Low Extra Delay Background Transport (LEDBAT)
draft-ietf-ledbat-congestion-03.txt

Abstract

LEDBAT is an experimental delay-based congestion control algorithm that attempts to utilize the available bandwidth on an end-to-end path while limiting the consequent increase in queueing delay on the path. LEDBAT uses changes in one-way delay measurements to limit congestion induced in the network by the LEDBAT flow. LEDBAT is designed largely for use by background bulk-transfer applications; it is designed to be no more aggressive than TCP congestion control and yields in the presence of competing TCP flows, thus reducing interference with the network performance of the competing flows.

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Table of Contents

1.  Requirements notation
2.  Introduction
    2.1.  Design Goals
    2.2.  Applicability
3.  LEDBAT Congestion Control
    3.1.  Overview
    3.2.  Preliminaries
    3.3.  Receiver-Side Operation
    3.4.  Sender-Side Operation
        3.4.1.  An Overview
        3.4.2.  The Complete Sender Algorithm
    3.5.  Parameter Values
4.  Understanding LEDBAT Mechanisms
    4.1.  Delay Estimation
        4.1.1.  Estimating Base Delay
        4.1.2.  Estimating Queueing Delay
    4.2.  Managing the Congestion Window
        4.2.1.  Window Increase: Probing For More Bandwidth
        4.2.2.  Window Decrease: Responding To Congestion
5.  Choosing Parameter Values
    5.1.  Queuing Delay Target
6.  Discussion
    6.1.  Framing Considerations
    6.2.  Competing With TCP Flows
    6.3.  Fairness Among LEDBAT Flows
7.  IANA Considerations
8.  Security Considerations
9.  Normative References
Appendix A.  Timestamp errors
    A.1.  Clock offset
    A.2.  Clock skew
        A.2.1.  Deployed clock skew correction mechanism
        A.2.2.  Skew correction with faster virtual clock
        A.2.3.  Skew correction with estimating drift
§  Authors' Addresses

1.  Requirements notation

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in [RFC2119] (Bradner, S., “Key words for use in RFCs to Indicate Requirement Levels,” March 1997.).

2.  Introduction

TCP congestion control [RFC2581] (Allman, M., Paxson, V., and W. Stevens, “TCP Congestion Control,” April 1999.), the predominant congestion control mechanism used on the Internet, aims to share bandwidth at a bottleneck link equitably among flows competing at the bottleneck. While TCP works well for many applications, applications such as software updates or file-sharing applications prefer to use bandwidth available in the network without interfering with the network performance of other interactive applications. Such "background" traffic can yield bandwidth to TCP-based "foreground" traffic by reacting earlier than TCP to congestion signals.

LEDBAT is an experimental delay-based congestion control mechanism that allows background applications, such as peer-to-peer applications, that send large amounts of data particularly over links with deep buffers, such as residential uplinks, to operate in the background, without interfering with performance of interactive applications. LEDBAT uses one-way delay measurements to determine congestion on the data path, and keeps latency across the tight link in the end-to-end path low while attempting to utilize the available bandwidth on the end-to-end path.

2.1.  Design Goals

As a "scavenger" mechanism for the Internet, LEDBAT's design goals are to:

1. Keep delay low when no other traffic is present
2. Add little to the queuing delays induced by TCP traffic
3. Quickly yield to traffic sharing the same bottleneck queue that uses standard TCP congestion control
4. Utilize end-to-end available bandwidth
5. Operate well in networks with FIFO queuing with drop-tail discipline

2.2.  Applicability

LEDBAT is a "scavenger" congestion control mechanism---a LEDBAT flow attempts to utilize all available bandwidth and yields quickly to a competing TCP flow---and is primarily motivated by background bulk-transfer applications, such as peer-to-peer file transfers and software updates. It can be used for any application that needs to run in the "background", to reduce the application's impact on the network and on other interactive network applications.

LEDBAT can be used with any transport protocol capable of carrying timestamps and acknowledging data frequently---LEDBAT can be easily used with TCP, SCTP, and DCCP.

