IPFIX Working Group B. Trammell
Internet-Draft E. Boschi
Intended status: Standards Track ETH Zurich
Expires: March 25, 2011 A. Wagner
Consecom AG
September 21, 2010
Exporting Aggregated Flow Data using the IP Flow Information Export
(IPFIX) Protocol
draft-trammell-ipfix-a9n-00.txt
Abstract
This document describes the export of aggregated Flow information
using IPFIX. An Aggregated Flow is essentially an IPFIX Flow
representing packets from zero or more original Flows, within an
externally imposed time interval. The document describes Aggregated
Flow export within the framework of IPFIX Mediators and defines an
interoperable, implementation-independent method for Aggregated Flow
export.
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This Internet-Draft will expire on March 25, 2011.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Requirements for Aggregation Support in IPFIX . . . . . . . . 4
4. Use Cases for IPFIX Aggregation . . . . . . . . . . . . . . . 5
5. Aggregation of IP Flows . . . . . . . . . . . . . . . . . . . 6
5.1. A general model for IP Flow Aggregation . . . . . . . . . 6
5.2. Interval Distribution . . . . . . . . . . . . . . . . . . 8
5.3. Key Aggregation . . . . . . . . . . . . . . . . . . . . . 8
5.4. Aggregating and Distributing Counters . . . . . . . . . . 10
5.5. Counting Original Flows . . . . . . . . . . . . . . . . . 12
5.6. Counting Distinct Key Values . . . . . . . . . . . . . . . 12
5.7. Exact versus Approximate Counting during Aggregation . . . 13
5.8. Interval Combination . . . . . . . . . . . . . . . . . . . 13
6. Aggregation in the IPFIX Architecture . . . . . . . . . . . . 13
7. Export of Aggregated IP Flows using IPFIX . . . . . . . . . . 15
7.1. Time Interval Export . . . . . . . . . . . . . . . . . . . 15
7.2. Flow Count Export . . . . . . . . . . . . . . . . . . . . 16
7.2.1. originalFlowsPresent Information Element . . . . . . . 16
7.2.2. originalFlowsInitiated InformationElement . . . . . . 16
7.2.3. originalFlowsCompleted InformationElement . . . . . . 16
7.2.4. originalFlows InformationElement . . . . . . . . . . . 17
7.3. Aggregate Counter Distibution Export . . . . . . . . . . . 17
7.3.1. Aggregate Counter Distribution Options Template . . . 17
7.3.2. valueDistributionMethod Information Element . . . . . 18
8. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
9. Security Considerations . . . . . . . . . . . . . . . . . . . 19
10. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 19
11. References . . . . . . . . . . . . . . . . . . . . . . . . . . 20
11.1. Normative References . . . . . . . . . . . . . . . . . . . 20
11.2. Informative References . . . . . . . . . . . . . . . . . . 20
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 21
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1. Introduction
The aggregation of packet data into flows serves a variety of
different purposes, as noted in [RFC3917] and [RFC5472]. Aggregation
beyond the flow level, into records representing multiple Flows, is a
common analysis and data reduction technique as well, with
applicability to large-scale network data analysis, archiving, and
inter-organization exchange.
Aggregation is applicable to a wide variety of situations, including
traffic matrix calculation, generation of time series data for
visualizations or anomaly detection, and data reduction. Depending
on the keys used for aggregation, it may have an anonymising affect
on the data. Aggregation can take place at one of any number of
locations within a measurement infrastructure. Exporters may export
aggregated Flow information simply as normal flow information, by
performing aggregation after metering but before export. IPFIX
Mediators are particularly well suited to performing aggregation, as
they can collect information from multiple original exporters at
geographically and topologically distinct observation points.
