INTERNET-DRAFT Expires Jan. 2000 INTERNET-DRAFT
Network Working Group Matt Mathis
INTERNET-DRAFT Pittsburgh Supercomputing Center
Expiration Date: Jan. 2000 Mark Allman
NASA Glenn
June, 1999
Empirical Bulk Transfer Capacity
< draft-ietf-ippm-btc-framework-01.txt >
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Abstract
Bulk Transport Capacity (BTC) is a measure of a network's ability to
transfer significant quantities of data with a single
congestion-aware transport connection (e.g., TCP). The intuitive
definition of BTC is the expected long term average data rate (bits
per second) of a single ideal TCP implementation over the path in
question. However, there are many congestion control algorithms
(and hence transport implementations) permitted by IETF standards.
This diversity in transport algorithms creates a difficulty for
standardizing BTC metrics because the allowed diversity is
sufficient to lead to situations where different implementations
will yield non-comparable measures -- and potentially fail the
formal tests for being a metric.
This document defines a framework for standardizing multiple BTC
metrics that parallel the permitted transport diversity. Two
approaches are used. First, each BTC metric must be much more
tightly specified than the typical IETF protocol. Pseudo-code or
reference implementations are expected to be the norm. Second, each
BTC methodology is expected to collect some ancillary metrics which
are potentially useful to support analytical models of BTC.
1. Introduction
Bulk Transport Capacity (BTC) is a measure of a network's ability to
transfer significant quantities of data with a single
congestion-aware transport connection (e.g., TCP). For many
applications the BTC of the underlying network dominates the overall
elapsed time for the application to run and thus dominates the
performance as perceived by a user. Examples of such applications
include FTP, and the world wide web when delivering large images or
documents.
The intuitive definition of BTC is the expected long term average
data rate (bits per second) of a single ideal TCP implementation
over the path in question.
Central to the notion of bulk transport capacity is the idea that
all transport protocols should have similar responses to congestion
in the Internet. Indeed the only form of equity significantly
deployed in the Internet today is that the vast majority of all
traffic is carried by TCP implementations sharing common congestion
control algorithms largely due to a shared developmental heritage.
[RFC2581] specifies the standard congestion control algorithms used
by these TCP implementations. Even though this document is a
(proposed) standard, it permits considerable latitude in
implementation. This latitude is by design, to encourage ongoing
evolution in congestion control algorithms.
This legal diversity in transport algorithms creates a difficulty
for standardizing BTC metrics because the allowed diversity is
sufficient to lead to situations where different implementations
will yield non-comparable measures -- and potentially fail the
formal tests for being a metric.
There is also evidence that most TCP implementations exhibit
non-linear performance over some portion of their operating region.
It is possible to construct simple simulation examples where
incremental improvements to a path (such as raising the link
data rate) results in lower overall TCP throughput [MathisIPPM1998?].
We beleive that such non-linearity reflects weakness in our current
understanding of congestion control and is present to some extent in
all TCP implementations and BTC metrics. Note that such
non-linearity (in either TCP or a BTC metric) is potentially
problematic in the market because investment in capacity might
actually reduce the preceived quality of the network. Ongoing
research in congestion dynamics has some hope of mitigating or
modeling the these non-linearities.
Furthermore related areas, including Integrated services[@@],
differentiated services[@@] and Internet traffic analysis[@@] are
all currently receiving significant attention from the research
community. It is likely that we will see new experimental
congestion control algorithms in the near future. In addition,
Explicit Congestion Notification (ECN) [RFC2481] is being tested for
Internet deployment. We do not yet know how any of these
developments might affect BTC metrics.
This document defines a framework for standardizing multiple BTC
metrics that parallel the permitted transport diversity. Two
approaches are used. First, each BTC metric must be much more
tightly specified than the typical IETF transport protocol.
