Internet Engineering Task Force (IETF) L. Xu
Request for Comments: 9438 UNL
Obsoletes: 8312 S. Ha
Updates: 5681 Colorado
Category: Standards Track I. Rhee
ISSN: 2070-1721 Bowery
V. Goel
Apple Inc.
L. Eggert, Ed.
NetApp
August 2023
CUBIC for Fast and Long-Distance Networks
Abstract
CUBIC is a standard TCP congestion control algorithm that uses a
cubic function instead of a linear congestion window increase
function to improve scalability and stability over fast and long-
distance networks. CUBIC has been adopted as the default TCP
congestion control algorithm by the Linux, Windows, and Apple stacks.
This document updates the specification of CUBIC to include
algorithmic improvements based on these implementations and recent
academic work. Based on the extensive deployment experience with
CUBIC, this document also moves the specification to the Standards
Track and obsoletes RFC 8312. This document also updates RFC 5681,
to allow for CUBIC's occasionally more aggressive sending behavior.
Status of This Memo
This is an Internet Standards Track document.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Further information on
Internet Standards is available in Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc9438.
Copyright Notice
Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
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Trust Legal Provisions and are provided without warranty as described
in the Revised BSD License.
Table of Contents
1. Introduction
2. Conventions
3. Design Principles of CUBIC
3.1. Principle 1 for the CUBIC Increase Function
3.2. Principle 2 for Reno-Friendliness
3.3. Principle 3 for RTT-Fairness
3.4. Principle 4 for the CUBIC Decrease Factor
4. CUBIC Congestion Control
4.1. Definitions
4.1.1. Constants of Interest
4.1.2. Variables of Interest
4.2. Window Increase Function
4.3. Reno-Friendly Region
4.4. Concave Region
4.5. Convex Region
4.6. Multiplicative Decrease
4.7. Fast Convergence
4.8. Timeout
4.9. Spurious Congestion Events
4.9.1. Spurious Timeouts
4.9.2. Spurious Fast Retransmits
4.10. Slow Start
5. Discussion
5.1. Fairness to Reno
5.2. Using Spare Capacity
5.3. Difficult Environments
5.4. Investigating a Range of Environments
5.5. Protection against Congestion Collapse
5.6. Fairness within the Alternative Congestion Control
Algorithm
5.7. Performance with Misbehaving Nodes and Outside Attackers
5.8. Behavior for Application-Limited Flows
5.9. Responses to Sudden or Transient Events
5.10. Incremental Deployment
6. Security Considerations
7. IANA Considerations
8. References
8.1. Normative References
8.2. Informative References
Appendix A. Evolution of CUBIC since the Original Paper
Appendix B. Proof of the Average CUBIC Window Size
Acknowledgments
Authors' Addresses
1. Introduction
CUBIC has been adopted as the default TCP congestion control
algorithm in the Linux, Windows, and Apple stacks, and has been used
and deployed globally. Extensive, decade-long deployment experience
in vastly different Internet scenarios has convincingly demonstrated
that CUBIC is safe for deployment on the global Internet and delivers
substantial benefits over classical Reno congestion control
[RFC5681]. It is therefore to be regarded as the currently most
widely deployed standard for TCP congestion control. CUBIC can also
be used for other transport protocols such as QUIC [RFC9000] and the
Stream Control Transmission Protocol (SCTP) [RFC9260] as a default
congestion controller.
The design of CUBIC was motivated by the well-documented problem
classical Reno TCP has with low utilization over fast and long-
distance networks [K03] [RFC3649]. This problem arises from a slow
increase of the congestion window (cwnd) following a congestion event
in a network with a large bandwidth-delay product (BDP). [HLRX07]
indicates that this problem is frequently observed even in the range
of congestion window sizes over several hundreds of packets. This
problem is equally applicable to all Reno-style standards and their
variants, including TCP-Reno [RFC5681], TCP-NewReno [RFC6582]
[RFC6675], SCTP [RFC9260], TCP Friendly Rate Control (TFRC)
[RFC5348], and QUIC congestion control [RFC9002], which use the same
linear increase function for window growth. All Reno-style standards
and their variants are collectively referred to as "Reno" in this
document.
CUBIC, originally proposed in [HRX08], is a modification to the
congestion control algorithm of classical Reno to remedy this
problem. Specifically, CUBIC uses a cubic function instead of the
linear window increase function of Reno to improve scalability and
stability under fast and long-distance networks.
This document updates the specification of CUBIC to include
algorithmic improvements based on the Linux, Windows, and Apple
implementations and recent academic work. Based on the extensive
deployment experience with CUBIC, it also moves the specification to
the Standards Track, obsoleting [RFC8312]. This requires an update
to Section 3 of [RFC5681], which limits the aggressiveness of Reno
TCP implementations. Since CUBIC is occasionally more aggressive
than the algorithms defined in [RFC5681], this document updates the
first paragraph of Section 3 of [RFC5681], replacing it with a
normative reference to guideline (1) in Section 3 of [RFC5033], which
allows for CUBIC's behavior as defined in this document.
Specifically, CUBIC may increase the congestion window more
aggressively than Reno during the congestion avoidance phase.
According to [RFC5681], during congestion avoidance, the sender must
not increment cwnd by more than Sender Maximum Segment Size (SMSS)
bytes once per round-trip time (RTT), whereas CUBIC may increase cwnd
much more aggressively. Additionally, CUBIC recommends the HyStart++
algorithm [RFC9406] for slow start, which allows for cwnd increases
of more than SMSS bytes for incoming acknowledgments during slow
start, while this behavior is not allowed as part of the standard
behavior prescribed by [RFC5681].
Binary Increase Congestion Control (BIC-TCP) [XHR04], a predecessor
of CUBIC, was selected as the default TCP congestion control
algorithm by Linux in the year 2005 and had been used for several
years by the Internet community at large.
CUBIC uses a window increase function similar to BIC-TCP and is
designed to be less aggressive and fairer to Reno in bandwidth usage
than BIC-TCP while maintaining the strengths of BIC-TCP such as
stability, window scalability, and RTT-fairness.
[RFC5033] documents the IETF's best current practices for specifying
new congestion control algorithms, specifically those that differ
from the general congestion control principles outlined in [RFC2914].
It describes what type of evaluation is expected by the IETF to
understand the suitability of a new congestion control algorithm and
the process of enabling a specification to be approved for widespread
deployment in the global Internet.
There are areas in which CUBIC differs from the congestion control
algorithms previously published in Standards Track RFCs; those
changes are specified in this document. However, it is not obvious
that these changes go beyond the general congestion control
principles outlined in [RFC2914], so the process documented in
[RFC5033] may not apply.
Also, the wide deployment of CUBIC on the Internet was driven by
direct adoption in most of the popular operating systems and did not
follow the practices documented in [RFC5033]. However, due to the
resulting Internet-scale deployment experience over a long period of
time, the IETF determined that CUBIC could be published as a
Standards Track specification. This decision by the IETF does not
alter the general guidance provided in [RFC2914].
The following sections
1. briefly explain the design principles of CUBIC,
2. provide the exact specification of CUBIC, and
3. discuss the safety features of CUBIC, following the guidelines
specified in [RFC5033].
2. Conventions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
3. Design Principles of CUBIC
CUBIC is designed according to the following design principles:
Principle 1: For better network utilization and stability, CUBIC
uses both the concave and convex profiles of a cubic function to
increase the congestion window size, instead of using just a
convex function.
Principle 2: To be Reno-friendly, CUBIC is designed to behave like
Reno in networks with short RTTs and small bandwidth where Reno
performs well.
