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new psamp framework draft
Folks,
A new version of the psamp framework draft is available (also attached)
http://www.ietf.org/internet-drafts/draft-ietf-psamp-framework-02.txt
Thanks to all the people who commented on the previous version.
Here are the main additions/changes:
Section 4.10: selection operations, categorized as MUST, SHOULD and MAY
Section 5.1: reporting, categorized as MUST and SHOULD
Section 2: architecture: now you can have more than one reporting
process feeding an export process.
The choice of selection operations area is also covered by the sampling
document, but without full agreement between the documents. It would be
great for folks to read both documents and comment before or at the WG
meeting, so we can get this part agreed ASAP.
Nick
INTERNET-DRAFT Nick Duffield
draft-ietf-psamp-framework-02.txt Albert Greenberg
March 2003 Matthias Grossglauser
Expires: September 2003 Jennifer Rexford
AT&T Labs - Research
Derek Chiou
Avici Systems
Benoit Claise
Peram Marimuthu
Ganesh Sadasivan
Cisco Systems
A Framework for Passive Packet Measurement
Copyright (C) The Internet Society (2003). All Rights Reserved.
This document is an Internet-Draft and is in full conformance with
all provisions of Section 10 of RFC2026.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF), its areas, and its working groups. Note that
other groups may also distribute working documents as Internet-
Drafts.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
The list of current Internet-Drafts can be accessed at
http://www.ietf.org/ietf/1id-abstracts.txt
The list of Internet-Draft Shadow Directories can be accessed at
http://www.ietf.org/shadow.html.
Abstract
A wide range of traffic engineering and troubleshooting tasks rely
on timely and detailed traffic measurements that can be
consistently interpreted. We describe a framework for passive
packet measurement that is (a) general enough to serve as the basis
for a wide range of operational tasks, and (b) needs only a small
set of packet selection operations that facilitate ubiquitous
deployment in router interfaces or dedicated measurement devices,
even at very high speeds.
Comments on this document should be addressed to the PSAMP WG
mailing list: psamp@ops.ietf.org
To subscribe: psamp-request@ops.ietf.org, in body: subscribe
Archive: https://ops.ietf.org/lists/psamp/
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0 Contents
1 Motivation ................................................. 3
2 Elements, Terminology, and Architecture .................... 4
3 Requirements ............................................... 6
3.1 Selection Process Requirements ......................... 6
3.2 Reporting Process Requirements ......................... 7
3.3 Export Process Requirements ............................ 7
3.4 Configuration Requirements ............................. 7
4 Packet Selection ............................................ 8
4.1 Filtering .............................................. 8
4.2 Systematic Sampling .................................... 8
4.3 Random Sampling ........................................ 8
4.3.1 Uniform Random Sampling ............................ 8
4.3.2 Stratified Random Sampling ......................... 9
4.3.3 Non-uniform Independent Random Sampling ............ 9
4.4 Hash-based Selection ................................... 9
4.3.1 Consistent Flow Sampling ........................... 10
4.3.2 Trajectory Sampling ................................ 10
4.5 Generation of Pseudorandom Variates .................... 11
4.6 Criteria for Choice of Selection Operations ............ 11
4.6.1 Evaluating the Need for Distinct Selection Operations 11
4.6.2 Comparison of Uniform Sampling Methods ............. 12
4.7 Constraints on the Sampling Rate ....................... 12
4.8 Selection According to Packet Treatment ................ 12
4.9 Input Sequence Numbers for Primitive Selection Operations 12
4.10 Selection Operations and Application Requirements ..... 13
4.10.1 Mandatory Selection Operations .................... 13
4.10.2 Recommended Selection Operations .................. 13
4.10.3 Optional Selection Operations ..................... 14
5 Reporting .................................................. 14
5.1 Mandatory Reporting .................................... 14
5.2 Recommended Reporting .................................. 15
5.3 Report Interpretation ................................. 15
6 Export and Congestion Avoidance ............................ 16
6.1 Collector Destination .................................. 16
6.2 Local Export ........................................... 16
6.3 Reliable vs. Unreliable Transport ...................... 16
6.4 Limiting Delay in Exporting Measurement Packets ........ 17
6.5 Configurable Export Rate Limit ......................... 17
6.6 Congestion-aware Unreliable Transport .................. 17
6.7 Collector-based Rate Reconfiguration ................... 18
6.7.1 Changing the Export Rate and Other Rates ........... 18
6.7.2 Notions of Fairness ................................ 18
6.7.3 Behavior Under Overload and Failure ................ 19
7 Parallel Measurement Processes ............................. 19
8 Configuration and Management ............................... 19
9 Feasibility and Complexity ................................. 20
9.1 Feasibility ............ ............................... 20
9.1.1 Filtering .......................................... 20
9.1.2 Sampling ........................................... 20
9.1.3 Hashing ............................................ 20
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9.1.4 Reporting .......................................... 20
9.1.5 Export ............................................. 21
9.2 Potential Hardware Complexity .......................... 21
10 Applications .............................................. 22
10.1 Baseline Measurement and Drill Down ................... 22
10.2 Passive Customer Performance Measurements ............. 23
10.3 Troubleshooting ....................................... 23
11 References ................................................ 24
12 Authors' Addresses ........................................ 25
13 Intellectual Property Statement ........................... 26
14 Full Copyright Statement .................................. 27
1 Motivation
This document describes a framework in which to define a standard
set of capabilities for network elements to sample subsets of
packets by statistical and other methods. The framework will
accommodate future work to (i) specify a set of selection
operations by which packets are sampled (ii) specify the
information that is to be made available for reporting on sampled
packets; (iii) describe a protocol by which information on sampled
packets is reported to applications; (iv) describe a protocol by
which packet selection and reporting are configured.
The motivation to standardize these capabilities comes from the
need for measurement-based support for network management and
control across multivendor domains. This requires domain wide
consistency in the types of selection schemes available, the manner
in which the resulting measurements are presented, and
consequently, consistency of the interpretation that can be put on
them.
The capabilities are positioned as suppliers of packet samples to
higher level consumers, including both remote collectors and
applications, and on board measurement-based applications. Indeed,
development of the standards within the framework described here
should be open to influence by the requirements of standards in
related IETF WGs, for example, IP Performance Metrics (IPPM)
[PAMM98] and Internet Traffic Engineering (TEWG) [LCTV02].