Pseudo-code or reference implementations are expected to be the
norm. Second, each BTC methodology is expected to collect some
ancillary metrics which are potentially useful to support analytical
models of BTC. If a BTC methodology does not collect these
ancillary metrics, it should collect enough information such that
these metrics can be derived (for instance a segment trace file).
For example, the models in [PFTK98, MSMO97, OKM96a, Lak94] all
predict bulk transfer performance based on path properties such as
loss rate and round trip time. A BTC methodology that also provides
ancillary measures of these properties is stronger because agreement
with the analytical models can be used to corroborate the direct BTC
measurement results.
More importantly the ancillary metrics are expected to be useful for
resolving disparity between different BTC methodologies. For
example, a path that predominantly experiences clustered packet
losses is likely to exhibit vastly different measures from BTC
metrics that mimic Tahoe, Reno, NewReno, and SACK TCP algorithms
[FF96]. The differences in the BTC metrics over such a path might
be diagnosed by an ancillary measure of loss clustering.
There are some path properties which are best measured as ancillary
metrics to a transport protocol. Examples of such properties
include bottleneck queue limits or the tendency to reorder packets.
These are difficult or impossible to measure at low rates and unsafe
to measure at rates higher than the bulk transport capacity of the
path.
It is expected that at some point in the future there will exist an
A-frame [RFC2330] which will unify all simple path metrics (e.g.,
segment loss rates, round trip time) and BTC ancillary metrics
(e.g., queue size and packet reordering) with different versions of
BTC metrics (e.g., that parallel Reno or SACK TCP).
2. Congestion Control Algorithms
Nearly all TCP implementations in use today utilize the congestion
control algorithms published in [Jac88] and further refined in
[RFC2581]. In addition to the basic notion of using an ACK clock,
TCP (and therefore BTC) implements five standard congestion control
algorithms: Congestion Avoidance, Retransmission timeouts,
Slow-start, Fast Retransmit and Fast Recovery. All BTC
implementations must use these algorithms as they are defined in
[RFC2581] (which the reader is assumed to be familiar with).
However, in all cases a BTC metric must more tightly specify these
algorithms, as discussed below.
2.1 Congestion Avoidance
The Congestion Avoidance algorithm drives the steady-state bulk
transfer behavior of TCP. It calls for opening the congestion
window (cwnd) by a constant additive amount during each round trip
time (RTT), and closing cwnd by a constant multiplicative fraction
on congestion, as indicated by lost segments or Explicit Congestion
Notification messages [RFC2481]. The window closes by half the
number of outstanding data segments in flight when loss is detected.
A BTC metric must specify the following Congestion Avoidance
details:
The exact algorithm for incrementing cwnd in TCP is left to the
implementer. Several candidate algorithms are outlined in
[RFC2581]. In addition, some of these algorithms include some
rounding. For these reasons, the exact algorithm for increasing
cwnd during congestion avoidance must be fully specified for
each BTC metric defined.
[RFC2581] permits, but does not require, an extra plus one
segment cwnd adjustment following the multiplicative decrease of
cwnd. This is because [RFC2581] allows a single invocation of
the Slow-Start algorithm when when cwnd equals ssthresh at the
end of recovery.
2.2 Retransmission Timeouts
In order to provide reliable data delivery, TCP resends a segment if
the ACK for the given segment does not arrive before the
retransmission timer (RTO) expires. A BTC metric must implement an
RTO timer to trigger retransmissions not handled by the fast
retransmit algorithm. Such retransmissions can have a large impact
on the measured BTC of the path. Calculating the RTO is subject to
a number of details that are not standardized (however, [WS95]
outlines a popular implementation). When implementing a BTC metric
the details of the RTO calculation, how and when the clock is set,
as well as the clock granularity must be fully documented.