Principle 3: For RTT-fairness, CUBIC is designed to achieve linear
bandwidth sharing among flows with different RTTs.
Principle 4: CUBIC appropriately sets its multiplicative window
decrease factor in order to achieve a balance between scalability
and convergence speed.
3.1. Principle 1 for the CUBIC Increase Function
For better network utilization and stability, CUBIC [HRX08] uses a
cubic window increase function in terms of the elapsed time from the
last congestion event. While most congestion control algorithms that
provide alternatives to Reno increase the congestion window using
convex functions, CUBIC uses both the concave and convex profiles of
a cubic function for window growth.
After a window reduction in response to a congestion event detected
by duplicate acknowledgments (ACKs), Explicit Congestion
Notification-Echo (ECN-Echo (ECE)) ACKs [RFC3168], RACK-TLP for TCP
[RFC8985], or QUIC loss detection [RFC9002], CUBIC remembers the
congestion window size at which it received the congestion event and
performs a multiplicative decrease of the congestion window. When
CUBIC enters into congestion avoidance, it starts to increase the
congestion window using the concave profile of the cubic function.
The cubic function is set to have its plateau at the remembered
congestion window size, so that the concave window increase continues
until then. After that, the cubic function turns into a convex
profile and the convex window increase begins.
This style of window adjustment (concave and then convex) improves
algorithm stability while maintaining high network utilization
[CEHRX09]. This is because the window size remains almost constant,
forming a plateau around the remembered congestion window size of the
last congestion event, where network utilization is deemed highest.
Under steady state, most window size samples of CUBIC are close to
that remembered congestion window size, thus promoting high network
utilization and stability.
Note that congestion control algorithms that only use convex
functions to increase the congestion window size have their maximum
increments around the remembered congestion window size of the last
congestion event and thus introduce many packet bursts around the
saturation point of the network, likely causing frequent global loss
synchronizations.
3.2. Principle 2 for Reno-Friendliness
CUBIC promotes per-flow fairness to Reno. Note that Reno performs
well over paths with small BDPs and only experiences problems when
attempting to increase bandwidth utilization on paths with large
BDPs.
A congestion control algorithm designed to be friendly to Reno on a
per-flow basis must increase its congestion window less aggressively
in small-BDP networks than in large-BDP networks.
The aggressiveness of CUBIC mainly depends on the maximum window size
before a window reduction, which is smaller in small-BDP networks
than in large-BDP networks. Thus, CUBIC increases its congestion
window less aggressively in small-BDP networks than in large-BDP
networks.
Furthermore, in cases when the cubic function of CUBIC would increase
the congestion window less aggressively than Reno, CUBIC simply
follows the window size of Reno to ensure that CUBIC achieves at
least the same throughput as Reno in small-BDP networks. The region
where CUBIC behaves like Reno is called the "Reno-friendly region".
3.3. Principle 3 for RTT-Fairness
Two CUBIC flows with different RTTs have a throughput ratio that is
linearly proportional to the inverse of their RTT ratio, where the
throughput of a flow is approximately the size of its congestion
window divided by its RTT.
Specifically, CUBIC maintains a window increase rate that is
independent of RTTs outside the Reno-friendly region, and thus flows
with different RTTs have similar congestion window sizes under steady
state when they operate outside the Reno-friendly region.
This notion of a linear throughput ratio is similar to that of Reno
under an asynchronous loss model, where flows with different RTTs
have the same packet loss rate but experience loss events at
different times. However, under a synchronous loss model, where
flows with different RTTs experience loss events at the same time but
have different packet loss rates, the throughput ratio of Reno flows
with different RTTs is quadratically proportional to the inverse of
their RTT ratio [XHR04].
CUBIC always ensures a linear throughput ratio that is independent of
the loss environment. This is an improvement over Reno. While there
is no consensus on the optimal throughput ratio for different RTT
flows, over wired Internet paths, use of a linear throughput ratio
seems more reasonable than equal throughputs (i.e., the same
throughput for flows with different RTTs) or a higher-order
throughput ratio (e.g., a quadratic throughput ratio of Reno in
synchronous loss environments).
3.4. Principle 4 for the CUBIC Decrease Factor
To achieve a balance between scalability and convergence speed, CUBIC
sets the multiplicative window decrease factor to 0.7, whereas Reno
uses 0.5.
While this improves the scalability of CUBIC, a side effect of this
decision is slower convergence, especially under low statistical
multiplexing. This design choice is following the observation that
HighSpeed TCP (HSTCP) [RFC3649] and other approaches (e.g., [GV02])
made: the current Internet becomes more asynchronous with less
frequent loss synchronizations under high statistical multiplexing.
In such environments, even strict Multiplicative-Increase
Multiplicative-Decrease (MIMD) can converge. CUBIC flows with the
same RTT always converge to the same throughput independently of
statistical multiplexing, thus achieving intra-algorithm fairness.
In environments with sufficient statistical multiplexing, the
convergence speed of CUBIC is reasonable.
4. CUBIC Congestion Control
This section discusses how the congestion window is updated during
the different stages of the CUBIC congestion controller.
4.1. Definitions
The unit of all window sizes in this document is segments of the
SMSS, and the unit of all times is seconds. Implementations can use
bytes to express window sizes, which would require factoring in the
SMSS wherever necessary and replacing _segments_acked_ (Figure 4)
with the number of acknowledged bytes.
4.1.1. Constants of Interest
* β__cubic_: CUBIC multiplicative decrease factor as described in
Section 4.6.
* α__cubic_: CUBIC additive increase factor used in the Reno-
friendly region as described in Section 4.3.
* _C_: Constant that determines the aggressiveness of CUBIC in
competing with other congestion control algorithms in high-BDP
networks. Please see Section 5 for more explanation on how it is
set. The unit for _C_ is
segment
âââââââ
3
second
4.1.2. Variables of Interest
This section defines the variables required to implement CUBIC:
* _RTT_: Smoothed round-trip time in seconds, calculated as
described in [RFC6298].
* _cwnd_: Current congestion window in segments.
* _ssthresh_: Current slow start threshold in segments.
* _cwnd_prior_: Size of _cwnd_ in segments at the time of setting
_ssthresh_ most recently, either upon exiting the first slow start
or just before _cwnd_ was reduced in the last congestion event.
* _W_max_: Size of _cwnd_ in segments just before _cwnd_ was reduced
in the last congestion event when fast convergence is disabled
(same as _cwnd_prior_ on a congestion event). However, if fast
convergence is enabled, _W_max_ may be further reduced based on
the current saturation point.
* _K_: The time period in seconds it takes to increase the
congestion window size at the beginning of the current congestion
avoidance stage to _W_max_.
* _t_current_: Current time of the system in seconds.
* _t_epoch_: The time in seconds at which the current congestion
avoidance stage started.
* _cwnd_epoch_: The _cwnd_ at the beginning of the current
congestion avoidance stage, i.e., at time _t_epoch_.
* W_cubic(_t_): The congestion window in segments at time _t_ in
seconds based on the cubic increase function, as described in
Section 4.2.
* _target_: Target value of the congestion window in segments after
the next RTT -- that is, W_cubic(_t_ + _RTT_), as described in
Section 4.2.
* _W_est_: An estimate for the congestion window in segments in the
Reno-friendly region -- that is, an estimate for the congestion
window of Reno.
* _segments_acked_: Number of SMSS-sized segments acked when a "new
ACK" is received, i.e., an ACK that cumulatively acknowledges the
delivery of previously unacknowledged data. This number will be a
decimal value when a new ACK acknowledges an amount of data that
is not SMSS-sized. Specifically, it can be less than 1 when a new
ACK acknowledges a segment smaller than the SMSS.