Conversely, we expect that aspects of this framework not
specifically concerned with the central issue of packet selection
and report formation may be able to leverage work in other
WGs. Potential examples are the format and export of measurement
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reports, which may leverage the information model and export
protocols of IP Flow Information Export (IPFIX) [QZCZCN02], and
work in congestion aware unreliable transport in the Datagram
Congestion Control Protocol (DCCP) [FHK02].
2 Elements, Terminology, and Architecture
This section defines the basic elements of the PSAMP framework.
* PSAMP Device: a device hosting at least one of each of the
following: an observation point, a measurement process, and an
export process.
* Observation Point: The observation point is a location in the
network where packets can be observed. Examples are, a line
to which a probe is attached, a shared medium, such as an
Ethernet-based LAN, a single port of a router, or set of
interfaces (physical or logical) of a router, an embedded
measurement subsystem within an interface.
* Measurement Process: the combination of a selection process
followed by a reporting process.
* Selection Process: A selection process selects packets for
reporting at an observation point. The inputs to the selection
process are the packets observed at the observation point
(including packet encapsulation headers), information derived
from the packets' treatment at the observation point, and
selection state that may be maintained by the observation
point. Selection is accomplished through operating on these
inputs with one or more selection operations.
* Selection Operation: A configurable packet selection operation.
It takes as input the selection process input for a single
packet. If the packet is selected, this same information may be
considered as the output. Selection operations may change the
selection state.
* Selection State: the observation point may maintain state
information for use by the reporting process, and/or by
multiple selection operations, either on the same packet, or on
different packets. Examples include sequence numbers of packets
at the input of packet selectors, timestamps, iterators for
pseudorandom number generators, calculated hash values, and
indicators of whether a packet was selected by a given
selection operation.
* Composite Selection Operation: a selection operation that is
expressed as an ordered composition of other selection
operations. Thus a packet is selected by the composite
operation if it is selected by all its constituent selection
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operations in order.
* Primitive Selection Operation: a selection operation that is not
a composite of other selection operations.
* Reporting Process: the creation of a report stream of information
on packets selected by a selection processes, in preparation
for export. The input to a reporting process comprises that
information available to a selection process, for the selected
packets. The report stream contains two distinguished types of
information: packet reports, and report interpretation.
* Packet Reports: a configurable subset of the per packet input to
the reporting process.
* Report Interpretation: subsidiary information relating to one or
more packets, that is used for interpretation of their packet
reports. Examples include configuration parameters of the PSAMP
device, and configuration parameters of the selection and
reporting process.
* Export Process: sends the output of one or more reporting process
from the PSAMP device to one or more collectors.
* Collector: a collector receives a report stream exported by one
or more measurement processes. In some cases, the PSAMP device
may serve as the collector.
* Measurement packets: one or packet reports, and perhaps report
interpretation, are bundled by the export process into a
measurement packet for export to a collector.
The various possibilities for the high level architecture of these
elements is as follows. Note in the last case: the PSAMP device may
also be a collector.
OP = Observation Point, MP = Measurement Process, EP = Export Process
+---------------------+ +------------------+
|PSAMP Device(1) | | Collector(1) |
|Observation Point(s) | | |
|MP(s)--->EP----------+---------------->| |
|MP(s)--->EP----------+-------+-------->| |
+---------------------+ | +------------------+
|
+---------------------+ | +------------------+
|PSAMP Device(2) | +-------->| Collector(2) |
|Observation Point(s) | | |
|MP(s)--->EP----------+---------------->| |
+---------------------+ +------------------+
+---------------------+
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|PSAMP Device(3) |
|Observation Point(s) |
|MP(s)--->EP---+ |
| | |
|Collector(3)<-+ |
+---------------------+
3 Requirements
3.1 Selection Process Requirements.
* Ubiquity: The selection operations must be simple enough to be
implemented ubiquitously at maximal line rate.
* Applicability: the set of selection operations must be rich
enough to support a range of existing and emerging measurement
based applications and protocols. This requires a workable
trade-off between the range of traffic engineering applications
and operational tasks it enables, and the complexity of the set
of capabilities.
* Extensibility: to allow for additional packet selection
operations to support future applications.
* Flexibility: to support selection of packets using different
network protocols or encapsulation layers (e.g. IPv4, IPv6,
MPLS, etc), and under packet encryption.
* Visibility: robustness of packet selection w.r.t. attempts to evade
measurement.
* Parallel measurements: support multiple independent measurement
processes at the same device.
* Non-contingency: in order to satisfy the ubiquity requirement,
the selection decision for each packet must not depend on
future packets. Rather, the selection decision must be capable
of being made on the basis of the selection process input up to
and including the packet in question. This excludes selection
functions that require caching of packet for selection
contingent on subsequent packets. See also the timeliness
requirement following.
A range of candidate selection operations is given in Section 4.
Some detailed requirements of all selection operations are given in
Section 4.9. Those selection operations to be required by the PSAMP
standard are described in Section 4.10. Parallel measurement
processes are discussed in Section 8. A target set of applications
for PSAMP to support are described in Section 10.
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3.2 Reporting Process Requirements
* Timeliness: reports on selected packets should be made available
to the collector quickly enough to support near real time
applications.
* Transparency: allow transparent interpretation of measurements as
communicated by PSAMP reporting, without need to obtain
additional information from the measuring device.
* Robustness: allow robust interpretation of measurements with
respect to reports missing due to loss, e.g. in transport, or
omission at the measurement device. Inclusion in reporting of
information enabling accuracy of measurements to be determined.
* Faithfulness: all reported quantities that relate to the packet
treatment must reflect the router state and configuration
encountered by the packet in the PSAMP device.
* Privacy: selection of the content of packet reports will be
cognizant of privacy and anonymity issues while being
responsive to the needs of measurement applications, and in
accordance with RFC 2804. Full packet capture of arbitrary
packet streams is explicitly out of scope.
A specific reporting processes meeting these requirements, and the
requirement for ubiquity, is described in Section 5.
