Internet DRAFT - draft-cpaasch-ippm-responsiveness
draft-cpaasch-ippm-responsiveness
IP Performance Measurement C. Paasch
Internet-Draft R. Meyer
Intended status: Experimental S. Cheshire
Expires: April 28, 2022 O. Shapira
Apple Inc.
October 25, 2021
Responsiveness under Working Conditions
draft-cpaasch-ippm-responsiveness-01
Abstract
For many years, a lack of responsiveness, variously called lag,
latency, or bufferbloat, has been recognized as an unfortunate, but
common symptom in today's networks. Even after a decade of work on
standardizing technical solutions, it remains a common problem for
the end users.
Everyone "knows" that it is "normal" for a video conference to have
problems when somebody else at home is watching a 4K movie or
uploading photos from their phone. However, there is no technical
reason for this to be the case. In fact, various queue management
solutions (fq_codel, cake, PIE) have solved the problem for tens of
thousands of people.
Our networks remain unresponsive, not from a lack of technical
solutions, but rather a lack of awareness of the problem. We believe
that creating a tool whose measurement matches people's every day
experience will create the necessary awareness, and result in a
demand for products that solve the problem.
This document specifies the "RPM Test" for measuring responsiveness.
It uses common protocols and mechanisms to measure user experience
especially when the network is fully loaded ("responsiveness under
working conditions".) The measurement is expressed as "Round-trips
Per Minute" (RPM) and should be included with throughput (up and
down) and idle latency as critical indicators of network quality.
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 https://datatracker.ietf.org/drafts/current/.
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This Internet-Draft will expire on April 28, 2022.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Terminology . . . . . . . . . . . . . . . . . . . . . . . 3
2. Design Constraints . . . . . . . . . . . . . . . . . . . . . 4
3. Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4. Measuring Responsiveness Under Working Conditions . . . . . . 5
4.1. Working Conditions . . . . . . . . . . . . . . . . . . . 5
4.1.1. From single-flow to multi-flow . . . . . . . . . . . 6
4.1.2. Parallel vs Sequential Uplink and Downlink . . . . . 6
4.1.3. Reaching saturation . . . . . . . . . . . . . . . . . 6
4.1.4. Final "Working Conditions" Algorithm . . . . . . . . 7
4.2. Measuring Responsiveness . . . . . . . . . . . . . . . . 8
4.2.1. Aggregating the Measurements . . . . . . . . . . . . 8
4.2.2. Statistical Confidence . . . . . . . . . . . . . . . 9
5. RPM Test Server API . . . . . . . . . . . . . . . . . . . . . 9
6. Security Considerations . . . . . . . . . . . . . . . . . . . 10
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10
8. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 10
9. Informative References . . . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
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1. Introduction
For many years, a lack of responsiveness, variously called lag,
latency, or bufferbloat, has been recognized as an unfortunate, but
common symptom in today's networks [Bufferbloat]. Solutions like
fq_codel [RFC8290] or PIE [RFC8033] have been standardized and are to
some extent widely implemented. Nevertheless, people still suffer
from bufferbloat.
Although significant, the impact on user experience can be transitory
- that is, its effect is not always present. Whenever a network is
actively being used at its full capacity, buffers can fill up and
create latency for traffic. The duration of those full buffers may
be brief: a medium-sized file transfer, like an email attachment or
uploading photos, can create bursts of latency spikes. An example of
this is lag occurring during a videoconference, where a connection is
briefly shown as unstable.
These short-lived disruptions make it hard to narrow down the cause.
We believe that it is necessary to create a standardized way to
measure and express responsiveness.
Existing network measurement tools could incorporate a responsiveness
measurement into their set of metrics. Doing so would also raise the
awareness of the problem and make the standard "network quality
measures" of throughput, idle latency, and responsiveness.
1.1. Terminology
A word about the term "bufferbloat" - the undesirable latency that
comes from a router or other network equipment buffering too much
data. This document uses the term as a general description of bad
latency, using more precise wording where warranted.
"Latency" is a poor measure of responsiveness, since it can be hard
for the general public to understand. The units are unfamiliar
("what is a millisecond?") and counterintuitive ("100 msec - that
sounds good - it's only a tenth of a second!").