4.2. Window Increase Function
CUBIC maintains the ACK clocking of Reno by increasing the congestion
window only at the reception of a new ACK. It does not make any
changes to the TCP Fast Recovery and Fast Retransmit algorithms
[RFC6582] [RFC6675].
During congestion avoidance, after a congestion event is detected as
described in Section 3.1, CUBIC uses a window increase function
different from Reno.
CUBIC uses the following window increase function:
3
W (t) = C * (t - K) + W
cubic max
Figure 1
where _t_ is the elapsed time in seconds from the beginning of the
current congestion avoidance stage -- that is,
t = t - t
current epoch
and where _t_epoch_ is the time at which the current congestion
avoidance stage starts. _K_ is the time period that the above
function takes to increase the congestion window size at the
beginning of the current congestion avoidance stage to _W_max_ if
there are no further congestion events. _K_ is calculated using the
following equation:
ââââââââââââââââââ
3 âW - cwnd
â² â max epoch
K = â² âââââââââââââââââ
â²â C
Figure 2
where _cwnd_epoch_ is the congestion window at the beginning of the
current congestion avoidance stage.
Upon receiving a new ACK during congestion avoidance, CUBIC computes
the _target_ congestion window size after the next _RTT_ using
Figure 1 as follows, where _RTT_ is the smoothed round-trip time.
The lower and upper bounds below ensure that CUBIC's congestion
window increase rate is non-decreasing and is less than the increase
rate of slow start [SXEZ19].
â§
âªcwnd if W (t + RTT) < cwnd
⪠cubic
â¨1.5 * cwnd if W (t + RTT) > 1.5 * cwnd
target = ⪠cubic
âªW (t + RTT) otherwise
â© cubic
The elapsed time _t_ in Figure 1 MUST NOT include periods during
which _cwnd_ has not been updated due to application-limited behavior
(see Section 5.8).
Depending on the value of the current congestion window size _cwnd_,
CUBIC runs in three different regions:
1. The Reno-friendly region, which ensures that CUBIC achieves at
least the same throughput as Reno.
2. The concave region, if CUBIC is not in the Reno-friendly region
and _cwnd_ is less than _W_max_.
3. The convex region, if CUBIC is not in the Reno-friendly region
and _cwnd_ is greater than _W_max_.
To summarize, CUBIC computes both W_cubic(_t_) and _W_est_ (see
Section 4.3) on receiving a new ACK in congestion avoidance and
chooses the larger of the two values.
The next sections describe the exact actions taken by CUBIC in each
region.
4.3. Reno-Friendly Region
Reno performs well in certain types of networks -- for example, under
short RTTs and small bandwidths (or small BDPs). In these networks,
CUBIC remains in the Reno-friendly region to achieve at least the
same throughput as Reno.
The Reno-friendly region is designed according to the analysis
discussed in [FHP00], which studies the performance of an AIMD
algorithm with an additive factor of α (segments per _RTT_) and a
multiplicative factor of β, denoted by AIMD(α, β). _p_ is the packet
loss rate. Specifically, the average congestion window size of
AIMD(α, β) can be calculated using Figure 3.
âââââââââââââââââ
â α * (1 + β)
AVG_AIMD(α, β) = â² ââââââââââââââââ
â²â2 * (1 - β) * p
Figure 3
By the same analysis, to achieve an average window size similar to
Reno that uses AIMD(1, 0.5), α must be equal to
1 - β
3 * âââââ
1 + β
Thus, CUBIC uses Figure 4 to estimate the window size _W_est_ in the
Reno-friendly region with
1 - β
cubic
α = 3 * ââââââââââ
cubic 1 + β
cubic
which achieves approximately the same average window size as Reno in
many cases. The model used to calculate α__cubic_ is not absolutely
precise, but analysis and simulation as discussed in
[AIMD-friendliness], as well as over a decade of experience with
CUBIC in the public Internet, show that this approach produces
acceptable levels of rate fairness between CUBIC and Reno flows.
Also, no significant drawbacks of the model have been reported.
However, continued detailed analysis of this approach would be
beneficial. When receiving a new ACK in congestion avoidance (where
_cwnd_ could be greater than or less than _W_max_), CUBIC checks
whether W_cubic(_t_) is less than _W_est_. If so, CUBIC is in the
Reno-friendly region and _cwnd_ SHOULD be set to _W_est_ at each
reception of a new ACK.
_W_est_ is set equal to _cwnd_epoch_ at the start of the congestion
avoidance stage. After that, on every new ACK, _W_est_ is updated
using Figure 4. Note that this equation uses _segments_acked_ and
_cwnd_ is measured in segments. An implementation that measures
_cwnd_ in bytes should adjust the equation accordingly using the
number of acknowledged bytes and the SMSS. Also note that this
equation works for connections with enabled or disabled delayed ACKs
[RFC5681], as _segments_acked_ will be different based on the
segments actually acknowledged by a new ACK.
segments_acked
W = W + α * ââââââââââââââ
est est cubic cwnd
Figure 4
Once _W_est_ has grown to reach the _cwnd_ at the time of most
recently setting _ssthresh_ -- that is, _W_est_ >= _cwnd_prior_ --
the sender SHOULD set α__cubic_ to 1 to ensure that it can achieve
the same congestion window increment rate as Reno, which uses AIMD(1,
0.5).
The next two sections assume that CUBIC is not in the Reno-friendly
region and uses the window increase function described in
Section 4.2. Although _cwnd_ is incremented in the same way for both
concave and convex regions, they are discussed separately to analyze
and understand the difference between the two regions.
4.4. Concave Region
When receiving a new ACK in congestion avoidance, if CUBIC is not in
the Reno-friendly region and _cwnd_ is less than _W_max_, then CUBIC
is in the concave region. In this region, _cwnd_ MUST be incremented
by
target - cwnd
âââââââââââââ
cwnd
for each received new ACK, where _target_ is calculated as described
in Section 4.2.
4.5. Convex Region
When receiving a new ACK in congestion avoidance, if CUBIC is not in
the Reno-friendly region and _cwnd_ is larger than or equal to
_W_max_, then CUBIC is in the convex region.
The convex region indicates that the network conditions might have
changed since the last congestion event, possibly implying more
available bandwidth after some flow departures. Since the Internet
is highly asynchronous, some amount of perturbation is always
possible without causing a major change in available bandwidth.
Unless the cwnd is overridden by the AIMD window increase, CUBIC will
behave cautiously when operating in this region. The convex profile
aims to increase the window very slowly at the beginning when _cwnd_
is around _W_max_ and then gradually increases its rate of increase.
This region is also called the "maximum probing phase", since CUBIC
is searching for a new _W_max_. In this region, _cwnd_ MUST be
incremented by
target - cwnd
âââââââââââââ
cwnd
for each received new ACK, where _target_ is calculated as described
in Section 4.2.
4.6. Multiplicative Decrease
When a congestion event is detected by the mechanisms described in
Section 3.1, CUBIC updates _W_max_ and reduces _cwnd_ and _ssthresh_
immediately, as described below. In the case of packet loss, the
sender MUST reduce _cwnd_ and _ssthresh_ immediately upon entering
loss recovery, similar to [RFC5681] (and [RFC6675]). Note that other
mechanisms, such as Proportional Rate Reduction [RFC6937], can be
used to reduce the sending rate during loss recovery more gradually.
The parameter β__cubic_ SHOULD be set to 0.7, which is different from
the multiplicative decrease factor used in [RFC5681] (and [RFC6675])
during fast recovery.