Aggregation as defined and described in this document covers a
superset of the applications defined in the IPFIX Mediators Problem
Statement [RFC5982], including 5.1 "Adjusting Flow Granularity
(herein referred to as Key Aggregation), 5.4 "Time Composition"
(herein referred to as Interval Combination), and 5.5 "Spatial
Composition", although the architectural aspects of spatial
composition are not addressed by this document.
Since aggregated flows as defined in the following section are
essentially Flows, IPFIX can be used to export [RFC5101] and store
[RFC5655] aggregated data without further specification. However,
this document further provides a common basis for the application of
IPFIX to the handling of aggregated data, through a detailed
terminology, model of aggregation operations, methods for original
Flow counting and counter distribution across time intervals, and an
aggregation metadata representation based upon IPFIX Options.
2. Terminology
Terms used in this document that are defined in the Terminology
section of the IPFIX Protocol [RFC5101] document are to be
interpreted as defined there.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119].
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In addition, this document defines the following terms
Aggregated Flow: A Flow, as defined by [RFC5101], derived from a
set of zero or more original Flows within a defined time interval.
The two primary differences between a Flow and an Aggregated Flow
are (1) that the time interval of a Flow is generally derived from
information about the timing of the packets comprising the Flow,
while the time interval of an Aggregated Flow are generally
externally imposed; and (2) that an Aggregated Flow may represent
zero packets (i.e., an assertion that no packets were seen for a
given Flow Key in a given time interval).
(Intermediate) Aggregation Function: A mapping from a set of zero
or more original Flows into a set of aggregated Flow, that
separates the original Flows into a set of one or more given time
intervals.
(Intermediate) Aggregation Process: An Intermediate Process, as in
[I-D.ietf-ipfix-mediators-framework], hosting an Intermediate
Aggregation Function.
Aggregation Interval: A time interval imposed upon an Aggregated
Flow. Aggregation Functions commonly use a regular Aggregation
Interval (e.g. "every five minutes", "every calendar month"),
though regularity is not necessary.
original Flow: A Flow given as input to an Aggregation Function in
order to generate Aggregated Flows.
contributing Flow: An original Flow that is partially or completely
represented within an Aggregated Flow. Each aggregated Flow is
made up of zero or more contributing Flows, and an original flow
may contribute to zero or more Aggregated Flows.
3. Requirements for Aggregation Support in IPFIX
In defining a terminology, model, and metadata for Aggregated Flow
export using IPFIX, we have sought to meet the following
requirements.
First, a specification of Aggregated Flow export must seek to be as
interoperable as possible. Export of Aggregated Flows using the
techniques described in this document will result in Flow data which
can be collected by Collecting Processes and read by File Readers
which do not provide any special support for Aggregated Flow export.
Second, a specification of Aggregated Flow export must seek to be as
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implementation-independent as the IPFIX protocol itself. In
Section 6, we specify the flow aggregation process as an intermediate
process within the IPFIX Mediator framework
[I-D.ietf-ipfix-mediators-framework], and specify a variety of
different architectural arrangements for flow aggregation; these are
meant to be descriptive as opposed to proscriptive. In metadata
export, we seek to define properties of the set of exported
Aggregated Flows, as opposed to the properties of the specific
algorithms used to aggregate these Flows. Specifically out of scope
for this effort are any definition of a language for defining
aggregation operations, or the configuration parameters of
Aggregation Processes.
From the definition of presented in Section 2, an Aggregated Flow is
a Flow as in [RFC5101], with a restricted definition as to the
packets making up the Flow. Practically speaking, Aggregated Flows
are derived from original Flows, as opposed to a raw packet stream.
Key to this definition of Aggregated Flow is how timing affects the
process of aggregation, as for the most part flow aggregation takes
place within some set of (usually regular) time intervals. Any
specification for Aggregated Flow export must account for the special
role time intervals play in aggregation, and the many-to-many
relationship between Aggregated Flows and original Flows which this
implies.
4. Use Cases for IPFIX Aggregation
Aggregation, as a common data analysis method, has many applications.