3.3 Export Process Requirements
* Congestion Avoidance: export of a report stream across a network
must be congestion avoiding in compliance with RFC 2914.
* Secure Export: the ability to export securely, e.g. by encryption
Candidate export processes meeting these requirements are described
in Section 6.
3.4 Configuration Requirements
* Ease of Configuration: of sampling and export parameters,
e.g. for automated remote reconfiguration in response to
measurements.
* Secure Configuration: configuration via protocols that prevent
unauthorized reconfiguration.
Specific configuration capabilities that meet these requirements
are discussed in Section 8. Feasibility and complexity of PSAMP
operations is discussed in Section 9.
Reuse of existing protocols will be encouraged provided the
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protocol capabilities are compatible with the requirements laid out
in this section.
4 Packet Selection
The function of packet selection is to select a subset from the
stream of all packets visible at an observation point. Selection
can be used to select packets of based on their content, and/or to
reduce the rate of packets reports regardless of content. This
section details some candidate primitive selection operations for
standardization that satisfy the requirements of Section 3.1. Not
all operations listed here are intended for standardization. Those
that are are listed in Section 4.10. Packet selection techniques
are discussed in more detail in [ZMR03].
4.1 Filtering
Filtering is the selection of packets based only the packet
content, the treatment of the packet at the observation point, and
deterministic functions of these occurring in the selection
state. The packet is selection if these quantities fall into a
specified range. Hash-based packet selection (see Section 4.3) can
also be regarded as a filter)
An example is a match/mask filter applied to a combination of bit
positions. The packet is selected if the bits and the match are
equal after taking the logical AND of both with the mask. Higher
level interfaces may be used to specify mask and matches for
particular fields, for example, for IP addresses. Filtering on
information derived from packet treatment, e.g., AS numbers derived
from routing state, is another possibility; see Section
4.8. Filtering based on calculated hashes is described separately
in Section 4.4.
4.2 Systematic Sampling
In systematic sampling, the triggers for sampling are periodic,
either in time or in packet count. All packets occurring in a
selection interval (either in time or packet count) beyond the
trigger are selected. The case that the selection interval covers
only the first available packet for count-based sampling is often
called 1 in N sampling: packets are selected with count period N.
More generally, some number M<N of consecutive packets are selected.
4.3 Random Sampling
4.3.1 Uniform Random Sampling
Packets are selected according to a probabilistic law. In the first
instance, we consider Independent Uniform Random Sampling: packets
are selected independently with some uniform probability 1/N. This
count-driven sampling is sometimes referred to a geometric random
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sampling, since the difference in count between successive selected
packets are independent random variables with a geometric
distribution of mean N. A time-driven analog, exponential random
sampling, has the time between triggers exponentially distributed.
Both geometric and exponential random sampling are examples of what
is known as additive random sampling, defined as sampling where the
intervals or counts between successive samples are independent
identically distributed random variable.
4.3.2 Stratified Random Sampling
Generally, in stratified random sampling, packets are assigned to
strata according to an attribute, then a number of elements are
drawn randomly from each stratum. Stratification reduces variance
of single packet statistics if the variance between strata is
greater than the variance within strata. In uniform stratified
sampling, the number of elements in each stratum is the same, as is
the number selected from each stratum. Thus each packet has the
same selection probability, but some combination selections are
disallowed.
With the non-contingency requirement on sampling, the only allowed
uniform stratification is that based on packet count. Each group of
N successive packets forms a stratum, then some number M<N of
each are drawn at random. This is non-contingent because the random
positions of the selected packets can be generated in advance for
each stratum.
4.3.3 Non-Uniform Independent Random Sampling
Also known as non-uniform probability sampling, or content
dependent sampling, this is a variant of independent random
sampling in which the sampling probabilities can depend on the
selection process input. This can be used to weight sampling
probabilities in order e.g. to boost the chance of sampling packets
that are rare but are deemed important. Unbiased estimators for
quantitative statistics are recovered by renormalization of sample
values; see [HT52].
4.4 Hash-based Selection
Hash-based selection offers both a way to emulate random selection
by generating from the packets' content pseudorandom variates on
which to make packet selection decisions, while consistently select
subsets of packets that share a common property. A hash function h
operates on the selection function input for a packet, and maps in
onto a range R. The packet is selected if the resulting hash falls
in a specified range S. Thus hashing is a filter. But for good hash
functions (see below) the inverse image inv(h(S)) will be extremely
complex, and hence h would not be expressible as, say, a match/mask
filter or a simple combination of these.
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A hash function should have good mixing properties, in that
changing one bit of the input should change many bits of the
output. Then the distribution of hashes will be be fairly uniform,
independent of the distribution of the input. The sampling rate is
then #S/#R. If the input comprises distinct packet fields, c1
... cm, selection decisions will appear uncorrelated with the
contents of any individual field, if the complementary fields are
sufficiently variable and uncorrelated with cj.
Even with a publicly known hash-function, accidental or
deliberate evasion (or overwhelming) of hash-based sampling is
militated against by keeping the selection range private, and/or
employing a parameterizable hash function and keeping the parameter
private.
4.4.1 Consistent Flow Sampling
Hash-based sampling can be used to select all packets from a
pseudorandom set of flows. The flow key of each packet is hash
sampled, and selected packets are reported on. All packets with a
given key are either selected or not selected together.
4.4.2 Trajectory Sampling
In trajectory sampling, PSAMP devices in a network hash-sample
packets using identical hash function and selection range. The
domain of the hash is restricted to those fields that are invariant
from hop to hop. Fields such as Time-to-Live, which is decremented
per hop, and header CRC, which is recalculated per hop, are thus
excluded from the hash domain. Thus a given packet is selected at
all either all points on its path through the network, or at
none. The domain of the hash function needs to be wider than just a
flow key, if packets are to be selected pseudorandomly within flows;
see [DuGr01]. A report on each selected packet is exported to a
collector. The collector can reconstruct trajectories of the
selected packets provided it can match different reports on the
same packet, and distinguish these from reports on different
packets. For this purpose, reports may also contain a second
distinct hash, the identification hash, of the selected packets
and/or timing information. The identification hash can be
considered as part of the selection state.