In Figure 5, _flight_size_ is the amount of outstanding
(unacknowledged) data in the network, as defined in [RFC5681]. Note
that a rate-limited application with idle periods or periods when
unable to send at the full rate permitted by _cwnd_ could easily
encounter notable variations in the volume of data sent from one RTT
to another, resulting in _flight_size_ that is significantly less
than _cwnd_ when there is a congestion event. The congestion
response would therefore decrease _cwnd_ to a much lower value than
necessary. To avoid such suboptimal performance, the mechanisms
described in [RFC7661] can be used. [RFC7661] describes how to
manage and use _cwnd_ and _ssthresh_ during a rate-limited interval,
and how to update _cwnd_ and _ssthresh_ after congestion has been
detected. The mechanisms defined in [RFC7661] are safe to use even
when _cwnd_ is greater than the receive window, because they validate
_cwnd_ based on the amount of data acknowledged by the network in an
RTT, which implicitly accounts for the allowed receive window.
Some implementations of CUBIC currently use _cwnd_ instead of
_flight_size_ when calculating a new _ssthresh_. Implementations
that use _cwnd_ MUST use other measures to prevent _cwnd_ from
growing when the volume of bytes in flight is smaller than
_cwnd_. This also effectively prevents _cwnd_ from growing beyond
the receive window. Such measures are important for preventing a
CUBIC sender from using an arbitrarily high cwnd _value_ when
calculating new values for _ssthresh_ and _cwnd_ when congestion is
detected. This might not be as robust as the mechanisms described in
[RFC7661].
A QUIC sender that uses a _cwnd_ _value_ to calculate new values for
_cwnd_ and _ssthresh_ after detecting a congestion event is REQUIRED
to apply similar mechanisms [RFC9002].
ssthresh = flight_size * β new ssthresh
cubic
cwnd = cwnd save cwnd
prior
â§max(ssthresh, 2) reduction on loss, cwnd >= 2 SMSS
cwnd = â¨max(ssthresh, 1) reduction on ECE, cwnd >= 1 SMSS
â©
ssthresh = max(ssthresh, 2) ssthresh >= 2 SMSS
Figure 5
A side effect of setting β__cubic_ to a value bigger than 0.5 is that
packet loss can happen for more than one RTT in certain cases, but it
can work efficiently in other cases -- for example, when HyStart++
[RFC9406] is used along with CUBIC or when the sending rate is
limited by the application. While a more adaptive setting of
β__cubic_ could help limit packet loss to a single round, it would
require detailed analyses and large-scale evaluations to validate
such algorithms.
Note that CUBIC MUST continue to reduce _cwnd_ in response to
congestion events detected by ECN-Echo ACKs until it reaches a value
of 1 SMSS. If congestion events indicated by ECN-Echo ACKs persist,
a sender with a _cwnd_ of 1 SMSS MUST reduce its sending rate even
further. This can be achieved by using a retransmission timer with
exponential backoff, as described in [RFC3168].
4.7. Fast Convergence
To improve convergence speed, CUBIC uses a heuristic. When a new
flow joins the network, existing flows need to give up some of their
bandwidth to allow the new flow some room for growth if the existing
flows have been using all the network bandwidth. To speed up this
bandwidth release by existing flows, the following fast convergence
mechanism SHOULD be implemented.
With fast convergence, when a congestion event occurs, _W_max_ is
updated as follows, before the window reduction described in
Section 4.6.
⧠1 + β
⪠cubic
âªcwnd * ââââââââââ if cwnd < W and fast convergence enabled,
W = ⨠2 max
max ⪠further reduce W
⪠max
â©cwnd otherwise, remember cwnd before reduction
During a congestion event, if the current _cwnd_ is less than
_W_max_, this indicates that the saturation point experienced by this
flow is getting reduced because of a change in available bandwidth.
This flow can then release more bandwidth by reducing _W_max_
further. This action effectively lengthens the time for this flow to
increase its congestion window, because the reduced _W_max_ forces
the flow to plateau earlier. This allows more time for the new flow
to catch up to its congestion window size.
Fast convergence is designed for network environments with multiple
CUBIC flows. In network environments with only a single CUBIC flow
and without any other traffic, fast convergence SHOULD be disabled.
4.8. Timeout
In the case of a timeout, CUBIC follows Reno to reduce _cwnd_
[RFC5681] but sets _ssthresh_ using β__cubic_ (same as in
Section 4.6) in a way that is different from Reno TCP [RFC5681].
During the first congestion avoidance stage after a timeout, CUBIC
increases its congestion window size using Figure 1, where _t_ is the
elapsed time since the beginning of the current congestion avoidance
stage, _K_ is set to 0, and _W_max_ is set to the congestion window
size at the beginning of the current congestion avoidance stage. In
addition, for the Reno-friendly region, _W_est_ SHOULD be set to the
congestion window size at the beginning of the current congestion
avoidance stage.
4.9. Spurious Congestion Events
In cases where CUBIC reduces its congestion window in response to
having detected packet loss via duplicate ACKs or timeouts, it is
possible that the missing ACK could arrive after the congestion
window reduction and a corresponding packet retransmission. For
example, packet reordering could trigger this behavior. A high
degree of packet reordering could cause multiple congestion window
reduction events, where spurious losses are incorrectly interpreted
as congestion signals, thus degrading CUBIC's performance
significantly.
For TCP, there are two types of spurious events: spurious timeouts
and spurious fast retransmits. In the case of QUIC, there are no
spurious timeouts, as the loss is only detected after receiving an
ACK.
4.9.1. Spurious Timeouts
An implementation MAY detect spurious timeouts based on the
mechanisms described in Forward RTO-Recovery [RFC5682]. Experimental
alternatives include the Eifel detection algorithm [RFC3522]. When a
spurious timeout is detected, a TCP implementation MAY follow the
response algorithm described in [RFC4015] to restore the congestion
control state and adapt the retransmission timer to avoid further
spurious timeouts.
4.9.2. Spurious Fast Retransmits
Upon receiving an ACK, a TCP implementation MAY detect spurious fast
retransmits either using TCP Timestamps or via D-SACK [RFC2883]. As
noted above, experimental alternatives include the Eifel detection
algorithm [RFC3522], which uses TCP Timestamps; and DSACK-based
detection [RFC3708], which uses DSACK information. A QUIC
implementation can easily determine a spurious fast retransmit if a
QUIC packet is acknowledged after it has been marked as lost and the
original data has been retransmitted with a new QUIC packet.
This section specifies a simple response algorithm when a spurious
fast retransmit is detected by acknowledgments. Implementations
would need to carefully evaluate the impact of using this algorithm
in different environments that may experience a sudden change in
available capacity (e.g., due to variable radio capacity, a routing
change, or a mobility event).
When packet loss is detected via acknowledgments, a CUBIC
implementation MAY save the current value of the following variables
before the congestion window is reduced.
undo_cwnd = cwnd
undo_cwnd = cwnd
prior prior
undo_ssthresh = ssthresh
undo_W = W
max max
undo_K = K
undo_t = t
epoch epoch
undo_W = W
est est
Once the previously declared packet loss is confirmed to be spurious,
CUBIC MAY restore the original values of the above-mentioned
variables as follows if the current _cwnd_ is lower than
_cwnd_prior_. Restoring the original values ensures that CUBIC's
performance is similar to what it would be without spurious losses.
cwnd = undo_cwnd â«
cwnd = undo_cwnd â®
prior prior â®
ssthresh = undo_ssthresh â®
W = undo_W â®
max max â¬if cwnd < cwnd
K = undo_K â® prior
t = undo_t â®
epoch epoch â®
W = undo_W â®
est est â
In rare cases, when the detection happens long after a spurious fast
retransmit event and the current _cwnd_ is already higher than
_cwnd_prior_, CUBIC SHOULD continue to use the current and the most
recent values of these variables.