2.3 Slow Start
Slow start is part of TCP's transient behavior. It is used to
quickly increase the congestion window for new or recently restarted
connections up to an appropriate level for the network path. In
addition, slow start is used to restart the ACK clock after a
retransmission timeout. A BTC implementation must use the slow
start algorithm, as specified by [RFC2581]. The slow start
algorithm is used while the congestion window (cwnd) is less than
the slow start threshold (ssthresh). However, whether to use slow
start or congestion avoidance when cwnd equals ssthresh is left to
the implementer by [RFC2581]. This detail must be specified in
every specific BTC metric definition.
2.4 Fast Retransmit/Fast Recovery
The Fast Retransmit/Fast Recovery algorithms are used to infer
segment loss before the RTO expires. A BTC implementation must
implement the algorithms as defined in [RFC2581].
In Reno TCP, Fast Retransmit and Fast Recovery are used to support
the Congestion Avoidance algorithm during loss recovery. During
Fast Recovery, the data receiver sends duplicated acknowledgments,
per the TCP specification [RFC793]. The data sender uses these
duplicate ACKs to detect loss, to estimate the quantity of
outstanding data in the network and to clock out new data in an
effort to keep the ACK clock running.
The Fast Retransmit/Fast Recovery algorithms should be implemented
in all BTC methodologies as specified in [RFC2581].
2.5 Advanced Recovery Algorithms
It has been observed that under some conditions the Fast Retransmit
and Fast Recovery algorithms do not reliably preserve TCP's
Self-Clock, causing unpredictable or unstable TCP performance
[Lak94@@@check, Flo95]. Simulations of reference TCP
implementations have uncovered situations where incidental changes
in the network path have a large effect on performance [MM96a].
Additional simulations have shown that under some conditions,
slightly better networks (higher bandwidth, lower delay or less
competing traffic) yield lower throughput [MathisIPPMDec1998?].
[RFC2581] allows a TCP implementation to use more robust loss
recovery algorithms, such as NewReno [RFC2582,FF96,Hoe96] and
SACK-based algorithms [FF96,MM96a,MM96b]. While allowing these
algorithms, [RFC2581] does not define any such algorithm and
therefore, a BTC metric that implements advanced loss recovery
algorithms must fully specify the details.
2.6 Segment Size
The actual segment size, or method of choosing a segment size (e.g.,
path MTU discovery [RFC1191]) and the number of header bytes assumed
to be prepended to each segment must be specified. In addition if
the segment size is artificially limited to less than the path MTU
this must be indicated (if known).
3 Ancillary Metrics
The following ancillary metrics can provide additional information
about the network and the behavior of the implemented congestion
control algorithm in response to the behavior of the network path.
It is recommended that these metrics be built into each BTC
methodology. Alternatively, the BTC implementation should provide
enough information such that the ancillary metrics can be derived
via post-processing (e.g., by providing a segment trace of the
connection).
3.1 Congestion Avoidance Capacity
The "Congestion Avoidance Capacity" (CAC) metric is the data rate
(bits per second) of a fully specified implementation of the
Congestion Avoidance algorithm, subject to the restriction that the
Retransmission Timeout and Slow-Start algorithms are not invoked.
The CAC metric is defined to have no meaning across Retransmission
Timeouts or Slow-Start periods (except the single segment Slow-Start
that is permitted to follow recovery, as discussed in section 2.3).
In principle a CAC metric would be an ideal BTC metric, as it
captures what should be TCP's steady state behavior. But, there is
a rather substantial difficulty with using it as such. The
Self-Clocking of the Congestion Avoidance algorithm can be very
fragile, depending on the specific details of the Fast Retransmit,
Fast Recovery or advanced recovery algorithms above. It has been
found that timeouts and periods of slow start loss recovery are
prevalent in traffic on the Internet [LK98] and therefore these
should be included in the BTC metric.
When TCP looses Self-Clock it is reestablished through a
retransmission timeout and Slow-Start. These algorithms nearly
always require more time than Congestion Avoidance would have taken.
It is easily observed that unless the network loses an entire window
of data (which would clearly require a retransmit timeout) TCP
missed some opportunity to safely transmit data. That is, if TCP
experiences a timeout after losing a partial window of data, it must
have received at least one ACK that was generated after some of the
partial data was delivered, but did not trigger the transmission of
new data. Recent research in congestion control (e.g., FACK
[MM96a], NewReno [FF96,RFC2582]) can be characterized as making
TCP's Self-Clock more tenacious, while preserving fairness under
adverse conditions. This work is often motivated by how poorly
current TCP implementations perform under some conditions, often due
to repeated clock loss. Since this is an active research area,
different TCP implementations have rather considerable differences
in their ability to preserve Self-Clock.
3.2 Preservation of Self-Clock
Losing the ACK clock can have a large effect on the overall BTC, and
the clock is itself fragile in ways that are dependent on the loss
recovery algorithm. Therefore, it is important that the transition
between timer driven and Self-Clocked operation be instrumented.
3.2.1 Lost Transmission Opportunities
If the last event before a timeout was the receipt of an ACK that
did not trigger a retransmission, the possibility exists that an
alternate congestion control algorithm would have successfully
preserved the Self-Clock. In this event, instrumenting key parts of
the BTC state (such as the congestion window) may lead to further
improvements in congestion control algorithms.
Note that in the absence of knowledge about the future, it is not
possible to design an algorithm that never misses transmission
opportunities. However, there are ever more subtle ways to gauge
network state, and to estimate if a given ACK is likely to be the
last.
3.2.2 Loosing an Entire Window
If an entire window of data (or ACKs) is lost, there will be no
returning ACKs to clock out additional data. This condition can
be detected if the last event before a timeout was a data
transmission triggered by an ACK. The loss of an entire window
of data/ACKs forces recovery to be via a Retransmission Timeout and
Slow-Start.
Losing an entire window of data implies an outage with a duration
at least as long as a round trip time. Such an outage can not be
diagnosed with low rate metrics and is unsafe to diagnose at
higher rates than the BTC. Therefore all BTC metrics at should
instrument and report losses of an entire window of data.
Note that there are some conditions, such as when operating with a
very small window, in which there is a significant probability that
an entire window can be lost through individual random losses.
3.2.3 Heroic Clock Preservation
All algorithms that permit a given BTC to sustain Self-Clock when
other algorithms might not, should be instrumented. Furthermore,
the details of the algorithms used must be fully documented.
BTC metrics that can sustain Self-Clock in the presence of multiple
losses within one round trip should instrument the loss
distribution, such that the performance of Reno style bulk transport
can be estimated.
3.2.4 False Timeouts
All false timeouts, (where the retransmission timer expires before
the ACK for some previously transmitted data arrives) should be
instrumented when possible. Note that depending upon how the BTC
metric implements sequence numbers, this may be difficult to detect.
3.3 Ancillary Metrics Relating to Flow Based Path Properties
All BTC metrics provide unique vantage points for instrumenting
certain path properties relating to closely spaced packets. As in
the case of RTT duration outages, these can be impossible to
diagnose at low rates (less than 1 packet per RTT) and inappropriate
to test at rates above the BTC.
All BTC metrics should instrument packet reordering. The frequency
and distance out of sequence must be instrumented for all
out-of-order packets. The severity of the reordering can be
classified as one of three different cases, each of which should be
reported.
Packets that are only slightly out of order should not trigger
retransmission (via fast retransmit), but they may affect the
window calculation. BTC metrics must document how slightly
out-of-order packets affect the congestion window calculation.
If packets are sufficiently out-of-order, the Fast Retransmit
algorithm will be invoked in advance of the delayed packet's
late arrival. These events must be instrumented. Even though
the the late arriving packet will complete recovery, the the
window will still be reduced by half.
Under some rare conditions packets have been observed that are
far out of order - sometimes many seconds late [Pax97b]. These
should always be instrumented.