3.  LEDBAT Congestion Control

3.1.  Overview

A TCP sender increases its congestion window until a loss occurs, which, in the absence of any Active Queue Management (AQM) in the network, occurs only when the queue at the bottleneck link on the end-to-end path overflows. Since packet loss at the bottleneck link is often preceded by an increase in the queueing delay at the bottleneck link, LEDBAT congestion control uses this increase in queueing delay as an early signal of congestion, enabling it to respond to congestion earlier than TCP, and enabling it to yield bandwidth to a competing TCP flow.

LEDBAT employs one-way delay measurements to estimate queueing delay. When the estimated queueing delay is lesser than a pre-determined target, LEDBAT infers that the network is not yet congested, and increases its sending rate to utilize any spare capacity in the network. When the estimated queueing delay becomes greater than a pre-determined target, LEDBAT decreases its sending rate quickly as a response to potential congestion in the network.

3.2.  Preliminaries

For the purposes of explaining LEDBAT, we assume a transport sender that uses fixed-size segments and a receiver that acknowledges each segment separately. It is straightforward to apply the mechanisms described here with variable-sized segments and with delayed acknowledgments. A LEDBAT sender uses a congestion window (cwnd) that gates the amount of data that the sender can send into the network in one RTT.

LEDBAT requires that each data segment carries a "timestamp" from the sender, based on which the receiver computes the one-way delay from the sender, and sends this computed value back to the sender.

In addition to the LEDBAT mechanism described below, we note that a slow start mechanism can be used as specified in [RFC2581] (Allman, M., Paxson, V., and W. Stevens, “TCP Congestion Control,” April 1999.). Since slow start leads to faster increase in the window than that specified in LEDBAT, conservative congestion control implementations employing LEDBAT may skip slow start altogether and start with an initial window of XXX MSS.

3.3.  Receiver-Side Operation

A LEDBAT receiver operates as follows:

on data_packet:
  remote_timestamp = data_packet.timestamp
  acknowledgement.delay = local_timestamp() - remote_timestamp
  # fill in other fields of acknowledgement
  acknowlegement.send()

3.4.  Sender-Side Operation

3.4.1.  An Overview

As a first approximation, a LEDAT sender operates as show below. TARGET is the maximum queueing delay that LEDBAT itself can introduce in the network, and GAIN determines the rate at which the congestion window changes; both constants are specified later.

on initialization:
  base_delay = +infinity

on acknowledgement:
  current_delay = acknowledgement.delay
  base_delay = min(base_delay, current_delay)
  queuing_delay = current_delay - base_delay
  off_target = TARGET - queuing_delay
  cwnd += GAIN * off_target / cwnd

3.4.2.  The Complete Sender Algorithm

The simplified mechanism above ignores noise filtering and base expiration. The full sender-side algorithm is specified below:

on initialization:
  set all NOISE_FILTER delays used by current_delay() to +infinity
  set all BASE_HISTORY delays used by base_delay() to +infinity
  last_rollover = -infinity # More than a minute in the past.

on acknowledgement:
  delay = acknowledgement.delay
  update_base_delay(delay)
  update_current_delay(delay)
  queuing_delay = current_delay() - base_delay()
  off_target = TARGET - queuing_delay + random_input()
  cwnd += GAIN * off_target / cwnd
  # flight_size() is the amount of currently not acked data.
  max_allowed_cwnd = ALLOWED_INCREASE + TETHER*flight_size()
  cwnd = min(cwnd, max_allowed_cwnd)

random_input()
  # random() is a PRNG between 0.0 and 1.0
  # NB: RANDOMNESS_AMOUNT is normally 0
  RANDOMNESS_AMOUNT * TARGET * ((random() - 0.5)*2)

update_current_delay(delay)
  # Maintain a list of NOISE_FILTER last delays observed.
  forget the earliest of NOISE_FILTER current_delays
  add delay to the end of current_delays

current_delay()
  min(the NOISE_FILTER delays stored by update_current_delay)

update_base_delay(delay)
  # Maintain BASE_HISTORY min delays. Each represents a minute.
  if round_to_minute(now) != round_to_minute(last_rollover)
    last_rollover = now
    forget the earliest of base delays
    add delay to the end of base_delays
  else
    last of base_delays = min(last of base_delays, delay)

base_delay()
  min(the BASE_HISTORY min delays stored by update_base_delay)