4.10. Slow Start
When _cwnd_ is no more than _ssthresh_, CUBIC MUST employ a slow
start algorithm. In general, CUBIC SHOULD use the HyStart++ slow
start algorithm [RFC9406] or MAY use the Reno TCP slow start
algorithm [RFC5681] in the rare cases when HyStart++ is not suitable.
Experimental alternatives include hybrid slow start [HR11], a
predecessor to HyStart++ that some CUBIC implementations have used as
the default for the last decade, and limited slow start [RFC3742].
Whichever startup algorithm is used, work might be needed to ensure
that the end of slow start and the first multiplicative decrease of
congestion avoidance work well together.
When CUBIC uses HyStart++ [RFC9406], it may exit the first slow start
without incurring any packet loss and thus _W_max_ is undefined. In
this special case, CUBIC sets _cwnd_prior = cwnd_ and switches to
congestion avoidance. It then increases its congestion window size
using Figure 1, where _t_ is the elapsed time since the beginning of
the current congestion avoidance stage, _K_ is set to 0, and _W_max_
is set to the congestion window size at the beginning of the current
congestion avoidance stage.
5. Discussion
This section further discusses the safety features of CUBIC,
following the guidelines specified in [RFC5033].
With a deterministic loss model where the number of packets between
two successive packet losses is always _1/p_, CUBIC always operates
with the concave window profile, which greatly simplifies the
performance analysis of CUBIC. The average window size of CUBIC (see
Appendix B) can be obtained via the following function:
ââââââââââââââââââ 4 ââââââ
âC * (3 + β ) â² â 3
4 â cubic â²âRTT
AVG_W = â² âââââââââââââââââ * ââââââââ
cubic â² â4 * (1 - β ) 4 ââââ
â²â cubic â² â 3
â²âp
Figure 6
With β__cubic_ set to 0.7, the above formula reduces to
4 ââââââ
âââââââââ â² â 3
4 âC * 3.7 â²âRTT
AVG_W = â² ââââââââ * ââââââââ
cubic â²â 1.2 4 ââââ
â² â 3
â²âp
Figure 7
The following subsection will determine the value of _C_ using
Figure 7.
5.1. Fairness to Reno
In environments where Reno is able to make reasonable use of the
available bandwidth, CUBIC does not significantly change this state.
Reno performs well in the following two types of networks:
1. networks with a small bandwidth-delay product (BDP)
2. networks with short RTTs, but not necessarily a small BDP
CUBIC is designed to behave very similarly to Reno in the above two
types of networks. The following two tables show the average window
sizes of Reno TCP, HSTCP, and CUBIC TCP. The average window sizes of
Reno TCP and HSTCP are from [RFC3649]. The average window size of
CUBIC is calculated using Figure 7 and the CUBIC Reno-friendly region
for three different values of _C_.
+=============+=======+========+================+=========+========+
| Loss Rate P | Reno | HSTCP | CUBIC (C=0.04) | CUBIC | CUBIC |
| | | | | (C=0.4) | (C=4) |
+=============+=======+========+================+=========+========+
| 1.0e-02 | 12 | 12 | 12 | 12 | 12 |
+-------------+-------+--------+----------------+---------+--------+
| 1.0e-03 | 38 | 38 | 38 | 38 | 59 |
+-------------+-------+--------+----------------+---------+--------+
| 1.0e-04 | 120 | 263 | 120 | 187 | 333 |
+-------------+-------+--------+----------------+---------+--------+
| 1.0e-05 | 379 | 1795 | 593 | 1054 | 1874 |
+-------------+-------+--------+----------------+---------+--------+
| 1.0e-06 | 1200 | 12280 | 3332 | 5926 | 10538 |
+-------------+-------+--------+----------------+---------+--------+
| 1.0e-07 | 3795 | 83981 | 18740 | 33325 | 59261 |
+-------------+-------+--------+----------------+---------+--------+
| 1.0e-08 | 12000 | 574356 | 105383 | 187400 | 333250 |
+-------------+-------+--------+----------------+---------+--------+
Table 1: Reno TCP, HSTCP, and CUBIC with RTT = 0.1 Seconds
Table 1 describes the response function of Reno TCP, HSTCP, and CUBIC
in networks with _RTT_ = 0.1 seconds. The average window size is in
SMSS-sized segments.
+=============+=======+========+================+=========+=======+
| Loss Rate P | Reno | HSTCP | CUBIC (C=0.04) | CUBIC | CUBIC |
| | | | | (C=0.4) | (C=4) |
+=============+=======+========+================+=========+=======+
| 1.0e-02 | 12 | 12 | 12 | 12 | 12 |
+-------------+-------+--------+----------------+---------+-------+
| 1.0e-03 | 38 | 38 | 38 | 38 | 38 |
+-------------+-------+--------+----------------+---------+-------+
| 1.0e-04 | 120 | 263 | 120 | 120 | 120 |
+-------------+-------+--------+----------------+---------+-------+
| 1.0e-05 | 379 | 1795 | 379 | 379 | 379 |
+-------------+-------+--------+----------------+---------+-------+
| 1.0e-06 | 1200 | 12280 | 1200 | 1200 | 1874 |
+-------------+-------+--------+----------------+---------+-------+
| 1.0e-07 | 3795 | 83981 | 3795 | 5926 | 10538 |
+-------------+-------+--------+----------------+---------+-------+
| 1.0e-08 | 12000 | 574356 | 18740 | 33325 | 59261 |
+-------------+-------+--------+----------------+---------+-------+
Table 2: Reno TCP, HSTCP, and CUBIC with RTT = 0.01 Seconds
Table 2 describes the response function of Reno TCP, HSTCP, and CUBIC
in networks with _RTT_ = 0.01 seconds. The average window size is in
SMSS-sized segments.
Both tables show that CUBIC with any of these three _C_ values is
more friendly to Reno TCP than HSTCP, especially in networks with a
short _RTT_ where Reno TCP performs reasonably well. For example, in
a network with _RTT_ = 0.01 seconds and p=10^-6, Reno TCP has an
average window of 1200 packets. If the packet size is 1500 bytes,
then Reno TCP can achieve an average rate of 1.44 Gbps. In this
case, CUBIC with _C_=0.04 or _C_=0.4 achieves exactly the same rate
as Reno TCP, whereas HSTCP is about ten times more aggressive than
Reno TCP.
_C_ determines the aggressiveness of CUBIC in competing with other
congestion control algorithms for bandwidth. CUBIC is more friendly
to Reno TCP if the value of _C_ is lower. However, it is NOT
RECOMMENDED to set _C_ to a very low value like 0.04, since CUBIC
with a low _C_ cannot efficiently use the bandwidth in fast and long-
distance networks. Based on these observations and extensive
deployment experience, _C_=0.4 seems to provide a good balance
between Reno-friendliness and aggressiveness of window increase.
Therefore, _C_ SHOULD be set to 0.4. With _C_ set to 0.4, Figure 7
is reduced to
4 ââââââ
â² â 3
â²âRTT
AVG_W = 1.054 * ââââââââ
cubic 4 ââââ
â² â 3
â²âp
Figure 8
Figure 8 is then used in the next subsection to show the scalability
of CUBIC.
5.2. Using Spare Capacity
CUBIC uses a more aggressive window increase function than Reno for
fast and long-distance networks.
Table 3 shows that to achieve the 10 Gbps rate, Reno TCP requires a
packet loss rate of 2.0e-10, while CUBIC TCP requires a packet loss
rate of 2.9e-8.