Applications of trajectory sampling include (i) estimation of the
network path matrix, i.e., the traffic intensities accordng to
network path, broken down by flow; (ii) detection of routing loops,
as indicated by self-intersecting trajectories; (iii) passive
performance measurement: prematurely terminating trajectories
indicate packet loss, and packet latencies can be determined if
reports include (synchronized) timestamps; (iv) network attack
tracing, of the actual paths taken by attack packets with spoofed
source addresses. Applications are discussed in Section 10.
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4.5 Generation of Pseudorandom Variates
Although pseudorandom number generators with well understood
properties have been developed, they may not be the method of
choice in setting where computational resources are scarce. A
convenient alternative is to use packet content as a source of
randomness. Hash-based sampling is an example: the hash (suitably
renormalized) is a pseudorandom variate in the interval
[0,1]. Other schemes may use packet fields in iterators for
pseudorandom numbers. The point here, is that the statistical
properties of the idealized packet selection law (such as
independence of sampling decisions for different packets, or
independence on packet content) may not be exactly shared by an
implementation, but only approximately so.
Although the selection decisions for non-uniform independent random
sampling (see Section 4.3.3 above) also depend on the packet
content, this form of sampling is distinguished from the use of
packet content to generate variates. In the former case, the
content only determines the selection probabilities: selection
could then proceed e.g by use of a variates obtained by an
independent pseudorandom number generator. In the latter case, the
content determines the pseudorandom variates rather than the
probabilities.
4.6 Criteria for Choice of Selection Operations
4.6.1 Evaluating the Need for Distinct Selection Operations
In current practice, sampling has been performed using particular
algorithms, including:
- pseudorandom independent sampling with probability 1/N;
- systematic sampling of every Nth packet.
The question arises as to whether both of these should be
standardized as distinct selection operations, or whether they can
be regarded as different implementations of a single selection
operation.
To determine the answer to this question, we need to consider
(a) measured or assumed statistical properties of the packet
stream, e.g., one or more of the following:
- contents of different packets are statistically independent
- correlations between contents of different packets decay
at a specified rate
- contents of certain fields within the same packet are
significantly variable and exhibit small cross correlation
(b) the desired reference sampling model, e.g., one of:
- sample packets with long term probability 1/N
- sample packets independent with probability 1/N
(c) the set of possible alternatives and implementations, e.g.,
one of:
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- pseudorandom independent sampling with probability 1/N
- systematic sampling with period N
- hash-based sampling with target probability 1/N
(d) the tolerance for error in the applications that use the
measurements.
We can say that a given alternative from (c) reproduces a reference
model (b) for the applications if the results obtained using them
are sufficiently accurate in (d) for traffic satisfying an assumed
statistical properties in (a). Clearly, application to evaluate
methods in (c) requires developing agreement on the relevant
properties in (a), (b) and (d).
Example: systematic sampling with period N will not count the
occurrence of closely space packets (less than N counts apart) from
the same flow. Thus for applications that are concerned with the
joint statistics of multiple packets within flows, systematic
sampling may not reproduce the results obtained with random
sampling sufficiently accurately.
4.6.2 Comparison of Uniform Sampling Methods
A comparison of sampling methods, and their accuracy for estimating
single packet statistics (e.g. mean and distribution of packet
length) has been performed in [CPB93]. It was found that
estimation using count-based methods was uniformly more accurate
than that using time-based methods. Time based methods were found
to be particularly inaccurate for assessing interarrival times,
since they may miss traffic bursts. There was comparatively little
difference between the systematic, stratified and random sampling,
at least for the single packet statistics examined. For this
reasons, we believe PSAMP should focus on count-based
methods. Amongst these, accuracy of single packet statistics is not
a great deciding factor. Systematic and random sampling are easier
to implement than stratified sampling.
4.7. Constraints on the Sampling Rate
Sampling at full line rate, i.e. with probability 1, is not
excluded in principle, although resource constraints may not
support it in practice.
4.8 Selection According to Packet Treatment
Router architectural considerations may preclude some information
concerning the packet treatment, e.g routing state, being available
at line rate for selection of packets. However, if selection not
based on routing state has reduced down from line rate,
subselection based on routing state may be feasible.
4.9 Input sequence numbers for primitive selection operations.
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Each instance of a primitive selection operation MUST maintain a
count of packets presented at its input. The counter value is to be
included as a sequence number for selected packets. This enables
applications to determine the attained rate at which packets are
selected, and hence correctly normalize network usage estimates
regardless of loss of information, whether this occurs because of
discard of packet reports in the PSAMP device, or loss of
measurement packets in transmission or collection; see [PPM01].
The sequence numbers are considered as part of the packet's
selection state.
4.10 Standardized Selection Operations and Application Requirements
In this section we list selection operations in three categories:
mandatory (MUST), recommended (SHOULD) and optional (MAY). In each
case, we list the applications (described in Section 10) that are
supported.
4.10.1 Mandatory Selection Operations
PSAMP devices MUST support the following:
(i) either count-based 1 in N systematic sampling or
count-based simple random sampling.
Either operation supports widespread baseline measurement. They
reflect current practice: both exist in various routers that are
currently available. Furthermore, initial implementation of PSAMP
may be constrained to be implemented in software, for devices whose
hardware was not designed with PSAMP in mind. It is reasonable that
such devices be PSAMP compliant by offering (i) above.
4.10.2 Recommended Selection Operations
PSAMP devices SHOULD support the following:
(ii) both options in 4.10.1(i) above.
(iii) general count-based systematic sampling.
(iv) The PSAMP device identifies packet fields relating to
different protocols, including IPv4, IPv6, MPLS and AToM.
(v) filtering by match/mask, or by single numerical range, on
all/any of the fields identified in (iv), together with packet
treatment, if any, performed by the PSAMP device.
(vi) hash-based selection on a configurable input comprising
any/all of the fields identified in (iv), using one or more
hash functions to be standardized.
(vii) composite sampling operations comprising filtering and any
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other selection operation, configurably composed in either
order.
(viii) at least two parallel independent selection processes,
one or both of which can be composite.
Filtering, and its combination with sampling, support drill down
analysis. Having at least two parallel independent selection
processes (and their associated reporting processes) allows drill
down to be simultaneously performed with baseline
measurements. Hash-based sampling supports trajectory sampling.
The operations (ii)-(viii) are listed as recommended, rather than
mandatory, in recognition of the fact that some PSAMP devices
e.g. simple switches or hubs, may not have the native capabilities
to provide detailed protocol information, or perform computations
for any selection other than sampling.
Conversely, when a PSAMP device is, in its usual capacity, capable
of recognizing use of a given protocol in the packet, then the
protocol fields MUST be identifiable in (iv) above, and the
contents made available for input to filters and hash-based
sampling. Thus, for example, a device which routes at the IP level,
must make IP field available for filtering.
Similarly, any information relating to packet treatment performed
by the device MUST be made available for filtering. Thus if the
device is a router, routing state MUST be made available for
filtering. (See also Section 4.8).
4.10.3 Optional Selection Operations
PSAMP devices MAY support the following:
(i) non-uniform independent random sampling, based on fields
identified in 4.10.2(iv).
5 Reporting
Information eligible for inclusion in packet reports includes (i)
the packet content itself (including encapsulating headers), but
not the requiring the full packet payload; (ii) information
relating to the packet treatment: incoming and outgoing interfaces,
subinterfaces and channel identifiers, routing state applied to or
derived from the packet e.g. next hop IP address, routing prefixes,
source and destination AS numbers; (iii) selection state associated
with the packet, e.g. timestamps, sequence numbers, hash values.
5.1 Mandatory Reporting
All PSAMP devices MUST report the following for each selected packet:
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(i) identifiers for the input and output interfaces of the
PSAMP device that were traversed by the packet.
(ii) the input sequence number(s) of any elementary selection
operation(s) that acted on the packet.
(iii) some number of contiguous bytes from the start of the
packet.
The motivation is that some devices may not have the resource
capacity or functionality to identify fields within a packet. The
burden of interpretation is placed on the collector or applications
that it supplies.
5.2 Recommended Reporting
PSAMP devices SHOULD report as follows for each selected packet:
(iv) identification of packet fields relating to different
protocols, including IPv4, IPv6, MPLS and AToM.
(v) configurable inclusion of any/all fields from (iv) in the
packet report, together with information from packet treatment
if present.
(vi) selection state associated with the packet, including
timestamps and hashes calculated.
Similar considerations apply as those of Section 4.10.2: if a device
has the native capability to recognize protocol fields and/or treat
packets, the the field contents and packet treatment MUST be made
available for reporting.
5.3 Report Interpretation
Information for use in report interpretation includes (i)
configuration parameters of the selectors of the packets reported
on; (ii) format of the packet reports (iii) configuration
parameters and state information of the network element; (iv)
indication of the inherent accuracy of the reported quantities,
e.g., of timestamps.
The requirements for robustness and transparency are motivations
for including report interpretation in the report stream. Inclusion
makes the report stream self-defining. The PSAMP framework
excludes reliance on an alternative model in which interpretation
is recovered out of band. This latter approach is not robust with
respect to undocumented changes in selection configuration, and
leaves an architectural hostage for network management systems to
coherently manage both configuration and data collection.
It is not envisaged that all report interpretation must be included
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in every packet report. Many of the quantities listed above are
expected to be relatively static; they could be communicated
periodically, and upon change.
To conserve network bandwidth and resources at the collector, the
PSAMP device may compress the measurement packets before export.
Compression should be quite effective since the sampled packets may
share many fields in common, e.g. if a filter focuses on packets
with certain values in particular header fields. Using compression,
however, could impact the timeliness of reports. Any consequent
delay should not violate the timeliness requirement for
availability of packet reports at the collector.
6 Export and Congestion Avoidance
6.1 Collector Destination
When exporting to a remote collector, the collector is identified
by IP address and port number.
6.2 Local Export
The report stream may be directly exported to on-board measurement
based applications, for example those that for composite statistics
from more than one packet. Local export may be presented through an
interface direct to the higher level applications, i.e., through an
API, rather than employing the transport used for off-board export.
A possible example of the local export could be that the selected
packets from the PSAMP measurement process serve as the input for
the IPFIX protocol, i.e. delivering flow records out of the packets
selected out via PSAMP. Note that IPFIX being still developed,
this is just listed as a possible example.
6.3 Reliable vs. Unreliable Transport
The export of the report stream does not require reliable
export. On the contrary, retransmission of lost measurement packets
consumes additional network resources and require maintenance of
state by the export process. The PSAMP device would have to be able
to receive and process acknowledgments, and to store unacknowledged
data. Furthermore, the PSAMP device may not possess its own network
address (for example an embedded measurement subsystem in an
interface) at which to receive acknowledgments. These requirements
would be a significant impediment to having ubiquitous support
PSAMP.
Instead, it is proposed that PSAMP devices support an unreliable
export mechanism. Sequence numbers on the measurement packets would
indicate when loss has occurred, and the analysis of the collected
measurement data can account for this loss. In some sense, packet
loss becomes another form of sampling (albeit a less desirable, and
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less controlled, form of sampling).
6.4 Limiting Delay in Exporting Measurement Packets
The device may queue the report stream in order to export multiple
reports in a single measurement packet. Any consequent delay should
still allow for timely availability of packet reports at the
collector.
6.5 Configurable Export Rate Limit
The export process must be able to limit its export rate; otherwise
it could overload the network and/or the collector. Note this
problem would be exacerbated if using reliable transport mode,
since the PSAMP device would retransmit any lost packets, thereby
imposing an additional load on the network.
At times, the device may generate new packet reports faster than
the allowed export rate. In this situation, the device should
discard the excess reports rather than transmitting them to the
collector. Sequence numbers reported for selector input enable
correction for lost reports. An additional sequence number for
dispatched measurement packets enables the collector to determine
the degree of loss in transmission.
There are two options for a configurable rate limit. First, if the
transport protocol has a configurable rate limit, that can be used.
The second option is to limit the rate at which measurement packets
are supplied to the transport protocol. A candidate for
implementation of rate limiting is the leaky bucket, with tokens
corresponding e.g. to bytes or packets.
The export rate limit must be configurable per export process. Note
that since congestion loss can occur at any link on the export
path, it is not sufficient to limit rate simply as a function of
the bandwidth of the interface out of which export takes place.