3.5.  Parameter Values

TARGET parameter MUST be set to 100 milliseconds. GAIN SHOULD be set to 1 so that max rampup rate is the same as for TCP. BASE_HISTORY SHOULD be 10; it MUST be no less than 2 and SHOULD NOT be more than 20. NOISE_FILTER SHOULD be 1; it MAY be tuned so that it is at least 1 and no more than cwnd/2. ALLOWED_INCREASE SHOULD be 1 packet; it MUST be at least 1 packet and SHOULD NOT be more than 3 packets. TETHER SHOULD be 1.5; it MUST be greater than 1. RANDONMESS_AMOUNT SHOULD be 0; it MUST be between 0 and 0.1 inclusively.

Note that using the same TARGET value across LEDBAT flows is important, since flows using different TARGET values will not share a bottleneck equitably---flows with higher values will get a larger share of the bottleneck bandwidth.

4.  Understanding LEDBAT Mechanisms

This section describes and provides insight into the delay estimation and window management mechanisms used in LEDBAT congestion control.

4.1.  Delay Estimation

LEDBAT estimates congestion in the network based on observed increase in queueing delay in the network. To observe an increase in the queueing delay in the network, LEDBAT must separate the queueing delay component from the rest of the end-to-end delay. This section explains how LEDBAT decomposes the observed changes in end-to-end delay into these two components.

LEDBAT estimates congestion in the direction of data flow. To avoid measuring queue build-up on the reverse path (or ack path), LEDBAT uses changes in one-way delay estimates. The extant One-Way Active Measurement Protocol (OWAMP) [XXXcite], can be used for measuring one-way delay, but since LEDBAT is used for sending data, and since LEDBAT requires only changes in one-way delay to infer congestion, simply adding a timestamp to the data segments and a measurement result field in the ack packets seems sufficient. Doing so also avoids the pitfall of measurement packets being treated differently from the data packets in the network.

4.1.1.  Estimating Base Delay

End-to-end delay can be decomposed into transmission (or serialization) delay, propagation (or speed-of-light) delay, queueing delay, and processing delay. On any given path, barring some noise, all delay components except for queueing delay are constant; over time, we expect only the queueing delay on the path to change as the queue sizes at the links change. Since queuing delay is always additive to the end-to-end delay, we estimate the sum of the constant delay components, which we call "base delay", to be the minimum delay observed on the end-to-end path. Using the minimum observed delay also allows LEDBAT to eliminate noise in the delay estimation, such as due to spikes in processing delay at a node on the path.

To respond to true changes in the base delay due to route changes, LEDBAT uses only "recent" measurements---measurements over the last N minutes---in estimating the base delay. To implement an approximate minimum over the last N minutes, a LEDBAT sender stores N+1 separate minima---N for the last N minutes, and one for the running current minute. At the end of the current minute, the window moves---the earliest minimum is dropped and the latest minimum is added. When the connection is idle for a given minute, no data is available for the one-way delay and, therefore, no minimum is stored. When the connection has been idle for N minutes, the measurement begins anew.

The duration of the observation window itself is a tradeoff between robustness of measurement and responsiveness to change: a larger observation window yields a more accurate base delay if the true base delay is unchanged, whereas a smaller observation window results in faster response to true changes in the base delay.

4.1.2.  Estimating Queueing Delay

Given that the base delay is constant, the queueing delay is represented by the variable component of the measured end-to-end delay. We measure queueing delay as simply the difference between an end-to-end delay measurement and the current estimate of base delay.

4.2.  Managing the Congestion Window

4.2.1.  Window Increase: Probing For More Bandwidth

A LEDBAT sender increases its congestion window most when the queuing delay estimate is zero. To be friendly to competing TCP flows, we set this highest rate of window growth to be the same as TCP's. In other words, the LEDBAT window grows by at most twice per round-trip time. Since queuing delay estimate is always non-negative, this growth rate ensures that a LEDBAT flow never ramps up faster than a competing TCP flow over the same path.