Instead, we create the term "Responsiveness under working conditions"
to make it clear that we are measuring all, not just idle,
conditions, and use "round-trips per minute" as the metric. The
values range from 50 (poor) to 3,000 (excellent), with the added
advantage that "bigger is better." Finally, we abbreviate the
measurement to "RPM", a wink to the "revolutions per minute" that we
use for cars.
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This document defines an algorithm for the "RPM Test" that explicitly
measures responsiveness under working conditions.
2. Design Constraints
There are many challenges around measurements on the Internet. They
include the dynamic nature of the Internet, the diverse nature of the
traffic, the large number of devices that affect traffic, and the
difficulty of attaining appropriate measurement conditions.
Internet paths are changing all the time. Daily fluctuations in the
demand make the bottlenecks ebb and flow. To minimize the
variability of routing changes, it's best to keep the test duration
relatively short.
TCP and UDP traffic, or traffic on ports 80 and 443, may take
significantly different paths on the Internet and be subject to
entirely different Quality of Service (QoS) treatment. A good test
will use standard transport layer traffic - typical for people's use
of the network - that is subject to the transport's congestion
control that might reduce the traffic's rate and thus its buffering
in the network.
Traditionally, one thinks of bufferbloat happening on the routers and
switches of the Internet. However, the networking stacks of the
clients and servers can have huge buffers. Data sitting in TCP
sockets or waiting for the application to send or read causes
artificial latency, and affects user experience the same way as
"traditional" bufferbloat.
Finally, it is important to note that queueing only happens behind a
slow "bottleneck" link in the network, and only occurs when
sufficient traffic is present. The RPM Test must ensure that buffers
are actually full for a sustained period, and only then make repeated
latency measurements in this particular state.
3. Goals
The algorithm described here defines an RPM Test that serves as a
good proxy for user experience. This means:
1. Today's Internet traffic primarily uses HTTP/2 over TLS. Thus,
the algorithm should use that protocol.
As a side note: other types of traffic are gaining in popularity
(HTTP/3) and/or are already being used widely (RTP). Traffic
prioritization and QoS rules on the Internet may subject traffic
to completely different paths:
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these could also be measured separately.
2. The Internet is marked by the deployment of countless middleboxes
like transparent TCP proxies or traffic prioritization for
certain types of traffic. The RPM Test must take into account
their effect on DNS-request [RFC1035], TCP-handshake [RFC0793],
TLS-handshake, and request/response.
3. The test result should be expressed in an intuitive, nontechnical
form.
4. Finally, to be useful to a wide audience, the measurement should
finish within a short time frame. Our target is 20 seconds.
4. Measuring Responsiveness Under Working Conditions
To make an accurate measurement, the algorithm must reliably put the
network in a state that represents those "working conditions". Once
the network has reached that state, the algorithm can measure its
responsiveness. The following explains how the former and the latter
are achieved.
4.1. Working Conditions
For the purpose of this methodology, typical "working conditions"
represent a state of the network in which the bottleneck node is
experiencing ingress and egress flows similar to those created by
humans in the typical day-to-day pattern.
While a single HTTP transaction might briefly put a network into
working conditions, making reliable measurements requires maintaining
the state over sufficient time.
The algorithm must also detect when the network is in a persistent
working condition, also called "saturation".
Desired properties of "working condition":
o Should not waste traffic, since the person may be paying for it
o Should finish within a short time to avoid impacting other people
on the same network, to avoid varying network conditions, and not
try the person's patience.
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4.1.1. From single-flow to multi-flow
A single TCP connection may not be sufficient to saturate a path.
For example, the 4MB constraints on TCP window size constraints may
not fill the pipe. Additionally, traditional loss-based TCP
congestion control algorithms react aggressively to packet loss by
reducing the congestion window. This reaction (intended by the
protocol design) decreases the queueing within the network, making it
hard to reach saturation.
The goal of the RPM Test is to keep the network as busy as possible
in a sustained and persistent way. It uses multiple TCP connections
and gradually adds more TCP flows until saturation is reached.
4.1.2. Parallel vs Sequential Uplink and Downlink
Poor responsiveness can be caused by queues in either (or both) the
upstream and the downstream direction. Furthermore, both paths may
differ significantly due to access link conditions (e.g., 5G
downstream and LTE upstream) or the routing changes within the ISPs.
To measure responsiveness under working conditions, the algorithm
must saturate both directions.
Measuring in parallel achieves more data samples for a given
duration. Given the desired test duration of 20 seconds, sequential
uplink and downlink tests would only yield half the data. The RPM
Test specifies parallel, concurrent measurements.
