Dynamic Congestion Avoidance / Control (long message)

Van Jacobson (van@lbl-csam.arpa)
Thu, 11 Feb 88 22:17:04 PST

A dozen people forwarded Phil Karn's question about TCP
congestion control to me, usually with pithy subject lines like
"how much longer till you publish something?". I do have three
RFCs and two papers in various stages of preparation, but innate
laziness combined with this semester's unexpectedly large
teaching load means it will probably be late spring before
anything gets finished. In the interim, here's a sort-of
overview of our congestion control work.

I should point out these algorithms are joint work with Mike
Karels of UC Berkeley (I guess my name got stuck on things
because I make the presentations while Mike is off fighting the
battles on EGP or routing or domains or ...). I should also
mention that there's not a whole lot that's original. In
particular, the two most important algorithms are quite close to
(prior) work done by DEC's Raj Jain. This was by accident in
one case and deliberate in the other.

This stuff has been discussed on the ietf and end2end lists
(Phil participated in some of those discussions and was, in
fact, one of the early beta testers for our new tcp -- I have
this nagging suspicion I was set up). I've appended a couple of
those mail messages.

Mike and I have put a number (six, actually) of new algorithms
into the 4bsd tcp. Our own measurements and the reports of our
beta testers suggest that the result is quite good at dealing
with current, congested conditions on the Internet. The various
algorithms were developed and presented at different times over
the past year and it may not be clear to anyone but the
developers how, or if, the pieces relate.

Most of the changes spring from one observation: The flow on a
TCP connection (or ISO TP-4 or XNS SPP connection) should, by
the design of the protocol, obey a `conservation of packets'
principle. And, if this principle were obeyed, congestion
collapse would become the exception rather than the rule.
Thus congestion control turns into finding places where
conservation is violated and fixing them.

By `conservation of packets' I mean the following:
If you look at the connection "in equilibrium", i.e., running
stably with a full window of data in transit, you notice that
the flow is what a physicist would call "conservative": A new
packet isn't put into the network until an old packet leaves.
Two scientific disciplines that deal with flow, hydrodynamics
and thermodynamics, predict that systems with this conservation
property should be extremely robust in the face of congestion.
Observation of the Arpanet or NSFNet suggests that they were not
particularly robust in the face of congestion. From whence
comes the discrepancy?

[Someone asked me for a simple explanation of why a conservative
flow system should be stable under load. I've been trying to
come up with one, thus far without success -- absolutely nothing
in hydrodynamics admits to simple explanations. The shortest
explanation is that the inhomogenous forcing terms in the
Fokker-Planck equation vanish and the remaining terms are highly
damped. But I don't suppose this means much to anyone (it
doesn't mean much to me). My intuition is that conservation
means there's never an `energy difference' between the outside
world and the system that the system could use to `pump up' an
instability to a state of collapse. Thus the only mechanism the
system can use for self-destruction is to re-arrange its
internal energy and create a difference large enough to break
something. But entropy (or diffusion) always trys to erase
large internal energy differences so collapse via these
mechanisms is improbable. Possible, but improbable, at least
until the load gets so outrageous that collective, non-ergodic
effects start to dominate the overall behavior.]

Packet conservation can fail three ways:

  1) the connection doesn't get to equilibrium, or

  2) a sender injects a packet before an old packet has exited, or

  3) the equilibrium can't be reached because of resource
     limits along the path.

(1) has to be from a connection starting or restarting after a
packet loss. Another way to look at the conservation property
is to say that the sender uses acks as a "clock" to strobe new
packets into the network. Since the receiver can generate acks
no faster than data packets can get through the network, the
protocol is "self clocking" (an ack can't go in until a packet
comes out, a packet can't go in until an ack comes out). Self
clocking systems automatically adjust to bandwidth and delay
variations and have a wide dynamic range (an important issue
when you realize that TCP spans everything from 800 Mbit/sec
Cray channels to 1200 bit/sec packet radio links). But the same
thing that makes a self-clocked system stable when it's running
makes it hard to get started -- to get data flowing you want acks
to clock packets out but to get acks you have to have data flowing.

To get the `clock' started, we came up with an algorithm called
slow-start that gradually increases the amount of data flowing.
Although we flatter ourselves that the design of this algorithm
is quite subtle, the implementation is trivial -- 3 lines of
code and one new state variable in the sender:

    1) whenever you're starting or restarting after a loss,
       set your "congestion window" to 1 packet.
    2) when you get an ack for new data, increase the
       congestion window by one packet.
    3) when you send, send the minimum of the receiver's
       advertised window and the congestion window.

(This is quite similar to Raj Jain's CUTE algorithm described in
IEEE Transactions on Communications, Oct, '86, although we didn't
know about CUTE at the time we were developing slowstart).

Actually, the slow-start window increase isn't all that gradual:
The window opening takes time proportional to log2(W) where W is
the window size in packets. This opens the window fast enough
to have a negligible effect on performance, even on links that
require a very large window. And the algorithm guarantees that
a connection will source data at a rate at most twice the
maximum possible on the path. (Without slow-start, by contrast,
when 10Mb ethernet hosts talk over the 56Kb Arpanet via IP
gateways, the gateways see a window's worth of packets (8-16)
delivered at 200 times the path bandwidth.)

Once you can reliably start data flowing, problems (2) and (3)
have to be addressed. Assuming that the protocol implementation
is correct, (2) must represent a failure of sender's retransmit
timer. A good round trip time estimator (the core of the
retransmit timer) is the single most important feature of any
protocol implementation that expects to survive heavy load. And
it almost always gets botched.

One mistake seems to be not estimating the variance of the rtt.
>From queuing theory we know that rtt and rtt variation increase
very quickly with load. Measuring the load by rho (the ratio of
average arrival rate to average departure rate), the rtt goes up
like 1/(1-rho) and the variation in rtt goes like 1/(1-rho)^2.
To make this concrete, if the network is running at 75% of
capacity (as the Arpanet was in last April's collapse), you
should expect rtt to vary by a factor of 16 around its mean
value. The RFC793 parameter "beta" accounts for rtt variation.
The suggested value of "2" can adapt to loads of at most 30%.
Above this point, a connection will respond to load increases by
retransmitting packets that have only been delayed in transit.
This makes the network do useless work (wasting bandwidth on
duplicates of packets that have been or will be delivered) at a
time when it's known to be having trouble with useful work.
I.e., this is the network equivalent of pouring gasoline on a

We came up a cheap method for estimating variation (see first of
attached msgs) and the resulting retransmit timer essentially
eliminates spurious retransmissions. A pleasant side effect of
estimating "beta" rather than using a fixed value is that low
load as well as high load performance improves, particularly
over high delay paths such as satellite links.

Another timer mistake seems to be in the backoff after a
retransmit: If we have to retransmit a packet more than once,
how should the retransmits be spaced? It turns out there's only
one scheme that's likely to work, exponential backoff, but
proving this is a bit involved (one of the two papers alluded to
in to opening goes through a proof). We might finesse a proof
by noting that a network is, to a very good approximation, a
linear system. (That is, it is composed of elements that behave
like linear operators -- integrators, delays, gain stages,
etc.). Linear system theory says that if a system is stable,
the stability is exponential. This suggests that if we have a
system that is unstable (a network subject to random load shocks
and prone to congestive collapse), the way to stabilize it is to
add some exponential damping (read: exponential timer backoff)
to its primary excitation (read: senders, traffic sources).

Once the timers are in good shape, you can state with some
confidence that a timeout really means a lost packet and not a
busted timer. At this point you can do something about (3).
Packets can get lost for two reasons: they are damaged in
transit or the network is congested and somewhere on the path
there was insufficient buffer capacity. On most of the network
paths we use, loss due to damage is rare (<<1%) so it is very
probable that a packet loss is due to congestion in the network.

Say we try to develop a `congestion avoidance' strategy like the
one Raj Jain, et.al., propose in DEC TR506 and ISO 8473. It
must have two components: The network must be able to signal
the transport endpoints that congestion is occurring (or about
to occur). And the endpoints must have a policy that decreases
utilization if this signal is received and increases utilization
if the signal isn't received.