The BTC should instrument the maximum cwnd observed during
congestion avoidance and slow start. A TCP running over the same
path as the BTC must have sufficient sender buffer space and
receiver window (and window shift [RFC1323]) to cover this cwnd.
There are several other path properties that one might measure
within a BTC metric. For example, with an embedded one-way delay
metric it may be possible to measure how queueing delay and and
(RED) drop probabilities are correlated to window size. These are
open research questions.
3.4 Ancillary Metrics Pertaining to MTU Discovery
Under some conditions, BTC can be very sensitive to segment size.
In addition to instrumenting the segment size, a BTC metric should
indicate how it was selected: by path MTU discovery [RFC1191], a
manual configuration, system default, or the maximum MTU for the
interface.
Note that the most popular LAN technologies have smaller MTUs than
nearly all WAN technologies. As a consequence, it is difficult to
measure the true performance of a wide area path without subjecting
it to the smaller MTU of the LAN.
3.4 Ancillary Metrics as Calibration Checks
Unlike low rate metrics, BTC must have explicit checks that the
test platform is not the bottleneck.
Ideally all queues within the tester should be instrumented. All
packets dropped within the tester should be instrumented as tester
failures, invalidating a measurement.
The maximum queue lengths should be instrumented. Any significant
queue may indicate that the tester itself has insufficient burst
data rate, and is slightly smoothing the data into the network.
3.4.3 Validate Reverse path load
@@@@ What happens to a BTC when the reverse path is congested? Is
this identical to TCP? What should happen? How should it be
instrumented?
#
# Some implementations (mine!) have an annoying feature whereby ACK loss
# looks just like data loss. This should be documented. If ACK loss
# and data loss can be detected separately, I think ACK loss rate should
# be reported, as it slightly changes the ACK clock (can impact
# algorithms like slow start that work on a per ACK basis and can make
# the sender more bursty, which could cause more loss).
@ and mine --MM--
3.5 Ancillary Metrics Relating to the Need for Advanced TCP Features
If TCP would require advanced TCP extensions to match BTC
performance (such as RFC 1323 or RFC 2018 features), it should be
reported.
4 Acknowledgments
Thanks to Jeff Semke for numerous clarifications.
5 References
[FF96] Fall, K., Floyd, S.. "Simulation-based Comparisons of Tahoe,
Reno and SACK TCP". Computer Communication Review, July 1996.
ftp://ftp.ee.lbl.gov/papers/sacks.ps.Z.
[Flo95] Floyd, S., "TCP and successive fast retransmits", March
1995, Obtain via ftp://ftp.ee.lbl.gov/papers/fastretrans.ps.
[Hoe96] Hoe, J., "Improving the start-up behavior of a congestion
control scheme for TCP, Proceedings of ACM SIGCOMM '96, August
1996.
[Hoe95] Hoe, J., "Startup dynamics of TCP's congestion control and
avoidance schemes". Master's thesis, Massachusetts Institute of
Technology, June 1995.
[Jac88] Jacobson, V., "Congestion Avoidance and Control",
Proceedings of SIGCOMM '88, Stanford, CA., August 1988.
[Lak94] Lakshman, Effects of random loss
[LK98] Lin, D. and Kung, H.T., "TCP Fast Recovery Strategies:
Analysis and Improvements", Proceedings of InfoCom, March 1998.
[MM96a] Mathis, M. and Mahdavi, J. "Forward acknowledgment: Refining
TCP congestion control", Proceedings of ACM SIGCOMM '96,
Stanford, CA., August 1996.
[MM96b] M. Mathis, J. Mahdavi, "TCP Rate-Halving with Bounding
Parameters" Available from
http://www.psc.edu/networking/papers/FACKnotes/current.
[MSMO97] Mathis, M., Semke, J., Mahdavi, J., Ott, T., "The
Macroscopic Behavior of the TCP Congestion Avoidance Algorithm",
Computer Communications Review, 27(3), July 1997.