4.2.2.  Window Decrease: Responding To Congestion

When the sender's queuing delay estimate is lower than the target, the sending rate should be increased. When the sender's queueing delay estimate is higher than the target, the sending rate should be reduced. LEDBAT uses a simple linear controller to detemine sending rate as a function of the delay estimate, where the response is proportional to the difference between the current queueing delay estimate and the target. In limited experiments with Bittorrent nodes, this controller seems to work well.

To deal with severe congestion when several packets are dropped in the network, and to provide a fallback against incorrect queuing delay estimates, a LEDBAT sender halves its cwnd when a loss event is detected. As with NewReno, LEDBAT reduces its cwnd by half at most once per RTT. Note that, unlike TCP-like loss-based congestion control, LEDBAT does not induce losses and so it normally does not rely on losses to determine the sending rate. LEDBAT's reaction to loss is thus less important than it is in the case of loss-based congestion control. For LEDBAT, reducing the congestion window on loss is a fallback mechanism in case of severe congestion and in the case of incorrect delay estimates.

5.  Choosing Parameter Values

Through this discussion, we hope to encourage informed experimentation with LEDBAT.

5.1.  Queuing Delay Target

Consider the queuing delay on the bottleneck. This delay is the extra delay induced by congestion control. One of our design goals is to keep this delay low. However, when this delay is zero, the queue is empty and so no data is being transmitted and the link is thus not saturated. Hence, our design goal is to keep the queuing delay low, but non-zero.

How low do we want the queuing delay to be? Because another design goal is to be deployable on networks with only simple FIFO queuing and drop-tail discipline, we can't rely on explicit signaling for the queuing delay. So we're going to estimate it using external measurements. The external measurements will have an error at least on the order of best-case scheduling delays in the OSes. There's thus a good reason to try to make the queuing delay larger than this error. There's no reason that would want us to push the delay much further up. Thus, we will have a delay target that we would want to maintain.

6.  Discussion

6.1.  Framing Considerations

The actual framing and wire format of the protocol(s) using the LEDBAT congestion control mechanism is outside of scope of this document, which only describes the congestion control part.

There is an implication of the need to use one-way delay from the sender to the receiver in the sender. An obvious way to support this is to use a framing that timestamps packets at the sender and conveys the measured one-way delay back to the sender in ack packets. This is the method we'll keep in mind for the purposes of exposition. Other methods are possible and valid.

Another implication is the receipt of frequent ACK packets. The exposition below assumes one ACK per data packet, but any reasonably small number of data packets per ACK will work as long as there is at least one ACK every round-trip time.

The protocols to which this congestion control mechanism is applicable, with possible appropriate extensions, are TCP, SCTP, DCCP, etc. It is not a goal of this document to cover such applications. The mechanism can also be used with proprietary transport protocols, e.g., those built over UDP for P2P applications.

6.2.  Competing With TCP Flows

Consider competition between a LEDBAT connection and a connection governed by loss-based congestion control (on a FIFO bottleneck with drop-tail discipline). Loss-based connection will need to experience loss to back off. Loss will only occur after the connection experiences maximum possible delays. LEDBAT will thus receive congestion indication sooner than the loss-based connection. If LEDBAT can ramp down faster than the loss-based connection ramps up, LEDBAT will yield. LEDBAT ramps down when queuing delay estimate exceeds the target: the more the excess, the faster the ramp-down. When the loss-based connection is standard TCP, LEDBAT will yield at precisely the same rate as TCP is ramping up when the queuing delay is double the target.

LEDBAT is most aggressive when its queuing delay estimate is most wrong and is as low as it can be. Queuing delay estimate is nonnegative, therefore the worst possible case is when somehow the estimate is always returned as zero. In this case, LEDBAT will ramp up as fast as TCP and halve the rate on loss. Thus, in case of worst possible failure of estimates, LEDBAT will behave identically to TCP. This provides an extra safety net.

6.3.  Fairness Among LEDBAT Flows

The design goals of LEDBAT center around the aggregate behavior of LEDBAT flows when they compete with standard TCP. It is also interesting how LEDBAT flows share bottleneck bandwidth when they only compete between themselves.