If packet loss is (almost) always due to congestion and if a
timeout is (almost) always due to a lost packet, we have a good
candidate for the `network is congested' signal. Particularly
since this signal is delivered automatically by all existing
networks, without special modification (as opposed to, say, ISO
8473 which requires a new bit in the packet headers and a mod to
*all* existing gateways to set this bit).

[As a (lengthy) aside:
Using `lost packets' to communicate information seems to bother
people. The second of the two papers mentioned above is devoted
to analytic and simulation investigations of our algorithm under
various network conditions. I'll briefly mention some of the
objections we've heard and, I think, addressed.

There have been claims that signaling congestion via dropping
packets will adversely affect total throughput (it's easily
proved that the opposite is true) or that this signal will be
`too slow' compared to alternatives (The fundamental frequency
of the system is set by its average round trip time. From
control theory and Nyquist's theorem, any control strategy that
attempts to respond in less than two round trip times will be
unstable. A packet loss is detected in at most two rtt, the
minimum stable response time).

There have also been claims that this scheme will result in
unfair or sub-optimal resource allocation (this is untrue if
equivalent implementations of ISO and our schemes are compared)
or that mistaking damage for congestion will unnecessarily
throttle the endpoints on some paths with a high intrinsic loss
rate (this is mostly untrue -- the scheme we propose is
analytically tractable so we've worked out its behavior under
random loss. It turns out that window flow control schemes just
don't handle high loss rates well and throughput of a vanilla
TCP under high, random loss is abysmal. Adding our congestion
control scheme makes things slightly worse but the practical
difference is negligible. As an example (that we have calculated
and Jon Crowcroft at UCL has measured), it takes 40 256-byte
packets to fill the Satnet pipe. Satnet currently shows a
random, 1% average packet loss. The maximum throughput a
vanilla tcp could achieve at this loss rate is 56% of the 40kbs
channel bandwidth. The maximum throughput our TCP can achieve at
this loss rate is 49% of the channel bandwidth.

In theory, the use of dynamic congestion control should allow
one to achieve much better throughput under high loss than is
possible with normal TCP -- performance comparable to, say,
NETBLT over the same lossy link. The reason is that regular TCP
tries to communicate two different things with one number (the
window field in the packet): the amount of buffer the receiver
has available and the delay-bandwidth product of the pipe. Our
congestion control scheme separates these two numbers. The
sender estimates the pipe size so the receiver window need only
describe the receiver's buffer size. As long as the receiver
advertises a sufficiently large buffer, (twice the delay-
bandwidth product) a 1% loss rate would translate into a 1%
throughput effect, not a factor-of-two effect. Of course, we
have yet to put this theory to test.]

The other part of a congestion avoidance strategy, the endnode
action, is almost identical in Jain's DEC/ISO scheme and our
TCP. This is not an accident (we copied Jain's scheme after
hearing his presentation at the Boston IETF & realizing that the
scheme was, in a sense, universal): The endnode strategy
follows directly from a first-order time-series model of the
network. Say we measure network `load' by average queue length
over fixed intervals of some appropriate length (something near
the rtt). If L(i) is the load at interval i, a network not
subject to congestion can be modeled by saying L(i) changes
slowly compared to the sampling time. I.e.,

    L(i) = N

(the average queue length doesn't change over time). If the
network is subject to congestion, this zero'th order model
breaks down. The average queue length becomes the sum of two
terms, the N above that accounts for the average arrival rate
of new traffic and a new term that accounts for the left-over
traffic from the last time interval:

    L(i) = N + a L(i-1)

(As pragmatists, we extend the original model just enough to
explain the new behavior. What we're doing here is taking the
first two terms in a taylor series expansion of L(t) when we
find that just the first term is inadequate. There is reason to
believe one would eventually need a three term, second order
model, but not until the Internet has grown to several times its
current size.)

When the network is congested, the `a' term must be large and
the load (queues) will start increasing exponentially. The only
way to stabilize the system is if the traffic sources throttle
back at least as fast as the queues are growing. Since the way
a source controls load in a window-based protocol is to adjust
the size of the window, W, we end up with the sender policy

    On congestion: W(i) = d W(i-1) (d < 1)

I.e., a multiplicative decrease of the window size. (This will
turn into an exponential decrease over time if the congestion

If there's no congestion, the `a' term must be near zero and the
network load is about constant. You have to try to increase the
bandwidth you're using to find out the current limit (e.g., you
could have been sharing the path with someone else and converged
to a window that gives you each half the available bandwidth. If
he shuts down, 50% of the bandwidth will get wasted unless you
make some effort to increase your window size.) What should the
increase policy be?

The first thought is to use a symmetric, multiplicative increase,
possibly with a longer time constant. E.g., W(i) = b W(i-1),
1 < b <= 1/d. This is a mistake. The result will oscillate
wildly and, on the average, deliver poor throughput. The reason
why is tedious to explain. It has to do with that fact that it
is easy to drive the net into saturation but hard for the net
to recover (what Kleinrock, vol.2, calls the "rush-hour effect").
Thus overestimating the available bandwidth is costly. But an
exponential, almost regardless of its time constant, increases
so quickly that large overestimates are inevitable.

Without justification, I'll simply state that the best increase
policy is to make small, constant changes to the window size (if
you've had a control theory course, you've seen the justification):

    On no congestion: W(i) = W(i-1) + u (u << the path delay-
                                                bandwidth product)

I.e., an additive increase policy. This is exactly the policy that
Jain, et.al., suggest in DEC TR-506 and exactly the policy we've
implemented in TCP. The only difference in our implementations is
the choice of constants for `d' and `u'. We picked .5 and 1 for
reasons that are partially explained in the second of the attached
messages. A more complete analysis is in the second in-progress
paper (and may be discussed at the upcoming IETF meeting).

All of the preceding has probably made it sound as if the
dynamic congestion algorithm is hairy but it's not. Like
slow-start, it turns out to be three lines of code and one new
connection state variable (the combined slow-start/congestion
control algorithm is described in the second of the attached

That covers about everything that's been done to date. It's
about 1/3 of what we think clearly needs to be done. The next
big step is to do the gateway `congestion detection' algorithms
so that a signal is sent to the endnodes as early as possible
(but not so early that the gateway ends up starved for traffic).
The way we anticipate doing these algorithms, gateway `self
protection' from a mis-behaving host will fall-out for free (that
host will simply have most of its packets dropped as the gateway
trys to tell it that it's using more than its fair share). Thus,
like the endnode algorithm, the gateway algorithm will improve
things even if no endnode is modified to do dynamic congestion
avoidance. And nodes that do implement congestion avoidance
will get their fair share of bandwidth and a minimum number of
packet drops.

Since congestion grows exponentially, detecting it early is
important. (If it's detected early, small adjustments to the
senders' windows will cure it. Otherwise massive adjustments
will be necessary to give the net enough spare capacity to pump
out the backlog.) But, given the bursty nature of traffic,
reliable detection is a non-trivial problem. There is a scheme
proposed in DEC TR-506 but we think it might have convergence
problems under high load and/or significant second-order dynamics
in the traffic. We are thinking of using some of our earlier
work on ARMAX models for rtt/queue length prediction as the
basis of the detection. Preliminary results suggest that this
approach works well at high load, is immune to second-order
effects in the traffic and is cheap enough to compute that it
wouldn't slow down thousand-packet-per-second gateways.

In addition to the gateway algorithms, we want to apply the
endnode algorithms to connectionless traffic (e.g., domain
server queries, RPC requests). We have the rate-based
equivalent of the TCP window algorithm worked out (there should
be a message describing it on the tcp list sometime in the near
future). Sun Microsystems has been very interested, and
supportive, during the development of the TCP congestion control
(I believe Sun OS 4.0 will ship with our new TCP) and Sun has
offered to cooperate in developing the RPC dynamic congestion
control algorithms, using NFS as a test-bed (since NFS is known
to have congestion problems).

The far future holds some work on controlling second-order, non-
ergodic effects, adaptively determining the rate constants in
the control algorithm, and some other things that are too vague
to mention.