However, a number of caveats come with measuring in parallel:
o Half-duplex links may not permit simultaneous uplink and downlink
traffic. This means the test might not saturate both directions
at once.
o Debuggability of the results becomes harder: During parallel
measurement it is impossible to differentiate whether the observed
latency happens in the uplink or the downlink direction.
o Consequently, the test should have an option for sequential
testing.
4.1.3. Reaching saturation
The RPM Test gradually increases the number of TCP connections and
measures "goodput" - the sum of actual data transferred across all
connections in a unit of time. When the goodput stops increasing, it
means that saturation has been reached.
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Saturation has two criteria: a) the load bearing connections are
utilizing all the capacity of the bottleneck, b) the buffers in the
bottleneck are completely filled.
The algorithm notes that throughput gradually increases until TCP
connections complete their TCP slow-start phase. At that point,
throughput eventually stalls usually due to receive window
limitations. The only means to further increase throughput is by
adding more TCP connections to the pool of load bearing connections.
If new connections leave the throughput the same, saturation has been
reached and - more importantly - the working condition is stable.
Filling buffers at the bottleneck depends on the congestion control
deployed on the sender side. Congestion control algorithms like BBR
may reach high throughput without causing queueing because the
bandwidth detection portion of BBR effectively seeks the bottleneck
capacity.
RPM Test clients and servers should use loss-based congestion
controls like Cubic to fill queues reliably.
The RPM Test detects saturation when the observed goodput is not
increasing even as connections are being added, or it detects packet
loss or ECN marks signaling congestion or a full buffer of the
bottleneck link.
4.1.4. Final "Working Conditions" Algorithm
The following algorithm reaches working conditions (saturation) of a
network by using HTTP/2 upload (POST) or download (GET) requests of
infinitely large files. The algorithm is the same for upload and
download and uses the same term "load bearing connection" for each.
The steps of the algorithm are:
o Create 4 load bearing connections
o At each 1 second interval:
* Compute "instantaneous aggregate" goodput which is the number
of bytes transferred within the last second.
* Compute a moving average of the last 4 "instantaneous aggregate
goodput" measurements
* If moving average > "previous" moving average + 5%:
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+ Network did not yet reach saturation. If no flows added
within the last 4 seconds, add 4 more flows
* Else, network reached saturation for the current flow count.
+ If new flows added and for 4 seconds the moving average
throughput did not change: network reached stable saturation
+ Else, add four more flows
Note: It is tempting to envision an initial base RTT measurement and
adjust the intervals as a function of that RTT. However, experiments
have shown that this makes the saturation detection extremely
unstable in low RTT environments. In the situation where the
"unloaded" RTT is in the single-digit millisecond range, yet the
network's RTT increases under load to more than a hundred
milliseconds, the intervals become much too low to accurately drive
the algorithm.
4.2. Measuring Responsiveness
Once the network is in a consistent working conditions, the RPM Test
must "probe" the network multiple times to measure its
responsiveness.
Each RPM Test probe measures:
1. The responsiveness of the different steps to create a new
connection, all during working conditions.
To do this, the test measures the time needed to make a DNS
request, establish a TCP connection on port 443, establish a TLS
context using TLS1.3 [RFC8446], and send and receive a one-byte
object with a HTTP/2 GET request. It repeats these steps
multiple times for accuracy.
2. The responsiveness of the network and the client/server
networking stacks for the load bearing connections themselves.
To do this, the load bearing connections multiplex an HTTP/2 GET
request for a one-byte object to get the end-to-end latency on
the connections that are using the network at full speed.
4.2.1. Aggregating the Measurements
The algorithm produces sets of 5 times for each probe, namely: DNS
handshake, TCP handshake, TLS handshake, HTTP/2 request/response on
separate (idle) connections, HTTP/2 request/response on load bearing
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connections. This fine-grained data is useful, but not necessary for
creating a useful metric.
To create a single "Responsiveness" (e.g., RPM) number, this first
iteration of the algorithm gives an equal weight to each of these
values. That is, it sums the five time values for each probe, and
divides by the total number of probes to compute an average probe
duration. The reciprocal of this, normalized to 60 seconds, gives
the Round-trips Per Minute (RPM).
4.2.2. Statistical Confidence
The number of probes necessary for statistical confidence is an open
question. One could imagine a computation of the variance and
confidence interval that would drive the number of measurements and
balance the accuracy with the speed of the measurement itself.
