all the post-jitter values for j(n) for n = 1, 2, ... N, where N is the length of the source sequence s to be offset. The exception can be stated as follows: We revisit all j(n) beginning from index n=2, and if j(n) is determined to be less than [j(n-1)+serial(n-1)], we redefine j(n) to be equal to [j(n-1)+serial(n-1)] and continue for all remaining n (i.e., n = 3, 4, .. N). This models the case where the packet n is sent immediately after packet (n-1) at the bottleneck link rate. Although this is generally the theoretical minimum in that it assumes that no other packets from other flows are in between packet n and n+1 at the bottleneck link, it is a reasonable assumption for per-flow queuing.
We note that this assumption holds for some important exception cases, such as packets immediately following outliers. There are a multitude of software-controlled elements common on end-to-end Internet paths (such as firewalls, application-layer gateways, and other middleboxes) that stop processing packets while servicing other functions (e.g., garbage collection). Often these devices do not drop packets, but rather queue them for later processing and cause many of the outliers. Thus NR-BPDV models this particular use case (assuming serial(n+1) is defined appropriately for the device causing the outlier) and is believed to be important for adaptation development for congestion-controlled RTP streams.
Whether Random Bounded PDV or Approximately Random Subject to No-Reordering Bounded PDV, it is recommended that z(n) is distributed according to a truncated Gaussian for the above jitter models:
z(n) ~ |max(min(N(0, std
2), N_STD * std), -N_STD * std)|
where N(0, std
2) is the Gaussian distribution with zero mean and std is standard deviation. Recommended values: