Internet DRAFT - draft-ietf-aqm-codel
draft-ietf-aqm-codel
AQM K. Nichols
Internet-Draft Pollere, Inc.
Intended status: Experimental V. Jacobson
Expires: April 16, 2018 A. McGregor, ed.
J. Iyengar, ed.
Google
October 13, 2017
Controlled Delay Active Queue Management
draft-ietf-aqm-codel-10
Abstract
This document describes a general framework called CoDel (Controlled
Delay) that controls bufferbloat-generated excess delay in modern
networking environments. CoDel consists of an estimator, a setpoint,
and a control loop. It requires no configuration in normal Internet
deployments.
Status of This Memo
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This Internet-Draft will expire on April 16, 2018.
Copyright Notice
Copyright (c) 2017 IETF Trust and the persons identified as the
document authors. All rights reserved.
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include Simplified BSD License text as described in Section 4.e of
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described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and terms used in this document . . . . . . . . . 4
3. Understanding the Building Blocks of Queue Management . . . . 5
3.1. Estimator . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2. Target Setpoint . . . . . . . . . . . . . . . . . . . . . 8
3.3. Control Loop . . . . . . . . . . . . . . . . . . . . . . 10
4. Overview of the Codel AQM . . . . . . . . . . . . . . . . . . 12
4.1. Non-starvation . . . . . . . . . . . . . . . . . . . . . 13
4.2. Setting INTERVAL . . . . . . . . . . . . . . . . . . . . 13
4.3. Setting TARGET . . . . . . . . . . . . . . . . . . . . . 14
4.4. Use with multiple queues . . . . . . . . . . . . . . . . 15
4.5. Setting up CoDel . . . . . . . . . . . . . . . . . . . . 15
5. Annotated Pseudo-code for CoDel AQM . . . . . . . . . . . . . 16
5.1. Data Types . . . . . . . . . . . . . . . . . . . . . . . 16
5.2. Per-queue state (codel_queue_t instance variables) . . . 17
5.3. Constants . . . . . . . . . . . . . . . . . . . . . . . . 17
5.4. Enqueue routine . . . . . . . . . . . . . . . . . . . . . 17
5.5. Dequeue routine . . . . . . . . . . . . . . . . . . . . . 17
5.6. Helper routines . . . . . . . . . . . . . . . . . . . . . 19
5.7. Implementation considerations . . . . . . . . . . . . . . 20
6. Further Experimentation . . . . . . . . . . . . . . . . . . . 21
7. Security Considerations . . . . . . . . . . . . . . . . . . . 21
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 21
9. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 21
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 22
10.1. Normative References . . . . . . . . . . . . . . . . . . 22
10.2. Informative References . . . . . . . . . . . . . . . . . 22
10.3. URIs . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Appendix A. Applying CoDel in the datacenter . . . . . . . . . . 24
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 25
1. Introduction
The "persistently full buffer" problem has been discussed in the IETF
community since the early 80's [RFC896]. The IRTF's End-to-End
Research Group called for the deployment of active queue management
(AQM) to solve the problem in 1998 [RFC2309]. Despite this
awareness, the problem has only gotten worse as Moore's Law growth in
memory density fueled an exponential increase in buffer pool size.
Efforts to deploy AQM have been frustrated by difficult configuration
and negative impact on network utilization. This "bufferbloat"
problem [TSV2011] [BB2011] has become increasingly important
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throughout the Internet but particularly at the consumer edge. Queue
management has become more critical due to increased consumer use of
the Internet, mixing large video transactions with time-critical VoIP
and gaming.
An effective AQM remediates bufferbloat at a bottleneck while "doing
no harm" at hops where buffers are not bloated. The development and
deployment of AQM however is frequently subject to misconceptions
about the cause of packet queues in network buffers. Network buffers
exist to absorb the packet bursts that occur naturally in
statistically multiplexed networks. Buffers helpfully absorb the
queues created by such reasonable packet network behavior as short-
term mismatches in traffic arrival and departure rates that arise
from upstream resource contention, transport conversation startup
transients and/or changes in the number of conversations sharing a
link. Unfortunately, other less useful network behaviors can cause
queues to fill and their effects are not nearly as benign.
Discussion of these issues and the reason why the solution is not
simply smaller buffers can be found in [RFC2309], [VANQ2006],
[REDL1998], and [CODEL2012]. To understand queue management, it is
critical to understand the difference between the necessary, useful
"good" queue, and the counterproductive "bad" queue.
Several approaches to AQM have been developed over the past two
decades but none has been widely deployed due to performance
problems. When designed with the wrong conceptual model for queues,
AQMs have limited operational range, require a lot of configuration
tweaking, and frequently impair rather than improve performance.
Learning from this past history, the CoDel approach is designed to
meet the following goals:
o Making it parameterless for normal operation, with no knobs for
operators, users, or implementers to adjust.
o Being able to distinguish "good queue" from bad queue and treat
them differently, that is, keep delay low while permitting
necessary bursts of traffic.
o Controlling delay while insensitive (or nearly so) to round trip
delays, link rates and traffic loads; this goal is to "do no harm"
to network traffic while controlling delay.
o Adapting to dynamically changing link rates with no negative
impact on utilization.
o Allowing simple and efficient implementation (can easily span the
spectrum from low-end access points and home routers up to high-
end router silicon).
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CoDel has five major differences from prior AQMs: use of local queue
minimum to track congestion ("bad queue"), use of an efficient single
state variable representation of that tracked statistic, use of
packet sojourn time as the observed datum, rather than packets,
bytes, or rates, use of mathematically determined setpoint derived
from maximizing network power [KLEIN81], and a modern state space
controller.
CoDel configures itself based on a round-trip time metric which can
be set to 100ms for the normal, terrestrial Internet. With no
changes to parameters, CoDel is expected to work across a wide range
of conditions, with varying links and the full range of terrestrial
round trip times.
CoDel is easily adapted to multiple queue systems as shown by [FQ-
CODEL-ID]. Implementers and users SHOULD use the fq_codel multiple-
queue approach as it deals with many problems beyond the reach of an
AQM on a single queue.
CoDel was first published in [CODEL2012] and has been implemented in
the Linux kernel.
Note that while this document refers to dropping packets when
indicated by CoDel, it is reasonable to ECN-mark packets instead as
well.
2. Conventions and terms used in this document
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].
In this document, these words will appear with that interpretation
only when in ALL CAPS. Lower case uses of these words are not to be
interpreted as carrying [RFC2119] significance.
The following terms are defined as used in this document:
sojourn time: the amount of time a packet has spent in a particular
buffer, i.e. the time a packet departs the buffer minus the time the
packet arrived at the buffer. A packet can depart a buffer via
transmission or drop.
standing queue: a queue (in packets, bytes, or time delay) in a
buffer that persists for a "long" time where "long" is on the order
of the longer round trip times through the buffer as discussed in
section 4.2. A standing queue occurs when the minimum queue over the
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"long" time is nonzero and is usually tolerable and even desirable as
long as it does not exceed some target delay.
bottleneck bandwidth: the limiting bandwidth along a network path.
3. Understanding the Building Blocks of Queue Management
At the heart of queue management is the notion of "good queue" and
"bad queue" and the search for ways to get rid of the bad queue
(which only adds delay) while preserving the good queue (which
provides for good utilization). This section explains queueing, both
good and bad, and covers the CoDel building blocks that can be used
to manage packet buffers to keep their queues in the "good" range.
Packet queues form in buffers facing bottleneck links, i.e., where
the line rate goes from high to low or where many links converge.
The well-known bandwidth-delay product (sometimes called "pipe size")
is the bottleneck's bandwidth multiplied by the sender-receiver-
sender round-trip delay, and is the amount of data that has to be in
transit between two hosts in order to run the bottleneck link at 100%
utilization. To explore how queues can form, consider a long-lived
TCP connection with a 25 packet window sending through a connection
with a bandwidth-delay product of 20 packets. After an initial burst
of packets the connection will settle into a five packet (+/-1)
standing queue; this standing queue size is determined by the
mismatch between the window size and the pipe size, and is unrelated
to the connection's sending rate. The connection has 25 packets in
flight at all times, but only 20 packets arrive at the destination
over a round trip time. If the TCP connection has a 30 packet
window, the queue will be ten packets with no change in sending rate.
Similarly, if the window is 20 packets, there will be no queue but
the sending rate is the same. Nothing can be inferred about the
sending rate from the queue size, and any queue other than transient
bursts only creates delays in the network. The sender needs to
reduce the number of packets in flight rather than sending rate.
In the above example, the five packet standing queue can be seen to
contribute nothing but delay to the connection, and thus is clearly
"bad queue". If, in our example, there is a single bottleneck link
and it is much slower than the link that feeds it (say, a high-speed
ethernet link into a limited DSL uplink) a 20 packet buffer at the
bottleneck might be necessary to temporarily hold the 20 packets in
flight to keep the bottleneck link's utilization high. The burst of
packets should drain completely (to 0 or 1 packets) within a round
trip time and this transient queue is "good queue" because it allows
the connection to keep the 20 packets in flight and for the
bottleneck link to be fully utilized. In terms of the delay
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experienced, the "good queue" goes away in about a round trip time,
while "bad queue" hangs around for longer, causing delays.
Effective queue management detects "bad queue" while ignoring "good
queue" and takes action to get rid of the bad queue when it is
detected. The goal is a queue controller that accomplishes this
objective. To control a queue, we need three basic components
o Estimator - figure out what we've got.
o Target setpoint - know what we want.
o Control loop - if what we've got isn't what we want, we need a way
to move it there.
3.1. Estimator
The estimator both observes the queue and detects when good queue
turns to bad queue and vice versa. CoDel has two parts to its
estimator: what is observed as an indicator of queue and how the
observations are used to detect good/bad queue.
Queue length has been widely used as an observed indicator of
congestion and is frequently conflated with sending rate. Use of
queue length as a metric is sensitive to how and when the length is
observed. A high speed arrival link to a buffer serviced at a much
lower rate can rapidly build up a queue that might disperse
completely or down to a single packet before a round trip time has
elapsed. If the queue length is monitored at packet arrival (as in
original RED) or departure time, every packet will see a queue with
one possible exception. If the queue length itself is time sampled
(as recommended in [REDL1998], a truer picture of the queue's
occupancy can be gained at the expense of considerable implementation
complexity.
The use of queue length is further complicated in networks that are
subject to both short and long term changes in available link rate
(as in WiFi). Link rate drops can result in a spike in queue length
that should be ignored unless it persists. It is not the queue
length that should be controlled but the amount of excess delay
packets experience due to a persistent or standing queue, which means
that the packet sojourn time in the buffer is exactly what we want to
track. Tracking the packet sojourn times in the buffer observes the
actual delay experienced by each packet. Sojourn time allows queue
management to be independent of link rate, gives superior performance
to use of buffer size, and is directly related to user-visible
performance. It works regardless of line rate changes or link
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sharing by multiple queues (which the individual queues may
experience as changing rates).
Consider a link shared by two queues with different priorities.
Packets that arrive at the high priority queue are sent as soon as
the link is available while packets in the other queue have to wait
until the high priority queue is empty (i.e., a strict priority
scheduler). The number of packets in the high priority queue might
be large but the queue is emptied quickly and the amount of time each
packet spends enqueued (the sojourn time) is not large. The other
queue might have a smaller number of packets, but packet sojourn
times will include the waiting time for the high priority packets to
be sent. This makes the sojourn time a good sample of the congestion
that each separate queue is experiencing. This example also shows
how the metric of sojourn time is independent of the number of queues
or the service discipline used, and is instead indicative of
congestion seen by the individual queues.
How can observed sojourn time be used to separate good queue from bad
queue? Although averages, especially of queue length, have
previously been widely used as an indicator of bad queue, their
efficacy is questionable. Consider the burst that disperses every
round trip time. The average queue will be one-half the burst size,
though this might vary depending on when the average is computed and
the timing of arrivals. The average queue sojourn time would be one-
half the time it takes to clear the burst. The average then would
indicate a persistent queue where there is none. Instead of averages
we recommend tracking the minimum sojourn time, then, if there is one
packet that has a zero sojourn time then there is no persistent
queue.
A persistent queue can be detected by tracking the (local) minimum
queue delay packets experience. To ensure that this minimum value
does not become stale, it has to have been experienced recently, i.e.
during an appropriate past time interval. This interval is the
maximum amount of time a minimum value is considered to be in effect,
and is related to the amount of time it takes for the largest
expected burst to drain. Conservatively, this interval SHOULD be at
least a round trip time to avoid falsely detecting a persistent queue
and not a lot more than a round trip time to avoid delay in detecting
the persistent queue. This suggests that the appropriate interval
value is the maximum round-trip time of all the connections sharing
the buffer.
(The following key insight makes computation of the local minimum
efficient: It is sufficient to keep a single state variable of how
long the minimum has been above or below the target value rather than
retaining all the local values to compute the minimum, leading to
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both storage and computational savings. We use this insight in the
pseudo-code for CoDel later in the document.)
These two parts, use of sojourn time as observed values and the local
minimum as the statistic to monitor queue congestion are key to
CoDel's estimator building block. The local minimum sojourn time
provides an accurate and robust measure of standing queue and has an
efficient implementation. In addition, use of the minimum sojourn
time has important advantages in implementation. The minimum packet
sojourn can only be decreased when a packet is dequeued which means
that all the work of CoDel can take place when packets are dequeued
for transmission and that no locks are needed in the implementation.
The minimum is the only statistic with this property.
A more detailed explanation with many pictures can be found in
http://www.ietf.org/proceedings/84/slides/slides-84-tsvarea-4.pdf
[1].
3.2. Target Setpoint
Now that we have a robust way of detecting standing queue, we need a
target setpoint that tells us when to act. If the controller is set
to take action as soon as the estimator has a non-zero value, the
average drop rate will be maximized, which minimizes TCP goodput
[MACTCP1997]. Also, this policy results in no backlog over time (no
persistent queue), which negates much of the value of having a
buffer, since it maximizes the bottleneck link bandwidth lost due to
normal stochastic variation in packet interarrival time. We want a
target that maximizes utilization while minimizing delay. Early in
the history of packet networking, Kleinrock developed the analytic
machinery to do this using a quantity he called 'power', which is the
ratio of a normalized throughput to a normalized delay [KLEIN81].
It is straightforward to derive an analytic expression for the
average goodput of a TCP conversation at a given round-trip time r
and target f (where f is expressed as a fraction of r). Reno TCP,
for example, yields:
goodput = r (3 + 6f - f^2) / (4 (1+f))
Since the peak queue delay is simply the product of f and r, power is
solely a function of f since the r's in the numerator and denominator
cancel:
power is proportional to (1 + 2f - 1/3 f^2) / (1 + f)^2
As Kleinrock observed, the best operating point, in terms of
bandwidth / delay tradeoff, is the peak power point, since points off
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the peak represent a higher cost (in delay) per unit of bandwidth.
The power vs. f curve for any Additive Increase Multiplicative
Decrease (AIMD) TCP is monotone decreasing. But the curve is very
flat for f < 0.1 followed by a increasing curvature with a knee
around f = 0.2, then a steep, almost linear fall off [TSV84]. Since
the previous equation showed that goodput is monotone increasing with
f, the best operating point is near the right edge of the flat top
since that represents the highest goodput achievable for a negligible
increase in delay. However, since the r in the model is a
conservative upper bound, a target of 0.1r runs the risk of pushing
shorter RTT connections over the knee and giving them higher delay
for no significant goodput increase. Generally, a more conservative
target of 0.05r offers a good utilization vs. delay tradeoff while
giving enough headroom to work well with a large variation in real
RTT.
As the above analysis shows, a very small standing queue gives close
to 100% utilization of the bottleneck link. While this result was
for Reno TCP, the derivation uses only properties that must hold for
any 'TCP friendly' transport. We have verified by both analysis and
simulation that this result holds for Reno, Cubic, and Westwood
[TSV84]. This results in a particularly simple form for the target:
the ideal range for the permitted standing queue, or the target
setpoint, is between 5% and 10% of the TCP connection's RTT.
We used simulation to explore the impact when TCPs are mixed with
other traffic and with connections of different RTTs. Accordingly,
we experimented extensively with values in the 5-10% of RTT range
and, overall, used target values between 1 and 20 milliseconds for
RTTs from 30 to 500ms and link bandwidths of 64Kbps to 100Mbps to
experimentally explore a value for the target that gives consistently
high utilization while controlling delay across a range of
bandwidths, RTTs, and traffic loads. Our results were notably
consistent with the mathematics above.
A congested (but not overloaded) CoDel link with traffic composed
solely or primarily of long-lived TCP flows will have a median delay
through the link will tend to the target. For bursty traffic loads
and for overloaded conditions (where it is difficult or impossible
for all the arriving flows to be accommodated) the median queues will
be longer than the target.
The non-starvation drop inhibit feature dominates where the link rate
becomes very small. By inhibiting drops when there is less than an
(outbound link) MTU worth of bytes in the buffer, CoDel adapts to
very low bandwidth links, as shown in [CODEL2012].
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3.3. Control Loop
Section 3.1 describes a simple, reliable way to measure bad
(persistent) queue. Section 3.2 shows that TCP congestion control
dynamics gives rise to a target setpoint for this measure that's a
provably good balance between enhancing throughput and minimizing
delay, and that this target is a constant fraction of the same
'largest average RTT' interval used to distinguish persistent from
transient queue. The only remaining building block needed for a
basic AQM is a 'control loop' algorithm to effectively drive the
queueing system from any 'persistent queue above the target' state to
a state where the persistent queue is below the target.
Control theory provides a wealth of approaches to the design of
control loops. Most of classical control theory deals with the
control of linear, time-invariant, single-input-single-output (SISO)
systems. Control loops for these systems generally come from a (well
understood) class known as Proportional-Integral-Derivative (PID)
controllers. Unfortunately, a queue is not a linear system and an
AQM operates at the point of maximum non-linearity (where the output
link bandwidth saturates so increased demand creates delay rather
than higher utilization). Output queues are also not time-invariant
since traffic is generally a mix of connections which start and stop
at arbitrary times and which can have radically different behaviors
ranging from "open loop" UDP audio/video to "closed-loop" congestion-
avoiding TCP. Finally, the constantly changing mix of connections
(which can't be converted to a single 'lumped parameter' model
because of their transfer function differences) makes the system
multi-input-multi-output (MIMO), not SISO.
Since queueing systems match none of the prerequisites for a
classical controller, a modern state-space controller is a better
approach with states 'no persistent queue' and 'has persistent
queue'. Since Internet traffic mixtures change rapidly and
unpredictably, a noise and error tolerant adaptation algorithm like
Stochastic Gradient is a good choice. Since there's essentially no
information in the amount of persistent queue [TSV84], the adaptation
should be driven by how long it has persisted.
Consider the two extremes of traffic behavior, a single open-loop UDP
video stream and a single, long-lived TCP bulk data transfer. If the
average bandwidth of the UDP video stream is greater that the
bottleneck link rate, the link's queue will grow and the controller
will eventually enter 'has persistent queue' state and start dropping
packets. Since the video stream is open loop, its arrival rate is
unaffected by drops so the queue will persist until the average drop
rate is greater than the output bandwidth deficit (= average arrival
rate - average departure rate) so the job of the adaptation algorithm
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is to discover this rate. For this example, the adaptation could
consist of simply estimating the arrival and departure rates then
dropping at a rate slightly greater than their difference. But this
class of algorithm won't work at all for the bulk data TCP stream.
TCP runs in closed-loop flow balance [TSV84] so its arrival rate is
almost always exactly equal to the departure rate - the queue isn't
the result of a rate imbalance but rather a mismatch between the TCP
sender's window and the source-destination-source round-trip path
capacity (i.e., the connection's bandwidth-delay product). The
sender's TCP congestion avoidance algorithm will slowly increase the
send window (one packet per round-trip-time) [RFC2581] which will
eventually cause the bottleneck to enter 'has persistent queue'
state. But, since the average input rate is the same as the average
output rate, the rate deficit estimation that gave the correct drop
rate for the video stream would compute a drop rate of zero for the
TCP stream. However, if the output link drops one packet as it
enters 'has persistent queue' state, when the sender discovers this
(via TCP's normal packet loss repair mechanisms) it will reduce its
window by a factor of two [RFC2581] so, one round-trip-time after the
drop, the persistent queue will go away.
If there were N TCP conversations sharing the bottleneck, the
controller would have to drop O(N) packets, one from each
conversation, to make all the conversations reduce their window to
get rid of the persistent queue. If the traffic mix consists of
short (<= bandwidth-delay product) conversations, the aggregate
behavior becomes more like the open-loop video example since each
conversation is likely to have already sent all its packets by the
time it learns about a drop so each drop has negligible effect on
subsequent traffic.
The controller does not know the number, duration, or kind of
conversations creating its queue, so it has to learn the appropriate
response. Since single drops can have a large effect if the degree
of multiplexing (the number of active conversations) is small,
dropping at too high a rate is likely to have a catastrophic effect
on throughput. Dropping at a low rate (< 1 packet per round-trip-
time) then increasing the drop rate slowly until the persistent queue
goes below the target is unlikely to overdrop and is guaranteed to
eventually dissipate the persistent queue. This stochastic gradient
learning procedure is the core of CoDel's control loop (the gradient
exists because a drop always reduces the (instantaneous) queue so an
increasing drop rate always moves the system "down" toward no
persistent queue, regardless of traffic mix).
The "next drop time" is decreased in inverse proportion to the square
root of the number of drops since the drop state was entered, using
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the well-known nonlinear relationship of drop rate to throughput to
get a linear change in throughput [REDL1998], [MACTCP1997].
Since the best rate to start dropping is at slightly more than one
packet per RTT, the controller's initial drop rate can be directly
derived from the estimator's interval. When the minimum sojourn time
first crosses the target and CoDel drops a packet, the earliest the
controller could see the effect of the drop is the round trip time
(interval) + the local queue wait time (the target). If the next
drop happens any earlier than this time (interval + target), CoDel
will overdrop. In practice, the local queue waiting time tends to
vary, so making the initial drop spacing (i.e., the time to the
second drop) be exactly the minimum possible also leads to
overdropping. Analysis of simulation and real-world measured data
shows that the 75th percentile magnitude of this variation is less
than the target, and so the initial drop spacing SHOULD be set to the
estimator's interval (i.e., initial drop spacing = interval) to
ensure that the controller has accounted for acceptable congestion
delays.
Use of the minimum statistic lets the controller be placed in the
dequeue routine with the estimator. This means that the control
signal (the drop) can be sent at the first sign of bad queue (as
indicated by the sojourn time) and that the controller can stop
acting as soon as the sojourn time falls below the target. Dropping
at dequeue has both implementation and control advantages.
4. Overview of the Codel AQM
CoDel was initially designed as a bufferbloat solution for the
consumer network edge. The CoDel building blocks are able to adapt
to different or time-varying link rates, to be easily used with
multiple queues, to have excellent utilization with low delay, and to
have a simple and efficient implementation.
The CoDel algorithm described in the rest of this document uses two
key variables: TARGET, which is the controller's target setpoint
described in Section 3.2 and INTERVAL, which is the estimator's
interval described in Section 3.3.
The only setting CoDel requires is the INTERVAL value, and as 100ms
satisfies that definition for normal Internet usage, CoDel can be
parameter-free for consumer use. To ensure that link utilization is
not adversely affected, CoDel's estimator sets TARGET to one that
optimizes power. CoDel's controller does not drop packets when the
drop would leave the queue empty or with fewer than a maximum
transmission unit (MTU) worth of bytes in the buffer. Section 3.2
shows that an ideal TARGET is 5-10% of the connection round trip time
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(RTT). In the open terrestrial-based Internet, especially at the
consumer edge, we expect most unbloated RTTs to have a ceiling of
100ms [CHARB2007]. Using this RTT gives a minimum TARGET of 5ms and
INTERVAL of 100ms. In practice, uncongested links will see sojourn
times below TARGET more often than once per RTT, so the estimator is
not overly sensitive to the value of INTERVAL.
When the estimator finds a persistent delay above TARGET, the
controller enters the drop state where a packet is dropped and the
next drop time is set. As discussed in section 3.3, the initial next
drop spacing is intended to be long enough to give the endpoints time
to react to the single drop so SHOULD be set to a value equal to
INTERVAL. If the estimator's output falls below TARGET, the
controller cancels the next drop and exits the drop state. (The
controller is more sensitive than the estimator to an overly short
INTERVAL value, since an unnecessary drop would occur and lower link
utilization.) If next drop time is reached while the controller is
still in drop state, the packet being dequeued is dropped and the
next drop time is recalculated.
Additional logic prevents re-entering the drop state too soon after
exiting it and resumes the drop state at a recent control level, if
one exists. This logic is described more precisely in the pseudo-
code below. Additional work is required to determine the frequency
and importance of re-entering the drop state.
Note that CoDel AQM only enters its drop state when the local minimum
sojourn delay has exceeded TARGET for a time period long enough for
normal bursts to dissipate, ensuring that a burst of packets that
fits in the pipe will not be dropped.
4.1. Non-starvation
CoDel's goals are to control delay with little or no impact on link
utilization and to be deployed on a wide range of link bandwidths,
including variable-rate links, without reconfiguration. To keep from
making drops when it would starve the output link, CoDel makes
another check before dropping to see if at least an MTU worth of
bytes remains in the buffer. If not, the packet SHOULD NOT be
dropped and, therefore, CoDel exits the drop state. The MTU size can
be set adaptively to the largest packet seen so far or can be read
from the interface driver.
4.2. Setting INTERVAL
The INTERVAL value is chosen to give endpoints time to react to a
drop without being so long that response times suffer. CoDel's
estimator, TARGET, and control loop all use INTERVAL. Understanding
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their derivation shows that CoDel is the most sensitive to the value
of INTERVAL for single long-lived TCPs with a decreased sensitivity
for traffic mixes. This is fortunate as RTTs vary across connections
and are not known a priori. The best policy seems to be to use an
INTERVAL value slightly larger than the RTT seen by most of the
connections using a link, a value that can be determined as the
largest RTT seen if the value is not an outlier (use of a 95-99th
percentile value should work). In practice, this value is not known
or measured (though see section 6.2 for an application where INTERVAL
is measured). An INTERVAL setting of 100ms works well across a range
of RTTs from 10ms to 1 second (excellent performance is achieved in
the range from 10 ms to 300ms). For devices intended for the normal
terrestrial Internet, INTERVAL SHOULD have a value of 100ms. This
will only cause overdropping where a long-lived TCP has an RTT longer
than 100ms and there is little or no mixing with other connections
through the link.
4.3. Setting TARGET
TARGET is the maximum acceptable persistent queue delay above which
CoDel is dropping or preparing to drop and below which CoDel will not
drop. TARGET SHOULD be set to 5ms for normal Internet traffic.
The calculations of section 3.2 show that the best TARGET value is
5-10% of the RTT, with the low end of 5% preferred. Extensive
simulations exploring the impact of different TARGET values when used
with mixed traffic flows with different RTTs and different bandwidths
show that below a TARGET of 5ms, utilization suffers for some
conditions and traffic loads, and above 5ms showed very little or no
improvement in utilization.
Sojourn times must remain above the TARGET for INTERVAL amount of
time in order to enter the drop state. Any packet with a sojourn
time less than TARGET will reset the time that the queue was last
below TARGET. Since Internet traffic has very dynamic
characteristics, the actual sojourn delay experienced by packets
varies greatly and is often less than TARGET unless the overload is
excessive. When a link is not overloaded, it is not a bottleneck and
packet sojourn times will be small or nonexistent. In the usual
case, there are only one or two places along a path where packets
will encounter a bottleneck (usually at the edge), so the total
amount of queueing delay experienced by a packet should be less than
10ms even under extremely congested conditions. This net delay is
substantially lower than common current queueing delays on the
Internet that grow to orders of seconds [NETAL2010, CHARB2007].
A note on the roles of TARGET and the minimum-tracking INTERVAL.
TARGET SHOULD NOT be increased in response to lower layers that have
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a bursty nature, where packets are transmitted for short periods
interspersed with idle periods where the link is waiting for
permission to send. CoDel's estimator will "see" the effective
transmission rate over an INTERVAL amount of time, and increasing
TARGET only leads to longer queue delays. On the other hand, where a
significant additional delay is added to the intrinsic RTT of most or
all packets due to the waiting time for a transmission, it is
necessary to increase INTERVAL by that extra delay. TARGET SHOULD
NOT be adjusted for such short-term bursts, but INTERVAL MAY need to
be adjusted if the path's intrinsic RTT changes.
4.4. Use with multiple queues
CoDel is easily adapted to multiple queue systems. With other
approaches there is always a question of how to account for the fact
that each queue receives less than the full link rate over time and
usually sees a varying rate over time. This is what CoDel excels at:
using a packet's sojourn time in the buffer completely circumvents
this problem. In a multiple-queue setting, a separate CoDel
algorithm runs on each queue, but each CoDel instance uses the packet
sojourn time the same way a single-queue CoDel does. Just as a
single-queue CoDel adapts to changing link bandwidths [CODEL2012], so
does a multiple-queue CoDel system. As an optimization to avoid
queueing more than necessary, when testing for queue occupancy before
dropping, the total occupancy of all queues sharing the same output
link SHOULD be used. This property of CoDel has been exploited in
fq_codel [FQ-CODEL-ID], which hashes on the packet header fields to
determine a specific bin, or sub-queue, for the packet, and runs
CoDel on each bin or sub-queue thus creating a well-mixed output flow
and obviating issues of reverse path flows (including "ack
compression").
4.5. Setting up CoDel
CoDel is set for use in devices in the open Internet. An INTERVAL
setting of 100ms is used, TARGET is set to 5% of INTERVAL, and the
initial drop spacing is also set to the INTERVAL. These settings
have been chosen so that a device, such as a small WiFi router, can
be sold without the need for any values to be made adjustable,
yielding a parameterless implementation. In addition, CoDel is
useful in environments with significantly different characteristics
from the normal Internet, for example, in switches used as a cluster
interconnect within a data center. Since cluster traffic is entirely
internal to the data center, round trip latencies are low (typically
<100us) but bandwidths are high (1-40Gbps) so it's relatively easy
for the aggregation phase of a distributed computation (e.g., the
Reduce part of a Map/Reduce) to persistently fill then overflow the
modest per-port buffering available in most high speed switches. A
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CoDel configured for this environment (TARGET and INTERVAL in the
microsecond rather than millisecond range) can minimize drops or ECN
marks while keeping throughput high and latency low.
Devices destined for these environments MAY use a different value for
INTERVAL, where suitable. If appropriate analysis indicates, the
TARGET MAY be set to some other value in the 5-10% of INTERVAL and
the initial drop spacing MAY be set to a value of 1.0 to 1.2 times
INTERVAL. But these settings will cause problems such as
overdropping and low throughput if used on the open Internet, so
devices that allow CoDel to be configured SHOULD default to Internet-
appropriate values given in this document.
5. Annotated Pseudo-code for CoDel AQM
What follows is the CoDel algorithm in C++-like pseudo-code. Since
CoDel adds relatively little new code to a basic tail-drop fifo-
queue, we have attempted to highlight just these additions by
presenting CoDel as a sub-class of a basic fifo-queue base class.
The reference code is included to aid implementers who wish to apply
CoDel to queue management as described here or to adapt its
principles to other applications.
Implementors are strongly encouraged to also look at the Linux kernel
version of CoDel - a well-written, well tested, real-world, C-based
implementation. As of this writing, it is available at
https://github.com/torvalds/linux/blob/master/net/sched/sch_codel.c.
5.1. Data Types
time_t is an integer time value in units convenient for the system.
The code presented here uses 0 as a flag value to indicate "no time
set."
packet_t* is a pointer to a packet descriptor. We assume it has a
tstamp field capable of holding a time_t and that field is available
for use by CoDel (it will be set by the enqueue routine and used by
the dequeue routine).
queue_t is a base class for queue objects (the parent class for
codel_queue_t objects). We assume it has enqueue() and dequeue()
methods that can be implemented in child classes. We assume it has a
bytes() method that returns the current queue size in bytes. This
can be an approximate value. The method is invoked in the dequeue()
method but shouldn't require a lock with the enqueue() method.
flag_t is a Boolean.
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5.2. Per-queue state (codel_queue_t instance variables)
time_t first_above_time_ = 0; // Time to declare sojourn time above
// TARGET
time_t drop_next_ = 0; // Time to drop next packet
uint32_t count_ = 0; // Packets dropped in drop state
uint32_t lastcount_ = 0; // Count from previous iteration
flag_t dropping_ = false; // Set to true if in drop state
5.3. Constants
time_t TARGET = MS2TIME(5); // 5ms TARGET queue delay
time_t INTERVAL = MS2TIME(100); // 100ms sliding-minimum window
u_int maxpacket = 512; // Maximum packet size in bytes
// (SHOULD use interface MTU)
5.4. Enqueue routine
All the work of CoDel is done in the dequeue routine. The only CoDel
addition to enqueue is putting the current time in the packet's
tstamp field so that the dequeue routine can compute the packet's
sojourn time. Note that packets arriving at a full buffer will be
dropped, but these drops are not counted towards CoDel's
computations.
void codel_queue_t::enqueue(packet_t* pkt)
{
pkt->timestamp() = clock();
queue_t::enqueue(pkt);
}
5.5. Dequeue routine
This is the heart of CoDel. There are two branches based on whether
the controller is in drop state: (i) if the controller is in drop
state (that is, the minimum packet sojourn time is greater than
TARGET) then the controller checks if it is time to leave drop state
or schedules the next drop(s); or (ii) if the controller is not in
drop state, it determines if it should enter drop state and do the
initial drop.
packet_t* CoDelQueue::dequeue()
{
time_t now = clock();
dodequeue_result r = dodequeue(now);
uint32_t delta;
if (dropping_) {
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if (! r.ok_to_drop) {
// sojourn time below TARGET - leave drop state
dropping_ = false;
}
// Time for the next drop. Drop current packet and dequeue
// next. If the dequeue doesn't take us out of dropping
// state, schedule the next drop. A large backlog might
// result in drop rates so high that the next drop should
// happen now, hence the 'while' loop.
while (now >= drop_next_ && dropping_) {
drop(r.p);
++count_;
r = dodequeue(now);
if (! r.ok_to_drop) {
// leave drop state
dropping_ = false;
} else {
// schedule the next drop.
drop_next_ = control_law(drop_next_, count_);
}
}
// If we get here we're not in drop state. The 'ok_to_drop'
// return from dodequeue means that the sojourn time has been
// above 'TARGET' for 'INTERVAL' so enter drop state.
} else if (r.ok_to_drop) {
drop(r.p);
r = dodequeue(now);
dropping_ = true;
// If min went above TARGET close to when it last went
// below, assume that the drop rate that controlled the
// queue on the last cycle is a good starting point to
// control it now. ('drop_next' will be at most 'INTERVAL'
// later than the time of the last drop so 'now - drop_next'
// is a good approximation of the time from the last drop
// until now.) Implementations vary slightly here; this is
// the Linux version, which is more widely deployed and
// tested.
delta = count_ - lastcount_;
count_ = 1;
if ((delta > 1) && (now - drop_next_ < 16*INTERVAL))
count_ = delta;
drop_next_ = control_law(now, count_);
lastcount_ = count_;
}
return (r.p);
}
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5.6. Helper routines
Since the degree of multiplexing and nature of the traffic sources is
unknown, CoDel acts as a closed-loop servo system that gradually
increases the frequency of dropping until the queue is controlled
(sojourn time goes below TARGET). This is the control law that
governs the servo. It has this form because of the sqrt(p)
dependence of TCP throughput on drop probability. Note that for
embedded systems or kernel implementation, the inverse sqrt can be
computed efficiently using only integer multiplication.
time_t codel_queue_t::control_law(time_t t, uint32_t count)
{
return t + INTERVAL / sqrt(count);
}
Next is a helper routine the does the actual packet dequeue and
tracks whether the sojourn time is above or below TARGET and, if
above, if it has remained above continuously for at least INTERVAL
amount of time. It returns two values: a Boolean indicating if it is
OK to drop (sojourn time above TARGET for at least INTERVAL), and the
packet dequeued.
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typedef struct {
packet_t* p;
flag_t ok_to_drop;
} dodequeue_result;
dodequeue_result codel_queue_t::dodequeue(time_t now)
{
dodequeue_result r = { queue_t::dequeue(), false };
if (r.p == NULL) {
// queue is empty - we can't be above TARGET
first_above_time_ = 0;
return r;
}
// To span a large range of bandwidths, CoDel runs two
// different AQMs in parallel. One is sojourn-time-based
// and takes effect when the time to send an MTU-sized
// packet is less than TARGET. The 1st term of the "if"
// below does this. The other is backlog-based and takes
// effect when the time to send an MTU-sized packet is >=
// TARGET. The goal here is to keep the output link
// utilization high by never allowing the queue to get
// smaller than the amount that arrives in a typical
// interarrival time (MTU-sized packets arriving spaced
// by the amount of time it takes to send such a packet on
// the bottleneck). The 2nd term of the "if" does this.
time_t sojourn_time = now - r.p->tstamp;
if (sojourn_time_ < TARGET || bytes() <= maxpacket_) {
// went below - stay below for at least INTERVAL
first_above_time_ = 0;
} else {
if (first_above_time_ == 0) {
// just went above from below. if still above at
// first_above_time, will say it's ok to drop.
first_above_time_ = now + INTERVAL;
} else if (now >= first_above_time_) {
r.ok_to_drop = true;
}
}
return r;
}
5.7. Implementation considerations
time_t is an integer time value in units convenient for the system.
Resolution to at least a millisecond is required and better
resolution is useful up to the minimum possible packet time on the
output link; 64- or 32-bit widths are acceptable but with 32 bits the
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resolution should be no finer than 2^{-16} to leave enough dynamic
range to represent a wide range of queue waiting times. Narrower
widths also have implementation issues due to overflow (wrapping) and
underflow (limit cycles because of truncation to zero) that are not
addressed in this pseudocode.
Since CoDel requires relatively little per-queue state and no direct
communication or state sharing between the enqueue and dequeue
routines, it is relatively simple to add CoDel to almost any packet
processing pipeline, including ASIC- or NPU-based forwarding engines.
One issue to consider is dodequeue()'s use of a 'bytes()' function to
determine the current queue size in bytes. This value does not need
to be exact. If the enqueue part of the pipeline keeps a running
count of the total number of bytes it has put into the queue and the
dequeue routine keeps a running count of the total bytes it has
removed from the queue, 'bytes()' is simply the difference between
these two counters (32-bit counters should be adequate.) Enqueue has
to update its counter once per packet queued but it does not matter
when (before, during or after the packet has been added to the
queue). The worst that can happen is a slight, transient,
underestimate of the queue size which might cause a drop to be
briefly deferred.
6. Further Experimentation
We encourage experimentation with the recommended values of TARGET
and INTERVAL for Internet settings. CoDel provides general,
efficient, parameterless building blocks for queue management that
can be applied to single or multiple queues in a variety of data
networking scenarios. CoDel's settings may be modified for other
special-purpose networking applications.
7. Security Considerations
This document describes an active queue management algorithm for
implementation in networked devices. There are no known security
exposures associated with CoDel at this time.
8. IANA Considerations
This document does not require actions by IANA.
9. Acknowledgments
The authors thank Jim Gettys for the constructive nagging that made
us get the work "out there" before we thought it was ready. We thank
Dave Taht, Eric Dumazet, and the open source community for showing
the value of getting it "out there" and for making it real. We thank
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Nandita Dukkipati for contributions to section 6 and for comments
which helped to substantially improve this draft. We thank the AQM
working group and the Transport Area shepherd, Wes Eddy, for
patiently prodding this draft all the way to a standard.
10. References
10.1. Normative References
[RFC2119] Bradner, S., "Key Words for use in RFCs to Indicate
Requirement Levels", March 1997.
10.2. Informative References
[BB2011] Gettys, J. and K. Nichols, "Bufferbloat: Dark Buffers in
the Internet", Communications of the ACM 9(11) pp. 57-65.
[BMPFQ] Suter, B., "Buffer Management Schemes for Supporting TCP
in Gigabit Routers with Per-flow Queueing", IEEE Journal
on Selected Areas in Communications Vol. 17 Issue 6, June,
1999, pp. 1159-1169.
[CHARB2007]
Dischinger, M., "Characterizing Residential Broadband
Networks", Proceedings of the Internet Measurement
Conference San Diego, CA, 2007.
[CODEL2012]
Nichols, K. and V. Jacobson, "Controlling Queue Delay",
Communications of the ACM Vol. 55 No. 11, July, 2012, pp.
42-50.
[FQ-CODEL-ID]
Hoeiland-Joergensen, T., McKenney, P.,
dave.taht@gmail.com, d., Gettys, J., and E. Dumazet,
"FlowQueue-Codel", draft-ietf-aqm-fq-codel-06 (work in
progress), March 2017.
[KLEIN81] Kleinrock, L. and R. Gail, "An Invariant Property of
Computer Network Power", International Conference on
Communications June, 1981,
<http://www.lk.cs.ucla.edu/data/files/Gail/power.pdf>.
[MACTCP1997]
Mathis, M., Semke, J., and J. Mahdavi, "The Macroscopic
Behavior of the TCP Congestion Avoidance Algorithm", ACM
SIGCOMM Computer Communications Review Vol. 27 no. 1, Jan.
2007.
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[NETAL2010]
Kreibich, C., "Netalyzr: Illuminating the Edge Network",
Proceedings of the Internet Measurement
Conference Melbourne, Australia, 2010.
[REDL1998]
Nichols, K., Jacobson, V., and K. Poduri, "RED in a
Different Light", Tech report, September, 1999,
<http://www.cnaf.infn.it/~ferrari/papers/ispn/
red_light_9_30.pdf>.
[RFC2309] Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering,
S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G.,
Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
S., Wroclawski, J., and L. Zhang, "Recommendations on
Queue Management and Congestion Avoidance in the
Internet", RFC 2309, April 1998.
[RFC2581] Allman, M., Paxson, V., and W. Stevens, "TCP Congestion
Control", RFC 2581, April 1999.
[RFC896] Nagle, J., "Congestion control in IP/TCP internetworks",
RFC 896, January 1984.
[SFQ1990] McKenney, P., "Stochastic Fairness Queuing", Proceedings
of IEEE INFOCOMM 90 San Francisco, 1990.
[TSV2011] Gettys, J., "Bufferbloat: Dark Buffers in the Internet",
IETF 80 presentation to Transport Area Open Meeting,
March, 2011,
<http://www.ietf.org/proceedings/80/tsvarea.html>.
[TSV84] Jacobson, V., "CoDel talk at TSV meeting IETF 84",
<http://www.ietf.org/proceedings/84/slides/
slides-84-tsvarea-4.pdf>.
[VANQ2006]
Jacobson, V., "A Rant on Queues", talk at MIT Lincoln
Labs, Lexington, MA July, 2006,
<http://www.pollere.net/Pdfdocs/QrantJul06.pdf>.
10.3. URIs
[1] http://www.ietf.org/proceedings/84/slides/slides-84-tsvarea-4.pdf
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Appendix A. Applying CoDel in the datacenter
Nandita Dukkipati and her group at Google realized that the CoDel
building blocks could be applied to bufferbloat problems in
datacenter servers, not just to Internet routers. The Linux CoDel
queueing discipline (qdisc) was adapted in three ways to tackle this
bufferbloat problem.
1. The default CoDel action was modified to be a direct feedback
from qdisc to the TCP layer at dequeue. The direct feedback
simply reduces TCP's congestion window just as congestion control
would do in the event of drop. The scheme falls back to ECN
marking or packet drop if the TCP socket lock could not be
acquired at dequeue.
2. Being located in the server makes it possible to monitor the
actual RTT to use as CoDel's interval rather than making a "best
guess" of RTT. The CoDel interval is dynamically adjusted by
using the maximum TCP round-trip time (RTT) of those connections
sharing the same Qdisc/bucket. In particular, there is a history
entry of the maximum RTT experienced over the last second. As a
packet is dequeued, the RTT estimate is accessed from its TCP
socket. If the estimate is larger than the current CoDel
interval, the CoDel interval is immediately refreshed to the new
value. If the CoDel interval is not refreshed for over a second,
it is decreased it to the history entry and the process is
repeated. The use of the dynamic TCP RTT estimate lets interval
adapt to the actual maximum value currently seen and thus lets
the controller space its drop intervals appropriately.
3. Since the mathematics of computing the setpoint are invariant, a
target of 5% of the RTT or CoDel interval was used here.
Non-data packets were not dropped as these are typically small and
sometimes critical control packets. Being located on the server,
there is no concern with misbehaving users as there would be on the
public Internet.
In several data center workload benchmarks, which are typically
bursty, CoDel reduced the queueing latencies at the qdisc, and
thereby improved the mean and 99th-percentile latencies from several
tens of milliseconds to less than one millisecond. The minimum
tracking part of the CoDel framework proved useful in disambiguating
"good" queue versus "bad" queue, particularly helpful in controlling
qdisc buffers that are inherently bursty because of TCP Segmentation
Offload (TSO).
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Authors' Addresses
Kathleen Nichols
Pollere, Inc.
PO Box 370201
Montara, CA 94037
USA
Email: nichols@pollere.com
Van Jacobson
Google
Email: vanj@google.com
Andrew McGregor
Google
Email: andrewmcgr@google.com
Janardhan Iyengar
Google
Email: jri@google.com
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