6.6 Congestion-aware Unreliable Transport
Exported measurement traffic competes for resources with other
Internet transfers. Congestion-aware export is important to ensure
that the measurement packets do not overwhelm the capacity of the
network or unduly degrade the performance of other applications,
while making good use of available bandwidth resources.
The PSAMP WG will evaluate (at least) the following alternatives
for congestion aware unreliable transport:
(i) protocols under development, including the Datagram Congestion
Control Protocol (DCCP); see [FHK02]
(ii) protocols adopted in the future by the IPFIX WG,
(iii) collector-based rate reconfiguration, as now described.
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6.7 Collector-based Rate Reconfiguration
Since collector-based rate reconfiguration is a new proposal, this
draft will discuss it in some detail.
The collector can detect congestion loss along the path from the
PSAMP device through lost packets, manifest as gaps in the sequence
numbers, or the absence of packets for a period of time. The server
can run an appropriate congestion-control algorithm to compute a
new export rate limit, then reconfigure the PSAMP device with the
new rate. This is an attractive alternative to requiring the PSAMP
device to receive acknowledgment packets. Implementing the
congestion control algorithm in the collector has the added
advantages of flexibility in adapting the sending rate and the
ability to incorporate new congestion-control algorithms as they
become available.
6.7.1 Changing the Export Rate and Other Rates
Forcing the PSAMP device to discard excess reports is an effective
control under short term congestion. Alternatively, the device
could be reconfigured to select fewer packets, and/or send smaller
reports on each selected packet. This may be a more appropriate
reaction to long-term congestion. In some cases, a collector may
receive measurement reports from more than one device, and could
decide to reduce the export or other rates at one device rather
than another, in order to prioritize the measurement data. This
type of flexibility is valuable for network operators that collect
measurement data from multiple locations to drive multiple
applications.
6.7.2 Notions of Fairness
In some cases, it may be reasonable to allow the collector to have
flexibility in deciding how aggressively to respond to congestion.
For example, the PSAMP device and the collector may have a very
small round-trip time relative to other traffic. Conventional
TCP-friendly congestion control would allocate a very large share
of the bandwidth to this traffic. Instead, the collector could
apply an algorithm that reacts more aggressively to congestion to
give a larger share of the bandwidth to other traffic (with larger
RTTs).
In other cases, the measurement packets may require a larger share
of the bandwidth than other flows. For example, consider a link
that carries tens of thousands of flows, including some non
TCP-friendly DoS attack traffic. Restricting the PSAMP traffic to
a fair share allocation may be too restrictive, and might limit the
collection of the data necessary to diagnose the DoS attack which
overloads links over which measurement packets are carried. In
order to maintain report collection during periods of congestion,
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PSAMP report streams may claim more than a fair share of link
bandwidth, provided the number of report streams in competition
with fair sharing traffic is limited. The collector could also
employ policies that allocate bandwidth in certain proportions
amongst different measurement processes.
6.7.3 Behavior Under Overload and Failure
The congestion control algorithm has to be robust to severe
overload or complete loss of connectivity between the PSAMP device
and the collector, and also to the failure of the device or the
collector. For example, in a scenario where the collector is unable
to reconfigure the export rate because of loss of reverse
(collector to PSAMP device) connectivity, it is desirable that
the device reduce the export rate automatically. Similarly, if no
measurement reports reach the collector because of loss of
forward connectivity, the collector should not react to
this by increasing the export rate. This problem may be solved
through periodic heartbeat packets in both directions (i.e.,
measurement reports in the forward direction, configuration refresh
messages in the reverse direction). This allows each side to detect
a loss in connectivity or outright failure and to react
appropriately.
7 Parallel Measurement Processes
Because of the increasing number of distinct measurement
applications, with varying requirements, it is desirable to set up
parallel measurement processes on a stream of packets. Each
process should consist of independently-configurable selection,
reporting and export processes.
Each of the parallel measurement processes should be, as far as
possible, independent. However, resource constraints may prevent
complete reporting on a packet selected by multiple selection
processes. In this case, reporting for the packet must be complete
for at least one measurement process; other measurement processes
need only report that they selected the packet. The priority
amongst measurement processes to report packets must be
configurable.
It is not proposed to standardize the number of parallel
measurement processes available beyond the recommendation of
Section 4.10.2.
8 Configuration and Management
A key requirement for PSAMP is the easy reconfiguration of
the parameters of the measurement process: those for
selection, packet reports and export. Examples are (i) support of
measurement based applications that want to drill-down on traffic
detail in real-time; (ii) collector-based rate reconfiguration.
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To facilitate reconfiguration and retrieval of parameters, they are
to reside in a Management Information Base (MIB). CLI and SNMP
access to these parameters must be available.
9 Feasibility and Complexity
In order for PSAMP to be supported across the entire spectrum of
networking equipment, it must be simple and inexpensive to
implement. One can envision easy-to-implement instances of the
mechanisms described within this draft. Thus, for that subset of
instances, it should be straightforward for virtually all system
vendors to include them within their products. Indeed, sampling and
filtering operations are already realized in available equipment.
Here we give some specific arguments to demonstrate feasibility and
comment on the complexity of hardware implementations. We stress
here that the point of these arguments is not to favor or recommend
any particular implementation, or to suggest a path for
standardization, but rather to demonstrate that the set of possible
implementations is not empty.
9.1 Feasibility
9.1.1 Filtering
Filtering consists of a small number of mask (bit-wise logical),
comparison and range (greater than) operations. Implementation of
at least a small number of such operations is straightforward. For
example, filters for security access control lists (ACLs) are
widely implemented. This could be as simple as an exact match on
certain fields, or involve more complex comparisons and ranges.
9.1.2 Sampling
Sampling based on either counters (counter set, decrement, test for
equal to zero) or range matching on the hash of a packet (greater
than) is possible given a small number of selectors, although there
may be some differences in ease of implementation for hardware
vs. software platforms.
9.1.3 Hashing
Hashing functions vary greatly in complexity. Execution of a small
number of sufficient simple hash functions is implementable at line
rate. Concerning the input to the hash function, hop-invariant IP
header fields (IP address, identification) and TCP/UDP header
fields (port numbers, TCP sequence number) drawn from the first 40
bytes of the packet have been found to possess a considerable
variability; see [DuGr01].