LEDBAT as described so far lacks a mechanism specifically designed to equalize utilization between these flows. The observed behavior of existing implementations indicates that a rough equalization, in fact, does occur.

The delay measurements used as control inputs by LEDBAT contain some amount of noise and errors. The linear controller converts this input noise into the same amount of output noise. The effect that this has is that the uncorrelated component of the noise between flows serves to randomly shuffle some amount of bandwidth between flows. The amount shuffled during each RTT is proportional to the noise divided by the target delay. The random-walk trajectory of bandwidth utilized by each of the flows over time tends to the fair share. The timescales on which the rates become comparable are proportional to the target delay multiplied by the RTT and divided by the noise.

In complex real-life systems, the main concern is usually the reduction of the amount of noise, which is copious if not eliminated. In some circumstances, however, the measurements might be "too good" -- since the equalization timescale is inversely proportional to noise, perfect measurements would result in lack of convergence.

Under these circumstances, it may be beneficial to introduce some artificial randomness into the inputs (or, equivalently, outputs) of the controller. Note that most systems should not require this and should be primarily concerned with reducing, not adding, noise.

With delay-based congestion control systems, there's a concern about the ability of late comers to measure the base delay correctly. Suppose a LEDBAT flow saturates a bottleneck; another LEDBAT flow starts and proceeds to measure the base delay and the current delay and to estimate the queuing delay. If the bottleneck always contains target delay worth of packets, the second flow would see the bottleneck as empty start building a second target delay worth of queue on top of the existing queue. The concern ("late comers' advantage") is that the initial flow would now back off because it sees the real delay and the late comer would use the whole capacity.

However, once the initial flow yields, the late comer immediately measures the true base delay and the two flows operate from the same (correct) inputs.

Additionally, in practice this concern is further alleviated by the burstiness of network traffic: all that's needed to measure the base delay is one small gap. These gaps can occur for a variety of reasons: the OS may delay the scheduling of the sending process until a time slice ends, the sending computer might be unusually busy for some number of milliseconds or tens of milliseconds, etc. If such a gap occurs while the late comer is starting, base delay is immediately correctly measured. With small number of flows, this appears to be the main mechanism of regulating the late comers' advantage.

7.  IANA Considerations

There are no IANA considerations for this document.

8.  Security Considerations

A network on the path might choose to cause higher delay measurements than the real queuing delay so that LEDBAT backs off even when there's no congestion present. Shaping of traffic into an artificially narrow bottleneck can't be counteracted, but faking timestamp field can and SHOULD. A protocol using the LEDBAT congestion control SHOULD authenticate the timestamp and delay fields, preferably as part of authenticating most of the rest of the packet, with the exception of volatile header fields. The choice of the authentication mechanism that resists man-in-the-middle attacks is outside of scope of this document.

9. Normative References

Appendix A.  Timestamp errors

One-way delay measurement needs to deal with timestamp errors. We'll use the same locally linear clock model and the same terminology as Network Time Protocol (NTP). This model is valid for any differentiable clocks. NTP uses the term "offset" to refer to difference from true time and "skew" to refer to difference of clock rate from the true rate. The clock will thus have a fixed offset from the true time and a skew. We'll consider what we need to do about the offset and the skew separately.

A.1.  Clock offset

First, consider the case of zero skew. The offset of each of the two clocks shows up as a fixed error in one-way delay measurement. The difference of the offsets is the absolute error of the one-way delay estimate. We won't use this estimate directly, however. We'll use the difference between that and a base delay. Because the error (difference of clock offsets) is the same for the current and base delay, it cancels from the queuing delay estimate, which is what we'll use. Clock offset is thus irrelevant to the design.