+===================+===========+=========+=========+=========+
| Throughput (Mbps) | Average W | Reno P | HSTCP P | CUBIC P |
+===================+===========+=========+=========+=========+
| 1 | 8.3 | 2.0e-2 | 2.0e-2 | 2.0e-2 |
+-------------------+-----------+---------+---------+---------+
| 10 | 83.3 | 2.0e-4 | 3.9e-4 | 2.9e-4 |
+-------------------+-----------+---------+---------+---------+
| 100 | 833.3 | 2.0e-6 | 2.5e-5 | 1.4e-5 |
+-------------------+-----------+---------+---------+---------+
| 1000 | 8333.3 | 2.0e-8 | 1.5e-6 | 6.3e-7 |
+-------------------+-----------+---------+---------+---------+
| 10000 | 83333.3 | 2.0e-10 | 1.0e-7 | 2.9e-8 |
+-------------------+-----------+---------+---------+---------+
Table 3: Required Packet Loss Rate for Reno TCP, HSTCP, and
CUBIC to Achieve a Certain Throughput
Table 3 describes the required packet loss rate for Reno TCP, HSTCP,
and CUBIC to achieve a certain throughput, with 1500-byte packets and
an _RTT_ of 0.1 seconds.
The test results provided in [HLRX07] indicate that, in typical cases
with a degree of background traffic, CUBIC uses the spare bandwidth
left unused by existing Reno TCP flows in the same bottleneck link
without taking away much bandwidth from the existing flows.
5.3. Difficult Environments
CUBIC is designed to remedy the poor performance of Reno in fast and
long-distance networks.
5.4. Investigating a Range of Environments
CUBIC has been extensively studied using simulations, testbed
emulations, Internet experiments, and Internet measurements, covering
a wide range of network environments [HLRX07] [H16] [CEHRX09] [HR11]
[BSCLU13] [LBEWK16]. They have convincingly demonstrated that CUBIC
delivers substantial benefits over classical Reno congestion control
[RFC5681].
Same as Reno, CUBIC is a loss-based congestion control algorithm.
Because CUBIC is designed to be more aggressive (due to a faster
window increase function and bigger multiplicative decrease factor)
than Reno in fast and long-distance networks, it can fill large drop-
tail buffers more quickly than Reno and increases the risk of a
standing queue [RFC8511]. In this case, proper queue sizing and
management [RFC7567] could be used to mitigate the risk to some
extent and reduce the packet queuing delay. Also, in large-BDP
networks after a congestion event, CUBIC, due to its cubic window
increase function, recovers quickly to the highest link utilization
point. This means that link utilization is less sensitive to an
active queue management (AQM) target that is lower than the amplitude
of the whole sawtooth.
Similar to Reno, the performance of CUBIC as a loss-based congestion
control algorithm suffers in networks where packet loss is not a good
indication of bandwidth utilization, such as wireless or mobile
networks [LIU16].
5.5. Protection against Congestion Collapse
With regard to the potential of causing congestion collapse, CUBIC
behaves like Reno, since CUBIC modifies only the window adjustment
algorithm of Reno. Thus, it does not modify the ACK clocking and
timeout behaviors of Reno.
CUBIC also satisfies the "full backoff" requirement as described in
[RFC5033]. After reducing the sending rate to one packet per RTT in
response to congestion events detected by ECN-Echo ACKs, CUBIC then
exponentially increases the transmission timer for each packet
retransmission while congestion persists.
5.6. Fairness within the Alternative Congestion Control Algorithm
CUBIC ensures convergence of competing CUBIC flows with the same RTT
in the same bottleneck links to an equal throughput. When competing
flows have different RTT values, their throughput ratio is linearly
proportional to the inverse of their RTT ratios. This is true and is
independent of the level of statistical multiplexing on the link.
The convergence time depends on the network environments (e.g.,
bandwidth, RTT) and the level of statistical multiplexing, as
mentioned in Section 3.4.
5.7. Performance with Misbehaving Nodes and Outside Attackers
CUBIC does not introduce new entities or signals, so its
vulnerability to misbehaving nodes or attackers is unchanged from
Reno.
5.8. Behavior for Application-Limited Flows
A flow is application limited if it is currently sending less than
what is allowed by the congestion window. This can happen if the
flow is limited by either the sender application or the receiver
application (via the receiver's advertised window) and thus sends
less data than what is allowed by the sender's congestion window.
CUBIC does not increase its congestion window if a flow is
application limited. Per Section 4.2, it is required that _t_ in
Figure 1 not include application-limited periods, such as idle
periods; otherwise, W_cubic(_t_) might be very high after restarting
from these periods.
5.9. Responses to Sudden or Transient Events
If there is a sudden increase in capacity, e.g., due to variable
radio capacity, a routing change, or a mobility event, CUBIC is
designed to utilize the newly available capacity more quickly than
Reno.
On the other hand, if there is a sudden decrease in capacity, CUBIC
reduces more slowly than Reno. This remains true regardless of
whether CUBIC is in Reno-friendly mode and regardless of whether fast
convergence is enabled.
5.10. Incremental Deployment
CUBIC requires only changes to congestion control at the sender, and
it does not require any changes at receivers. That is, a CUBIC
sender works correctly with Reno receivers. In addition, CUBIC does
not require any changes to routers and does not require any
assistance from routers.
6. Security Considerations
CUBIC makes no changes to the underlying security of a transport
protocol and inherits the general security concerns described in
[RFC5681]. Specifically, changing the window computation on the
sender may allow an attacker, through dropping or injecting ACKs (as
described in [RFC5681]), to either force the CUBIC implementation to
reduce its bandwidth or convince it that there is no congestion when
congestion does exist, and to use the CUBIC implementation as an
attack vector against other hosts. These attacks are not new to
CUBIC and are inherently part of any transport protocol like TCP.
7. IANA Considerations
This document does not require any IANA actions.
8. References
8.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC2883] Floyd, S., Mahdavi, J., Mathis, M., and M. Podolsky, "An
Extension to the Selective Acknowledgement (SACK) Option
for TCP", RFC 2883, DOI 10.17487/RFC2883, July 2000,
<https://www.rfc-editor.org/info/rfc2883>.
[RFC2914] Floyd, S., "Congestion Control Principles", BCP 41,
RFC 2914, DOI 10.17487/RFC2914, September 2000,
<https://www.rfc-editor.org/info/rfc2914>.
[RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
of Explicit Congestion Notification (ECN) to IP",
RFC 3168, DOI 10.17487/RFC3168, September 2001,
<https://www.rfc-editor.org/info/rfc3168>.
[RFC4015] Ludwig, R. and A. Gurtov, "The Eifel Response Algorithm
for TCP", RFC 4015, DOI 10.17487/RFC4015, February 2005,
<https://www.rfc-editor.org/info/rfc4015>.
[RFC5033] Floyd, S. and M. Allman, "Specifying New Congestion
Control Algorithms", BCP 133, RFC 5033,
DOI 10.17487/RFC5033, August 2007,
<https://www.rfc-editor.org/info/rfc5033>.
[RFC5348] Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP
Friendly Rate Control (TFRC): Protocol Specification",
RFC 5348, DOI 10.17487/RFC5348, September 2008,
<https://www.rfc-editor.org/info/rfc5348>.
[RFC5681] Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
Control", RFC 5681, DOI 10.17487/RFC5681, September 2009,
<https://www.rfc-editor.org/info/rfc5681>.