 - Van

Subject: Re: interpacket arrival variance and mean
In-reply-to: Your message of Mon, 08 Jun 87 12:31:33 EDT.
Date: Mon, 15 Jun 87 06:08:01 PDT
From: Van Jacobson <van@lbl-csam.arpa>

Mark -

I'm working on long paper about transport protocol timers (models,
estimation, implementations, common implementation mistakes, etc.).
This has me all primed on the subject so attached is a long-winded
explanation and example C code for estimating the mean and variance
of a series of measurements.

Though I know your application isn't tcp, I'm going to use a new
tcp retransmit timer algorithm as an example. The algorithm
illustrates the method and, based on 3 months of experiment, is
much better than the algorithm in rfc793 (though it isn't as good
as the algorithm I talked about at IETF and am currently debugging).

You're probably familiar with the rfc793 algorithm for estimating
the mean round trip time. This is the simplest example of a
class of estimators called "recursive prediction error" or
"stochastic gradient" algorithms. In the past 20 years these
algorithms have revolutionized estimation and control theory so
it's probably worth while to look at the rfc793 estimator in some

Given a new measurement, Meas, of the rtt, tcp updates an
estimate of the average rtt, Avg, by

        Avg <- (1 - g) Avg + g Meas

where g is a "gain" (0 < g < 1) that should be related to the
signal- to-noise ratio (or, equivalently, variance) of Meas.
This makes a bit more sense (and computes faster) if we rearrange
and collect terms multiplied by g to get

        Avg <- Avg + g (Meas - Avg)

We can think of "Avg" as a prediction of the next measurement.
So "Meas - Avg" is the error in that prediction and the
expression above says we make a new prediction based on the old
prediction plus some fraction of the prediction error. The
prediction error is the sum of two components: (1) error due to
"noise" in the measurement (i.e., random, unpredictable effects
like fluctuations in competing traffic) and (2) error due to a
bad choice of "Avg". Calling the random error RE and the
predictor error PE,

        Avg <- Avg + g RE + g PE

The "g PE" term gives Avg a kick in the right direction while the
"g RE" term gives it a kick in a random direction. Over a number
of samples, the random kicks cancel each other out so this
algorithm tends to converge to the correct average. But g
represents a compromise: We want a large g to get mileage out of
the PE term but a small g to minimize the damage from the RE
term. Since the PE terms move Avg toward the real average no
matter what value we use for g, it's almost always better to use
a gain that's too small rather than one that's too large.
Typical gain choices are .1 - .2 (though it's always a good
idea to take long look at your raw data before picking a gain).

It's probably obvious that Avg will oscillate randomly around
the true average and the standard deviation of Avg will be
something like g sdev(Meas). Also that Avg converges to the
true average exponentially with time constant 1/g. So
making g smaller gives a stabler Avg at the expense of taking
a much longer time to get to the true average.

[My paper makes the preceding hand-waving a bit more rigorous
but I assume no one cares about rigor. If you do care, check
out almost any text on digital filtering or estimation theory.]

If we want some measure of the variation in Meas, say to compute
a good value for the tcp retransmit timer, there are lots of
choices. Statisticians love variance because it has some nice
mathematical properties. But variance requires squaring (Meas -
Avg) so an estimator for it will contain two multiplies and a
large chance of integer overflow. Also, most of the applications
I can think of want variation in the same units as Avg and Meas,
so we'll be forced to take the square root of the variance to use
it (i.e., probably at least a divide, multiply and two adds).

A variation measure that's easy to compute, and has a nice,
intuitive justification, is the mean prediction error or mean
deviation. This is just the average of abs(Meas - Avg).
Intuitively, this is an estimate of how badly we've blown our
recent predictions (which seems like just the thing we'd want to
set up a retransmit timer). Statistically, standard deviation
(= sqrt variance) goes like sqrt(sum((Meas - Avg)^2)) while mean
deviation goes like sum(sqrt((Meas - Avg)^2)). Thus, by the
triangle inequality, mean deviation should be a more "conservative"
estimate of variation.