9.1.4 Reporting
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The simplest packet report would duplicate the first n bytes of the
packet. However, such an uncompressed format may tax the bandwidth
capabilities of the PSAMP device for high sampling rates; reporting
selected fields would save on bandwidth within the PSAMP
device. Thus there is a trade-off between simplicity and bandwidth
limitations within the PSAMP device.
9.1.5 Export
Ease of exporting measurement packets depends on the system
architecture. Most systems should be able to support export
by insertion of measurement packets, even through the software
path.
9.2 Potential Hardware Complexity
We now comment on the complexity of possible hardware
implementations. Achieving low constants for performance while
minimizing hardware resources is, of course, a challenge,
especially at very high clock frequencies. Most of these
operations, however, are very basic and their implementations very
well understood; in fact, the average ASIC designer simply uses
canned library instances of these operations rather than design
them from scratch. In addition, networking equipment generally does
not need to run at the fastest clock rates, further reducing the
effort required to get reasonably efficient implementations.
Simple bit-wise logical operations are easy to implement in
hardware. Such operations (NAND/NOR/XNOR/NOT) directly translate
to four-transistor gates. Each bit of a multiple-bit logical
operation is completely independent and thus can be performed in
parallel incurring no additional performance cost above a single
bit operation.
Comparisons (EQ/NEQ) take O(lg(M)) stages of logic, where M is the
number of bits involved in the comparison. The lg(M) is required
to accumulate the result into a single bit.
Greater than operations, as used to determine whether a hash falls
in a selection range, are a determination of the most significant
not-equivalent bit in the two operands. The operand with that
most-significant-not-equal bit set to be one is greater than the
other. Thus, a greater than operation is also an O(lg(M)) stages
of logic operation. Optimized implementations of arithmetic
operations are also O(lg(M)) due to propagation of the carry bit.
Setting a counter is simply loading a register with a state. Such
an operation is simple and fast O(1). Incrementing or decrementing
a counter is a read, followed by an arithmetic operation followed
by a store. Making the register dual-ported does take additional
space, but it is a well-understood technique. Thus, the
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increment/decrement is also an O(lg(M)) operation.
Hashing functions come in a variety of forms. The computation
involved in a standard Cyclic Redundancy Code (CRC) for example are
essentially a set of XOR operations, where the intermediate result
is stored and XORed with the next chunk of data. There are only
O(1) operations and no log complexity operations. Thus, a simple
hash function, such as CRC or generalizations thereof, can be
implemented in hardware very efficiently.
At the other end of the range of complexity, the MD5 function uses
a large number of bit-wise conditional operations and arithmetic
operations. The former are O(1) operations and the latter are
O(lg(M)). MD5 specifies 256 32b ADD operations per 16B of input
processed. Consider processing 10Gb/sec at 100MHz (this processing
rate appears to be currently available). This requires processing
12.5B/cycle, and hence at least 200 adders, a sizeable
number. Because of data dependencies within the MD5 algorithm, the
adders cannot be simply run in parallel, thus requiring either
faster clock rates and/or more advanced architectures. Thus
selection hashing functions as complex as MD5 may be precluded from
ubiquitous use at full line rate. This motivates exploring the use
of selection hash functions with complexity somewhere between that
of MD5 and CRC. However, identification hashing with MD5 on only
selected packets is feasible at a sufficiently low sampling rate.
10 Applications
We first describe several representative operational applications
that require traffic measurements at various levels of temporal and
spatial granularity enabled by a PSAMP device. Some of the goals
here appear similar to those of IPFIX, at least in the broad
classes of applications supported. However, there are two major
differences:
- PSAMP aims for ubiquitous deployment, thus offering broader
reach for existing applications.
- PSAMP can support new applications.
10.1 Baseline Measurement and Drill Down
Packet sampling is ideally suited to determine the composition of
the traffic across a network. The approach is to enable measurement
on a cut-set of the network links such that each packet entering
the network is seen at least once, for example, on all ingress
links. Unfiltered sampling with a relatively low rate establishes
baseline measurements of the network traffic. Reports include
packet attributes of common interest: source and destination
address and port numbers, prefix, protocol number, type of service,
etc. Traffic matrices are indicated by reporting source and
destination AS matrices. Absolute traffic volumes are estimated by
renormalizing the sampled traffic volumes through division by
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either the target sampling rate, or the attained sampling rate (as
derived by interface packet counters included in the report stream)
Suppose an operator or a measurement based application detects an
interesting subset of traffic identified by a particular packet
attribute. Real-time drill-down to that subset is achieved by
instantiating a new measurement process at the PSAMP device from
which the subset was reported. The selection process of the new
measurement process filters according to the attribute of interest,
and composes with sampling if necessary to manage the rate of
packet selection.
10.2 Customer Performance
Hash-based sampling enables the tracking of the performance
experience by customer traffic, customers identified by a
list of source or destination prefixes, or by ingress or egress
interfaces. Operational uses include the verification of SLAs, and
troubleshooting following a customer complaint.
In this application, Trajectory Sampling is enabled at all ingress
and egress interfaces. The label hash is used to match up ingress
and egress samples. Rates of loss in transit between ingress and
egress are estimated from the proportion of trajectories for which
no egress report is received. Note loss of customer packets is
distinguishable from loss of packet reports through use of report
sequence numbers. Assuming synchronization of clock between PSAMP
devices, delay of customer traffic across the network may also be
measured.
Extending hash-sampling to all interfaces in the network would
enable attribution of poor performance to individual network links.
10.3 Troubleshooting
PSAMP can also be used to diagnose problems whose occurrence is
evident from aggregate statistics, per interface utilization and
packet loss statistics. These statistics are typically moving
averages over relatively long time windows, e.g., 5 minutes, and
serve as a coarse-grain indication of operational health of the
network. The most common method of obtaining such measurements are
through the appropriate SNMP MIBs (MIB-II and vendor-specific
MIBs.)