[RFC5682] Sarolahti, P., Kojo, M., Yamamoto, K., and M. Hata,
"Forward RTO-Recovery (F-RTO): An Algorithm for Detecting
Spurious Retransmission Timeouts with TCP", RFC 5682,
DOI 10.17487/RFC5682, September 2009,
<https://www.rfc-editor.org/info/rfc5682>.
[RFC6298] Paxson, V., Allman, M., Chu, J., and M. Sargent,
"Computing TCP's Retransmission Timer", RFC 6298,
DOI 10.17487/RFC6298, June 2011,
<https://www.rfc-editor.org/info/rfc6298>.
[RFC6582] Henderson, T., Floyd, S., Gurtov, A., and Y. Nishida, "The
NewReno Modification to TCP's Fast Recovery Algorithm",
RFC 6582, DOI 10.17487/RFC6582, April 2012,
<https://www.rfc-editor.org/info/rfc6582>.
[RFC6675] Blanton, E., Allman, M., Wang, L., Jarvinen, I., Kojo, M.,
and Y. Nishida, "A Conservative Loss Recovery Algorithm
Based on Selective Acknowledgment (SACK) for TCP",
RFC 6675, DOI 10.17487/RFC6675, August 2012,
<https://www.rfc-editor.org/info/rfc6675>.
[RFC7567] Baker, F., Ed. and G. Fairhurst, Ed., "IETF
Recommendations Regarding Active Queue Management",
BCP 197, RFC 7567, DOI 10.17487/RFC7567, July 2015,
<https://www.rfc-editor.org/info/rfc7567>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
[RFC8985] Cheng, Y., Cardwell, N., Dukkipati, N., and P. Jha, "The
RACK-TLP Loss Detection Algorithm for TCP", RFC 8985,
DOI 10.17487/RFC8985, February 2021,
<https://www.rfc-editor.org/info/rfc8985>.
[RFC9002] Iyengar, J., Ed. and I. Swett, Ed., "QUIC Loss Detection
and Congestion Control", RFC 9002, DOI 10.17487/RFC9002,
May 2021, <https://www.rfc-editor.org/info/rfc9002>.
[RFC9406] Balasubramanian, P., Huang, Y., and M. Olson, "HyStart++:
Modified Slow Start for TCP", RFC 9406,
DOI 10.17487/RFC9406, May 2023,
<https://www.rfc-editor.org/info/rfc9406>.
8.2. Informative References
[AIMD-friendliness]
Briscoe, B. and O. Albisser, "Friendliness between AIMD
Algorithms", DOI 10.48550/arXiv.2305.10581, May 2023,
<https://arxiv.org/abs/2305.10581>.
[BSCLU13] Belhareth, S., Sassatelli, L., Collange, D., Lopez-
Pacheco, D., and G. Urvoy-Keller, "Understanding TCP cubic
performance in the cloud: A mean-field approach", 2013
IEEE 2nd International Conference on Cloud Networking
(CloudNet), DOI 10.1109/cloudnet.2013.6710576, November
2013, <https://doi.org/10.1109/cloudnet.2013.6710576>.
[CEHRX09] Cai, H., Eun, D., Ha, S., Rhee, I., and L. Xu, "Stochastic
convex ordering for multiplicative decrease internet
congestion control", Computer Networks, vol. 53, no. 3,
pp. 365-381, DOI 10.1016/j.comnet.2008.10.012, February
2009, <https://doi.org/10.1016/j.comnet.2008.10.012>.
[FHP00] Floyd, S., Handley, M., and J. Padhye, "A Comparison of
Equation-Based and AIMD Congestion Control", May 2000,
<https://www.icir.org/tfrc/aimd.pdf>.
[GV02] Gorinsky, S. and H. Vin, "Extended Analysis of Binary
Adjustment Algorithms", Technical Report TR2002-39,
Department of Computer Sciences, The University of Texas
at Austin, August 2002, <https://citeseerx.ist.psu.edu/doc
ument?repid=rep1&type=pdf&doi=1828bdcef118b02d3996b8e00b8a
aa92b50abb0f>.
[H16] Ha, S., "Deployment, Testbed, and Simulation Results for
CUBIC", Wayback Machine archive, 3 November 2016,
<https://web.archive.org/web/20161118125842/
http://netsrv.csc.ncsu.edu/wiki/index.php/TCP_Testing>.
[HLRX07] Ha, S., Le, L., Rhee, I., and L. Xu, "Impact of background
traffic on performance of high-speed TCP variant
protocols", Computer Networks, vol. 51, no. 7, pp.
1748-1762, DOI 10.1016/j.comnet.2006.11.005, May 2007,
<https://doi.org/10.1016/j.comnet.2006.11.005>.
[HR11] Ha, S. and I. Rhee, "Taming the elephants: New TCP slow
start", Computer Networks, vol. 55, no. 9, pp. 2092-2110,
DOI 10.1016/j.comnet.2011.01.014, June 2011,
<https://doi.org/10.1016/j.comnet.2011.01.014>.
[HRX08] Ha, S., Rhee, I., and L. Xu, "CUBIC: a new TCP-friendly
high-speed TCP variant", ACM SIGOPS Operating Systems
Review, vol. 42, no. 5, pp. 64-74,
DOI 10.1145/1400097.1400105, July 2008,
<https://doi.org/10.1145/1400097.1400105>.
[K03] Kelly, T., "Scalable TCP: improving performance in
highspeed wide area networks", ACM SIGCOMM Computer
Communication Review, vol. 33, no. 2, pp. 83-91,
DOI 10.1145/956981.956989, April 2003,
<https://doi.org/10.1145/956981.956989>.
[LBEWK16] Lukaseder, T., Bradatsch, L., Erb, B., Van Der Heijden,
R., and F. Kargl, "A Comparison of TCP Congestion Control
Algorithms in 10G Networks", 2016 IEEE 41st Conference on
Local Computer Networks (LCN), DOI 10.1109/lcn.2016.121,
November 2016, <https://doi.org/10.1109/lcn.2016.121>.
[LIU16] Liu, K. and J. Lee, "On Improving TCP Performance over
Mobile Data Networks", IEEE Transactions on Mobile
Computing, vol. 15, no. 10, pp. 2522-2536,
DOI 10.1109/tmc.2015.2500227, October 2016,
<https://doi.org/10.1109/tmc.2015.2500227>.
[RFC3522] Ludwig, R. and M. Meyer, "The Eifel Detection Algorithm
for TCP", RFC 3522, DOI 10.17487/RFC3522, April 2003,
<https://www.rfc-editor.org/info/rfc3522>.
[RFC3649] Floyd, S., "HighSpeed TCP for Large Congestion Windows",
RFC 3649, DOI 10.17487/RFC3649, December 2003,
<https://www.rfc-editor.org/info/rfc3649>.
[RFC3708] Blanton, E. and M. Allman, "Using TCP Duplicate Selective
Acknowledgement (DSACKs) and Stream Control Transmission
Protocol (SCTP) Duplicate Transmission Sequence Numbers
(TSNs) to Detect Spurious Retransmissions", RFC 3708,
DOI 10.17487/RFC3708, February 2004,
<https://www.rfc-editor.org/info/rfc3708>.
[RFC3742] Floyd, S., "Limited Slow-Start for TCP with Large
Congestion Windows", RFC 3742, DOI 10.17487/RFC3742, March
2004, <https://www.rfc-editor.org/info/rfc3742>.
[RFC6937] Mathis, M., Dukkipati, N., and Y. Cheng, "Proportional
Rate Reduction for TCP", RFC 6937, DOI 10.17487/RFC6937,
May 2013, <https://www.rfc-editor.org/info/rfc6937>.