If you really want standard deviation for some obscure reason,
there's usually a simple relation between mdev and sdev. Eg.,
if the prediction errors are normally distributed, mdev =
sqrt(pi/2) sdev. For most common distributions the factor
to go from sdev to mdev is near one (sqrt(pi/2) ~= 1.25). Ie.,
mdev is a good approximation of sdev (and is much easier to

So let's do estimators for Avg and mean deviation, Mdev. Both
estimators compute means so we get two instances of the rfc793

        Err = abs (Meas - Avg)
        Avg <- Avg + g (Meas - Avg)
        Mdev <- Mdev + g (Err - Mdev)

If we want to do the above fast, it should probably be done in
integer arithmetic. But the expressions contain fractions (g < 1)
so we'll have to do some scaling to keep everything integer. If
we chose g so that 1/g is an integer, the scaling is easy. A
particularly good choice for g is a reciprocal power of 2
(ie., g = 1/2^n for some n). Multiplying through by 1/g gives

        2^n Avg <- 2^n Avg + (Meas - Avg)
        2^n Mdev <- 2^n Mdev + (Err - Mdev)

To minimize round-off error, we probably want to keep the scaled
versions of Avg and Mdev rather than the unscaled versions. If
we pick g = .125 = 1/8 (close to the .1 that rfc793 suggests) and
express all the above in C:

        Meas -= (Avg >> 3);
        Avg += Meas;
        if (Meas < 0)
                Meas = -Meas;
        Meas -= (Mdev >> 3);
        Mdev += Meas;

I've been using a variant of this to compute the retransmit timer
for my tcp. It's clearly faster than the two floating point
multiplies that 4.3bsd uses and gives you twice as much information.
Since the variation is estimated dynamically rather than using
the wired-in beta of rfc793, the retransmit performance is also
much better: faster retransmissions on low variance links and
fewer spurious retransmissions on high variance links.

It's not necessary to use the same gain for Avg and Mdev.
Empirically, it looks like a retransmit timer estimate works
better if there's more gain (bigger g) in the Mdev estimator.
This forces the timer to go up quickly in response to changes in
the rtt. (Although it may not be obvious, the absolute value in
the calculation of Mdev introduces an asymmetry in the timer:
Because Mdev has the same sign as an increase and the opposite
sign of a decrease, more gain in Mdev makes the timer go up fast
and come down slow, "automatically" giving the behavior Dave
Mills suggests in rfc889.)

Using a gain of .25 on the deviation and computing the retransmit
timer, rto, as Avg + 2 Mdev, my tcp timer code looks like:

        Meas -= (Avg >> 3);
        Avg += Meas;
        if (Meas < 0)
                Meas = -Meas;
        Meas -= (Mdev >> 2);
        Mdev += Meas;
        rto = (Avg >> 3) + (Mdev >> 1);

Hope this helps.

  - Van

To: jain%erlang.DEC@decwrl.dec.com (Raj Jain, LKG1-2/A19, DTN: 226-7642)
Cc: ietf@gateway.mitre.org
Subject: Re: Your congestion scheme
In-Reply-To: Your message of 03 Nov 87 12:51:00 GMT.
Date: Mon, 16 Nov 87 06:03:29 PST
From: Van Jacobson <van@lbl-csam.arpa>


Thanks for the note. I hope you'll excuse my taking the liberty
of cc'ing this reply to the ietf: At the last meeting there was a
great deal of interest in your congestion control scheme and our
adaptation of it.

> I am curious to know what values of increase amount and decrease
> factor did you use. In our previous study on congestion using
> timeout (CUTE scheme, IEEE JSAC, Oct 1986), we had found that the
> decrease factor should be small since packet losses are
> expensive. In fact, we found that a decrease factor of zero
> (decreasing to one) was the best.

We use .5 for the decrease factor and 1 for the increase factor.
We also use something very close to CUTE (Mike Karels and I were
behind on our journal reading so we independently rediscovered
the algorithm and called it slow-start). Since we use a lost
packet as the "congestion experienced" indicator, the CUTE
algorithm and the congestion avoidance algorithm (BTW, have you
picked a name for it yet?) get run together, even though they are
logically distinct.