[OKM96a], Ott, T., Kemperman, J., Mathis, M., "The Stationary
Behavior of Ideal TCP Congestion Avoidance", In progress, August
1996. Obtain via pub/tjo/TCPwindow.ps using anonymous ftp to
ftp.bellcore.com
[OKM96b], Ott, T., Kemperman, J., Mathis, M., "Window Size Behavior
in TCP/IP with Constant Loss Probability", DIMACS Special Year
on Networks, Workshop on Performance of Real-Time Applications
on the Internet, Nov 1996.
[Pax97a] Paxson, V., "Automated Packet Trace Analysis of TCP
Implementations", Proceedings of ACM SIGCOMM '97, August 1997.
[Pax97b] Paxson, V., "End-to-End Internet Packet Dynamics,"
Proceedings of SIGCOMM '97, Cannes, France, Sep. 1997.
[PFTK98] Padhye, J., Firoiu. V., Towsley, D., and Kurose, J., "TCP
Throughput: A Simple Model and its Empirical Validation",
Proceedings of ACM SIGCOMM '98, August 1998.
[RFC793] Postel, J., "Transmission Control Protocol", 1981, Obtain
via: ftp://ds.internic.net/rfc/rfc793.txt
[RFC1191] Mogul, J., Deering, S., "Path MTU Discovery", November
1990, Obtain via: ftp://ds.internic.net/rfc/rfc1191.txt
[RFC1323] Jacobson, V., Braden, R., Borman, D., "TCP Extensions for
High Performance", May 1992, Obtain via:
ftp://ds.internic.net/rfc/rfc1323.txt
[RFC2001] Stevens, W., "TCP Slow Start, Congestion Avoidance, Fast
Retransmit, and Fast Recovery Algorithms", 1997, Obtain via:
ftp://ds.internic.net/rfc/rfc2001.txt
[RFC2018] Mathis, M., Mahdavi, J. Floyd, S., Romanow, A., "TCP
Selective Acknowledgment Options", 1996, Obtain via:
ftp://ds.internic.net/rfc/rfc2018.txt
[RFC2330] Paxson, V., Almes, G., Mahdavi, J., Mathis, M., "Framework
for IP Performance Metrics" , 1998, Obtain via:
ftp://ds.internic.net/rfc/rfc2330.txt
[RFC2481] K. Ramakrishnan, S. Floyd, "A Proposal to add Explicit
Congestion Notification (ECN) to IP", 1999, Obtain via:
ftp://ds.internic.net/rfc/rfc2481.txt
[RFC2525] V. Paxson, M. Allman, S. Dawson, W. Fenner, J. Griner,
I. Heavens, K. Lahey, J. Semke, B. Volz, "Known TCP
Implementation Problems", 1999, Obtain via:
ftp://ds.internic.net/rfc/rfc2525.txt
[RFC2581] Allman, M., Paxson, V., Stevens, W., "TCP Congestion
Control"., 1999, Obtain via:
ftp://ds.internic.net/rfc/rfc2581.txt
[RFC2582] Floyd, S., Henderson, T., "The NewReno Modification to
TCP's Fast Recovery Algorithm", 1999, Obtain via:
ftp://ds.internic.net/rfc/rfc2582.txt
[Ste94] Stevens, W., "TCP/IP Illustrated, Volume 1: The Protocols",
Addison-Wesley, 1994.
[WS95] Wright, G., Stevens, W., "TCP/IP Illustrated Volume II: The
Implementation", Addison-Wesley, 1995.
Author's Addresses
Matt Mathis
Pittsburgh Supercomputing Center
4400 Fifth Ave.
Pittsburgh PA 15213
mathis@psc.edu
http://www.psc.edu/~mathis
Mark Allman
NASA Glenn Research Center/GTE Internetworking
Lewis Field
21000 Brookpark Rd. MS 54-2
Cleveland, OH 44135
216-433-6586
mallman@grc.nasa.gov
http://roland.grc.nasa.gov/~mallman
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