[RFC7661] Fairhurst, G., Sathiaseelan, A., and R. Secchi, "Updating
TCP to Support Rate-Limited Traffic", RFC 7661,
DOI 10.17487/RFC7661, October 2015,
<https://www.rfc-editor.org/info/rfc7661>.
[RFC8312] Rhee, I., Xu, L., Ha, S., Zimmermann, A., Eggert, L., and
R. Scheffenegger, "CUBIC for Fast Long-Distance Networks",
RFC 8312, DOI 10.17487/RFC8312, February 2018,
<https://www.rfc-editor.org/info/rfc8312>.
[RFC8511] Khademi, N., Welzl, M., Armitage, G., and G. Fairhurst,
"TCP Alternative Backoff with ECN (ABE)", RFC 8511,
DOI 10.17487/RFC8511, December 2018,
<https://www.rfc-editor.org/info/rfc8511>.
[RFC9000] Iyengar, J., Ed. and M. Thomson, Ed., "QUIC: A UDP-Based
Multiplexed and Secure Transport", RFC 9000,
DOI 10.17487/RFC9000, May 2021,
<https://www.rfc-editor.org/info/rfc9000>.
[RFC9260] Stewart, R., Tüxen, M., and K. Nielsen, "Stream Control
Transmission Protocol", RFC 9260, DOI 10.17487/RFC9260,
June 2022, <https://www.rfc-editor.org/info/rfc9260>.
[SXEZ19] Sun, W., Xu, L., Elbaum, S., and D. Zhao, "Model-Agnostic
and Efficient Exploration of Numerical Congestion Control
State Space of Real-World TCP Implementations", IEEE/ACM
Transactions on Networking, vol. 29, no. 5, pp. 1990-2004,
DOI 10.1109/tnet.2021.3078161, October 2021,
<https://doi.org/10.1109/tnet.2021.3078161>.
[XHR04] Xu, L., Harfoush, K., and I. Rhee, "Binary increase
congestion control (BIC) for fast long-distance networks",
IEEE INFOCOM 2004, DOI 10.1109/infcom.2004.1354672, March
2004, <https://doi.org/10.1109/infcom.2004.1354672>.
Appendix A. Evolution of CUBIC since the Original Paper
CUBIC has gone through a few changes since the initial release
[HRX08] of its algorithm and implementation. This appendix
highlights the differences between the original paper and [RFC8312].
* The original paper [HRX08] includes the pseudocode of CUBIC
implementation using Linux's pluggable congestion control
framework, which excludes system-specific optimizations. The
simplified pseudocode might be a good starting point for learning
about CUBIC.
* [HRX08] also includes experimental results showing its performance
and fairness.
* The definition of the β__cubic_ constant was changed in [RFC8312].
For example, β__cubic_ in the original paper was referred to as
the window decrease constant, while [RFC8312] changed it to "CUBIC
multiplicative decrease factor". With this change, the current
congestion window size after a congestion event as listed in
[RFC8312] was β__cubic_ * _W_max_, while it was (1-β__cubic_) *
_W_max_ in the original paper.
* Its pseudocode used _W_(last_max)_, while [RFC8312] used _W_max_.
* Its AIMD-friendly window was _W_tcp_, while [RFC8312] used
_W_est_.
Appendix B. Proof of the Average CUBIC Window Size
This appendix contains a proof for the average CUBIC window size
_AVG_W_cubic_ in Figure 6.
We find _AVG_W_cubic_ under a deterministic loss model, where the
number of packets between two successive packet losses is
1/_p_. With this model, CUBIC always operates with the concave
window profile and the time period between two successive packet
losses is _K_.
The average window size _AVG_W_cubic_ is defined as follows, where
the numerator 1/_p_ is the total number of packets between two
successive packet losses and the denominator _K_/_RTT_ is the total
number of RTTs between two successive packet losses.
1
â
p
AVG_W = âââ
cubic K
âââ
RTT
Figure 9
Below, we find _K_ as a function of CUBIC parameters β__cubic_ and
_C_, and network parameters _p_ and _RTT_. According to the
definition of _K_ in Figure 2, we have
ââââââââââââââââââââââ
3 âW - W * β
â² â max max cubic
K = â² âââââââââââââââââââââ
â²â C
Figure 10
The total number of packets between two successive packet losses can
also be obtained as follows, using the window increase function in
Figure 1. Specifically, the window size in the first RTT (i.e.,
_n_=1, or equivalently, _t_=0) is _C_(-_K_)^3+_W_max_ and the window
size in the last RTT (i.e., _n_=_K_/_RTT_, or equivalently, _t_=_K_-
_RTT_) is _C_(-_RTT_)^3+_W_max_.
K
âââ
RTT
â¯â¯
1 â² â 3 â
â = â± âC((n-1) * RTT-K) + W â
p âºâº â maxâ
n=1
K
âââ
RTT
â¯â¯
â² â 3 3 â
= â± âC * RTT (-n) + W â
âºâº â maxâ
n=1
K
âââ
RTT
â¯â¯
3 â² 3 K
= -C * RTT * â± n + W * âââ
âºâº max RTT
n=1
4
3 1 â K â K
≈ -C * RTT * â *âââââ + W * âââ
4 âRTTâ max RTT
4
1 K K
= -C * â * âââ + W * âââ
4 RTT max RTT
Figure 11
After solving the equations in Figures 10 and 11 for _K_ and _W_max_,
we have
ââââââââââââââââââââââââ
â 4 * â1-β â
4 â â cubicâ RTT
K = â² âââââââââââââââââ * âââ
â² âC * â3 + β â p
â²â â cubicâ
Figure 12
The average CUBIC window size _AVG_W_cubic_ can be obtained by
substituting _K_ from Figure 12 in Figure 9.
1 âââââââââââââââââââââââââ
â âC * â3 + β â 3
p 4 â â cubicâ RTT
AVG_W = âââ = â² âââââââââââââââââ * ââââ
cubic K â² â 4 * â1-β â 3
âââ â²â â cubicâ p
RTT
Acknowledgments
Richard Scheffenegger and Alexander Zimmermann originally coauthored
[RFC8312].
These individuals suggested improvements to this document:
* Bob Briscoe
* Christian Huitema
* Gorry Fairhurst
* Jonathan Morton
* Juhamatti Kuusisaari
* Junho Choi
* Markku Kojo
* Martin Duke
* Martin Thomson
* Matt Mathis
* Matt Olson
* Michael Welzl
* Mirja Kühlewind
* Mohit P. Tahiliani
* Neal Cardwell
* Praveen Balasubramanian
* Randall Stewart
* Richard Scheffenegger
* Rod Grimes
* Spencer Dawkins
* Tom Henderson
* Tom Petch
* Wesley Rosenblum
* Yoav Nir
* Yoshifumi Nishida
* Yuchung Cheng
Authors' Addresses
Lisong Xu
University of Nebraska-Lincoln
Department of Computer Science and Engineering
Lincoln, NE 68588-0115
United States of America
Email: xu@unl.edu
URI: https://cse.unl.edu/~xu/
Sangtae Ha
University of Colorado at Boulder
Department of Computer Science
Boulder, CO 80309-0430
United States of America
Email: sangtae.ha@colorado.edu
URI: https://netstech.org/sangtaeha/
Injong Rhee
Bowery Farming
151 W 26th Street, 12th Floor
New York, NY 10001
United States of America
Email: injongrhee@gmail.com
Vidhi Goel
Apple Inc.
One Apple Park Way
Cupertino, CA 95014
United States of America
Email: vidhi_goel@apple.com
Lars Eggert (editor)
NetApp
Stenbergintie 12 B
FI-02700 Kauniainen
Finland
Email: lars@eggert.org
URI: https://eggert.org/