The sender keeps two state variables for congestion control: a
congestion window, cwnd, and a threshhold size, ssthresh, to
switch between the two algorithms. The sender's output routine
sends the minimum of cwnd and the window advertised by the
receiver. The rest of the congestion control sender code looks
like: On a timeout, record half the current window size as
"ssthresh" (this is the multiplicative decrease part of the
congestion avoidance algorithm), then set the congestion window
to 1 packet and call the output routine (this initiates
slowstart/CUTE). When new data is acked, the sender does
something like

        if (cwnd < ssthresh) // if we're still doing slowstart
                cwnd += 1 packet // open the window exponentially
                cwnd += 1/cwnd // otherwise do the Congestion
                                  // Avoidance increment-by-1

Notice that our variation of CUTE opens the window exponentially
in time, as opposed to the linear scheme in your JSAC article.
We looked at a linear scheme but were concerned about the
performance hit on links with a large bandwidth-delay product
(ie., satellite links). An exponential builds up fast enough to
accomodate any bandwidth-delay and our testing showed no more
packet drops with exponential opening that with linear opening.
(My model of what slowstart is doing -- starting an ack "clock"
to meter out packets -- suggested that there should be no
loss-rate difference between the two policies).

The reason for the 1/2 decrease, as opposed to the 7/8 you use,
was the following hand-waving: When a packet is dropped, you're
either starting (or restarting after a drop) or steady-state
sending. If you're starting, you know that half the current
window size "worked", ie., that a window's worth of packets were
exchanged with no drops (slowstart guarantees this). Thus on
congestion you set the window to the largest size that you know
works then slowly increase the size. If the connection is
steady-state running and a packet is dropped, it's probably
because a new connection started up and took some of your
bandwidth. We usually run our nets with rho <= .5 so it's
probable that there are now exactly two conversations sharing the
bandwidth. Ie., that you should reduce your window by half
because the bandwidth available to you has been reduced to half.
And, if there are more than two conversations sharing the
bandwidth, halving your window is conservative -- and being
conservative at high traffic intensities is probably wise.

Obviously, this large decrease term is accounting for the high
cost of our "congestion experienced" indicator compared to yours --
a dropped packet vs. a bit in the ack. If the DEC bit were
available, the preceding "logic" should be ignored. But I wanted
tcp congestion avoidance that we could deploy immediately and
incrementally, without adding code to the hundreds of Internet
gateways. So using dropped packets seemed like the only choice.
And, in system terms, the cost isn't that high: Currently packets
are dropped only when a large queue has formed. If we had a bit
to force senders to reduce their windows, we'd still be stuck
with the queue since we'd still be running the bottleneck at 100%
utilization so there's no excess bandwidth available to dissipate
the queue. If we toss a packet, a sender will shut up for 2 rtt,
exactly the time we need to empty the queue (in the ususal case).
If that sender restarts with the correct window size the queue
won't reform. Thus we've reduced the delay to minimum without
the system losing any bottleneck bandwidth.

The 1-packet increase has less justification that the .5
decrease. In fact, it's almost certainly too large. If the
algorithm converges to a window size of w, you get O(w^2) packets
between drops with an additive increase policy. We were shooting
for an average drop rate of <1% and found that on the Arpanet
(the worst case of the 4 networks we tested), windows converged
to 8 - 12 packets. This yields 1 packet increments for a 1%
average drop rate.

But, since we've done nothing in the gateways, the window we
converge to is the maximum the gateway can accept without dropping
packets. I.e., in the terms you used, we are just to the left of
the cliff rather than just to the right of the knee. If we
now fix the gateways so they start dropping packets when the
queue gets pushed past the knee, our increment will be much too
agressive and we'll have to drop it by at least a factor of 4
(since all my measurements on an unloaded Arpanet or Milnet
place their "pipe size" at 4-5 packets). Mike and I have talked
about a second order control loop to adaptively determine the
appropriate increment to use for a path (there doesn't seem to
be much need to adjust the decrement). It looks trivial to
implement such a loop (since changing the increment affects
the frequency of oscillation but not the mean window size,
the loop would affect rate of convergence but not convergence
and (first-order) stability). But we think 2nd order stuff
should wait until we've spent some time on the 1st order part
of the algorithm for the gateways.

I'm tired and probably no longer making sense. I think I'll
head home and get some sleep. Hope to hear from you soon.


 - Van

This archive was generated by hypermail 2.0b3 on Thu Mar 09 2000 - 14:40:42 GMT