A.2.  Clock skew

Now consider the skew. For a given clock, skew manifests in a linearly changing error in the time estimate. For a given pair of clocks, the difference in skews is the skew of the one-way delay estimate. Unlike the offset, this no longer cancels in the computation of the queuing delay estimate. On the other hand, while the offset could be huge, with some clocks off by minutes or even hours or more, the skew is typically not too bad. For example, NTP is designed to work with most clocks, yet it gives up when the skew is more than 500 parts per million (PPM). Typical skews of clocks that have never been trained seem to often be around 100-200 PPM. Previously trained clocks could have 10-20 PPM skew due to temperature changes. A 100-PPM skew means accumulating 6 milliseconds of error per minute. The expiration of base delay related to route changes mostly takes care of clock skew. A technique to specifically compute and cancel it is trivially possible and involves tracking base delay skew over a number of minutes and then correcting for it, but usually isn't necessary, unless the target is unusually low, the skew is unusually high, or the base interval is unusually long. It is not further described in this document.

For cases when the base interval is long or the skew is high or the target is low, a technique to correct for skew can be beneficial. The technique described here or a different mechanism MAY be used by implementations. The technique described is still experimental, but it is actually currently used. The pseudocode in the specification below does not include any of the skew correction algorithms.

A.2.1.  Deployed clock skew correction mechanism

Clock skew can be in two directions: either the sender's clock is faster than the receiver's, or vice versa. We refer to the former situation as clock drift "in sender's favor" and to the latter as clock drift "in receiver's favor".

When the clock drift is "in sender's favor", nothing special needs to be done, because the timestamp differences (i.e., the raw delay estimates) will grow smaller with time, and thus the base delay will be continuously updated with the drift.

When the clock drift is "in receiver's favor", the raw delay estimates will drift up with time, suppressing the throughput needlessly. This is the case that can benefit from a special mechanism. Assume symmetrical framing (i.e., same information about delays transmitted in both way). If the sender can detect the receiver reducing its base delay, it can infer that this is due to clock drift "in receiver's favor". This can be compensated for by increasing the sender's base delay by the same amount. Since, in our implementation, the receiver sends back the raw timestamp estimate, the sender can run the same base delay calculation algorithm it runs for itself for the receiver as well; when it reduces the inferred receiver's delay, it increases its own by the same amount.

A.2.2.  Skew correction with faster virtual clock

This is an alternative skew correction algorithm, currently under consideration and not deployed in the wild.

Since having a faster clock on the sender is, relatively speaking, a non-problem, one can use two different virtual clocks on each LEDBAT implementation: use, for example, the default machine clock for situations where the instance is acting as a receiver, and use a virtual clock, easily computed from the default machine clock through a linear transformation, for situations where the instance is acting as a sender. Make the virtual clock, e.g., 500 PPM faster than the machine clock. Since 500 PPM is more than the variability of most clocks (plus or minus 100 PPM), any sender's clock is very likely to be faster than any receiver's clock, thus benefitting from the implicit correction of taking the minimum as the base delay.

Note that this approach is not compatible with the one described in the preceding section.

A.2.3.  Skew correction with estimating drift

This is an alternative skew correction algorithm, currently under consideration and not deployed in the wild.

The history of base delay minima we already keep for each minute provides us with direct means of computing the clock skew difference between the two hosts. Namely, we can fit a linear function to the set of base delay estimates for each minute. The slope of the function is an estimate of the clock skew difference for the given pair of sender and receiver. Once the clock skew difference is estimated, it can be used to correct the clocks so that they advance at nearly the same rate. Namely, the clock needs to be corrected by half of the estimated skew amount, since the other half will be corrected by the other endpoint. Note that the skew differences are then maintained for each connection and the virtual clocks used with each connection can differ, since they do not attempt to estimate the skew with respect to the true time, but instead with respect to the other endpoint.

A.2.3.1.  Byzantine skew correction

This is an alternative skew correction algorithm, currently under consideration and not deployed in the wild.

When it is known that each host maintains long-lived connections to a number of different other hosts, a byzantine scheme can be used to estimate the skew with respect to the true time. Namely, calculate the skew difference for each of the peer hosts as described in the preceding section, then take the median of the skew differences.

This inherent clock drift can then be corrected with a linear transformation before the clock data is used in the algorithm from the preceding section, the currently deployed algorithm, or nearly any other skew correction algorithm.

While this scheme is not universally applicable, it combines well with other schemes, since it is essentially a clock training mechanism. The scheme also acts the fastest, since the state is preserved between connections.

Authors' Addresses