Internet-Draft Responsiveness under Working Conditions October 2021
Paasch, et al. Expires 28 April 2022 [Page]
IP Performance Measurement
Intended Status:
C. Paasch
Apple Inc.
R. Meyer
Apple Inc.
S. Cheshire
Apple Inc.
O. Shapira
Apple Inc.

Responsiveness under Working Conditions


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

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This Internet-Draft will expire on 28 April 2022.

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.

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:

    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":

  • Should not waste traffic, since the person may be paying for it
  • 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.

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.

4.1.3. 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.

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:

  • Create 4 load bearing connections
  • 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%:

      • 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 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.
  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": "",
        "large_https_download_url": "",
        "https_upload_url": ""

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.

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

Gettys, J. and K. Nichols, "Bufferbloat: Dark Buffers in the Internet", Communications of the ACM, Volume 55, Number 1 (2012) , n.d..
Postel, J., "Transmission Control Protocol", STD 7, RFC 793, DOI 10.17487/RFC0793, , <>.
Mockapetris, P., "Domain names - implementation and specification", STD 13, RFC 1035, DOI 10.17487/RFC1035, , <>.
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, , <>.
Bray, T., Ed., "The JavaScript Object Notation (JSON) Data Interchange Format", STD 90, RFC 8259, DOI 10.17487/RFC8259, , <>.
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, , <>.
Rescorla, E., "The Transport Layer Security (TLS) Protocol Version 1.3", RFC 8446, DOI 10.17487/RFC8446, , <>.

Authors' Addresses

Christoph Paasch
Apple Inc.
One Apple Park Way
Cupertino, California 95014,
United States of America
Randall Meyer
Apple Inc.
One Apple Park Way
Cupertino, California 95014,
United States of America
Stuart Cheshire
Apple Inc.
One Apple Park Way
Cupertino, California 95014,
United States of America
Omer Shapira
Apple Inc.
One Apple Park Way
Cupertino, California 95014,
United States of America