5. RPM Test Server API
The RPM measurement uses standard protocols: no new protocol is
defined.
Both the client and the server MUST support HTTP/2 over TLS 1.3. The
client MUST be able to send a GET request and a POST. The server
MUST be able to respond to both of these HTTP commands. Further, the
server endpoint MUST be accessible through a hostname that can be
resolved through DNS. The server MUST have the ability to provide
content upon a GET request. Both client and server SHOULD use loss-
based congestion controls like Cubic. The server MUST use a packet
scheduling algorithm that minimizes internal queueing to avoid
affecting the client's measurement.
The server MUST respond to 4 URLs:
1. A "small" URL/response: The server must respond with a status
code of 200 and 1 byte in the body. The actual body content is
irrelevant.
2. A "large" URL/response: The server must respond with a status
code of 200 and a body size of at least 8GB. The body can be
bigger, and may need to grow as network speeds increases over
time. The actual body content is irrelevant. The client will
probably never completely download the object, but will instead
close the connection after reaching working condition and making
its measurements.
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3. An "upload" URL/response: The server must handle a POST request
with an arbitrary body size. The server should discard the
payload.
4. A configuration URL that returns a JSON [RFC8259] object with the
information the client uses to run the test (sample below).
Sample JSON:
{
"version": 1,
"urls": {
"small_https_download_url": "https://networkquality.example.com/api/v1/small",
"large_https_download_url": "https://networkquality.example.com/api/v1/large",
"https_upload_url": "https://networkquality.example.com/api/v1/upload"
}
}
The client begins the responsiveness measurement by querying for the
JSON configuration. This supplies the URLs for creating the load
bearing connections in the upstream and downstream direction as well
as the small object for the latency measurements.
6. Security Considerations
TBD
7. IANA Considerations
TBD
8. Acknowledgments
We would like to thank Rich Brown for his editorial pass over this
I-D. We also thank Erik Auerswald for his constructive feedback on
the I-D.
9. Informative References
[Bufferbloat]
Gettys, J. and K. Nichols, "Bufferbloat: Dark Buffers in
the Internet", Communications of the ACM, Volume 55,
Number 1 (2012) , n.d..
[RFC0793] Postel, J., "Transmission Control Protocol", STD 7,
RFC 793, DOI 10.17487/RFC0793, September 1981,
<https://www.rfc-editor.org/info/rfc793>.
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[RFC1035] Mockapetris, P., "Domain names - implementation and
specification", STD 13, RFC 1035, DOI 10.17487/RFC1035,
November 1987, <https://www.rfc-editor.org/info/rfc1035>.
[RFC8033] Pan, R., Natarajan, P., Baker, F., and G. White,
"Proportional Integral Controller Enhanced (PIE): A
Lightweight Control Scheme to Address the Bufferbloat
Problem", RFC 8033, DOI 10.17487/RFC8033, February 2017,
<https://www.rfc-editor.org/info/rfc8033>.
[RFC8259] Bray, T., Ed., "The JavaScript Object Notation (JSON) Data
Interchange Format", STD 90, RFC 8259,
DOI 10.17487/RFC8259, December 2017,
<https://www.rfc-editor.org/info/rfc8259>.
[RFC8290] Hoeiland-Joergensen, T., McKenney, P., Taht, D., Gettys,
J., and E. Dumazet, "The Flow Queue CoDel Packet Scheduler
and Active Queue Management Algorithm", RFC 8290,
DOI 10.17487/RFC8290, January 2018,
<https://www.rfc-editor.org/info/rfc8290>.
[RFC8446] Rescorla, E., "The Transport Layer Security (TLS) Protocol
Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,
<https://www.rfc-editor.org/info/rfc8446>.
Authors' Addresses
Christoph Paasch
Apple Inc.
One Apple Park Way
Cupertino, California 95014
United States of America
Email: cpaasch@apple.com
Randall Meyer
Apple Inc.
One Apple Park Way
Cupertino, California 95014
United States of America
Email: rrm@apple.com
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Stuart Cheshire
Apple Inc.
One Apple Park Way
Cupertino, California 95014
United States of America
Email: cheshire@apple.com
Omer Shapira
Apple Inc.
One Apple Park Way
Cupertino, California 95014
United States of America
Email: oesh@apple.com
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