Suppose an operator detects a link that is persistently overloaded
and experiences significant packet drop rates. There is a wide
range of potential causes: routing parameters (e.g., OSPF link
weights) that are poorly adapted to the traffic matrix, e.g.,
because of a shift in that matrix; a denial of service attack or a
flash crowd; a routing problem (link flapping). In most cases,
aggregate link statistics are not sufficient to distinguish between
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such causes, and to decide on an appropriate corrective action. For
example, if routing over two links is unstable, and the links flap
between being overloaded and inactive, this might be averaged out
in a 5 minute window, indicating moderate loads on both links.
Baseline PSAMP measurement the congested link, as described in
Section 10.1, enables measurements that are fine grained in both
space and time. The operator has to be able to determine how many
bytes/packets are generated for each source/destination address,
port number, and prefix, or other attributes, such as protocol
number, MPLS forwarding equivalence class (FEC), type of service,
etc. This allows to pinpoint precisely the nature of the offending
traffic. For example, in the case of a DDoS attack, the operator
would see a significant fraction of traffic with an identical
destination address.
In certain circumstances, precise information about the spatial
flow of traffic through the network domain is required to detect
and diagnose problems and verify correct network behavior. In the
case of the overloaded link, it would be very helpful to know the
precise set of paths that packets traversing this link follow. This
would readily reveal a routing problem such as a loop, or a link
with a misconfigured weight. More generally, complex diagnosis
scenarios can benefit from measurement of traffic intensities (and
other attributes) over a set of paths that is constrained in some
way. For example, if a multihomed customer complains about
performance problems on one of the access links from a particular
source address prefix, the operator should be able to examine in
detail the traffic from that source prefix which also traverses the
specified access link towards the customer.
While it is in principle possible to obtain the spatial flow of
traffic through auxiliary network state information, e.g., by
downloading routing and forwarding tables from routers, this
information is often unreliable, outdated, voluminous, and
contingent on a network model. For operational purposes, a direct
observation of traffic flow is more reliable, as it does not depend
on any such auxiliary information. For example, if there was a bug
in a router's software, direct observation would allow to diagnose
the effect of this bug, while an indirect method would not.
Trajectory sampling by enabling common hash-based sampling on all
routers in a domain supports such diagnoses. In particular, routing
loops are revealed as cycles in trajectories.
11 References
[B88] R.T. Braden, A pseudo-machine for packet monitoring and
statistics, in Proc ACM SIGCOMM 1988
[ClPB93] K.C. Claffy, G.C. Polyzos, H.-W. Braun, Application of
Sampling Methodologies to Network Traffic Characterization,
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Proceedings of ACM SIGCOMM'93, San Francisco, CA, USA, September
13-17, 1993
[DuGr01] N. G. Duffield and M. Grossglauser, Trajectory Sampling for
Direct Traffic Observation, IEEE/ACM Trans. on Networking, 9(3), pp.
280-292, June 2001.
[FHK02] S. Floyd, M. Handley. E. Kohler, Problem Statement for
DCCP, Internet Draft draft-ietf-dccp-problem-00.txt, work in
progress, October 2002.
[HT52] D.G. Horvitz and D.J. Thompson, A Generalization of Sampling
without replacement from a Finite Universe. J. Amer. Statist.
Assoc. Vol. 47, pp. 663-685, 1952.
[LCTV02] W.S. Lai, B.Christian, R.W. Tibbs, S. Van den Berghe, A
Framework for Internet Traffic Engineering Measurement, Internet
Draft draft-ietf-tewg-measure-04.txt, work in progress, September
2002.
[PPM01] P. Phaal, S. Panchen, N. McKee, InMon Corporation's
sFlow: A Method for Monitoring Traffic in Switched and Routed
Networks, RFC 3176, September 2001
[PAMM98] V. Paxson, G. Almes, J. Mahdavi, M. Mathis, Framework for
IP Performance Metrics, RFC 2330, May 1998
[QZCZCN02] J. Quittek, T. Zseby, B. Claise, S. Zander, G. Carle,
K.C. Norseth, Requirements for IP Flow Information Export,
Internet Draft draft-ietf-ipfix-reqs-08.txt, work in progress,
January 2003.
[SPSJTKS01] A. C. Snoeren, C. Partridge, L. A. Sanchez, C. E. Jones,
F. Tchakountio, S. T. Kent, W. T. Strayer, Hash-Based IP Traceback,
Proc. ACM SIGCOMM 2001, San Diego, CA, September 2001.
[ZMR02] T. Zseby, M. Molina, F. Raspall, Sampling
and Filtering Techniques for IP Packet Selection, Internet Draft
draft-ietf-psamp-sample-tech-01.txt, work in progress, March 2003.
12 Authors' Addresses
Nick Duffield
AT&T Labs - Research
Room B-139
180 Park Ave
Florham Park NJ 07932, USA
Phone: +1 973-360-8726
Email: duffield@research.att.com
Albert Greenberg
AT&T Labs - Research
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Room A-161
180 Park Ave
Florham Park NJ 07932, USA
Phone: +1 973-360-8730
Email: albert@research.att.com
Matthias Grossglauser
AT&T Labs - Research
Room A-167
180 Park Ave
Florham Park NJ 07932, USA
Phone: +1 973-360-7172
Email: mgross@research.att.com
Jennifer Rexford
AT&T Labs - Research
Room A-169
180 Park Ave
Florham Park NJ 07932, USA
Phone: +1 973-360-8728
Email: jrex@research.att.com
Derek Chiou
Avici Systems
101 Billerica Ave
North Billerica, MA 01862
Phone: +1 978-964-2017
Email: dchiou@avici.com
Peram Marimuthu
Cisco Systems
170, W. Tasman Drive
San Jose, CA 95134
Phone: (408) 527-6314
Email: peram@cisco.com
Ganesh Sadasivan
Cisco Systems
170 W. Tasman Drive
San Jose, CA 95134
Phone: (408) 527-0251
Email: gsadasiv@cisco.com
13 Intellectual Property Statement
AT&T Corporation may own intellectual property applicable to this
contribution. The IETF has been notified of AT&T's licensing intent
for the specification contained in this document. See
http://www.ietf.org/ietf/IPR/ATT-GENERAL.txt for AT&T's IPR
statement.
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14 Full Copyright Statement
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