When used with a regular Aggregation Interval, it generates time
series data from a collection of flows with discrete intervals. Time
series data is itself useful for a wide variety of analysis tasks,
such as generating parameters for network anomaly detection systems,
or driving visualizations of volume per time for traffic with
specific characteristics. Traffic matrix calculation from flow data
is inherently an aggregation action, by aggregating the flow key down
to interface, address prefix, or autonomous system.
Irregular or data-dependent Aggregation Intervals and Key Aggregation
operations can be also be used to provide adaptive aggregation of
network flow data, providing a higher-resolution view on data of
interest (e.g., potential attacks) to an application while providing
lower resolution to "less interesting" data (e.g., normal web
traffic). Indeed, this multiple-resolution approach can be applied
by a Mediator exporting unchanged original Flow data for the most
interesting flows alongside the Aggregated Flows of varying
resolution for the less interesting ones.
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Note that an aggregation operation which removes potentially
sensitive information as identified in [I-D.ietf-ipfix-anon] may tend
to have an anonymising effect on the Aggregated Flows, as well;
however, any application of aggregation as part of a data protection
scheme should ensure that all the issues raised in Section 4 of
[I-D.ietf-ipfix-anon] are addressed.
5. Aggregation of IP Flows
As stated in Section 2, an Aggregated Flow is simply an IPFIX Flow
generated from original Flows by an Aggregation Function. Here, we
present a general model for aggregation, and elaborate and provide
examples of specific aggregation operations that may be performed by
the Aggregation Process; we use this to define the export of
Aggregated Flows in Section 7
5.1. A general model for IP Flow Aggregation
An Intermediate Aggregation Process consumes original Flows and
exports Aggregated Flows, as defined in Section 2. While this
document does not define an implementation of an Intermediate
Aggregation Process further than this, or the Aggregation Functions
that it applies, it can be helpful to partially decompose this
function into a set of common operations, in order to more fully
examine the effects these operations have.
Aggregation is composed of three general types of operations on
original Flows: those that externally impose a time interval, called
here the Aggregation Interval; those that reduce or otherwise modify
the Flow Key; and those that aggregate and distribute the resulting
non-Flow Key fields accordingly. Most aggregation functions will
perform each of these types of operations.
Interval Distribution is the external imposition of a time interval
onto an original Flow. Note that this may lead to an original Flow
contributing to multiple aggregated Flows, if the original Flow's
time interval crosses at least one boundary between Aggregation
Intervals. Interval Distribution is described in more detail in
Section 5.2.
Key aggregation, the modification of Flow Keys, may occur in two
ways. First, the Flow Key may be projected: that is, Information
Elements may be removed from the Flow Key, or the space of values in
the Flow Key may be reduced. Second, derived Information Elements
may be added to the Flow Key. Both of these modifications may result
in multiple original Flows contributing to the same Aggregated Flow.
Key Aggregation is described in more detail in Section 5.3.
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Interval distribution and key aggregation together may generate
multiple intermediate aggregated Flows covering the same time
interval with the same Flow Key; these intermediate Flows must then
be combined into Aggregated Flows. Non-key values are first
distributed among the Aggregated Flows to which an original Flow
contributes according to some distribution algorithm (see
Section 5.4), and values from multiple contributing Flows are
combined using the same operation by which values are combined from
packets to form Flows for each Information Element: in general,
counters are added, averages are averaged, flags are unioned, and so
on. Aggregation may also introduce new non-key fields, e.g. per-flow
average counters, or distinct counters for key fields projected out
of the Aggregated Flow.
As a result of this final combination and distribution,an Aggregation
Function produces at most one Aggregated Flow resulting from a set of
original Flows for a given modified Flow Key and Aggregation
Interval.
This general model is illustrated in the figure below. Note that
interval and key field steps are commutative and optional, and as
such may occur in any order.
original Flows
|
V
+------------------------+
| Interval Distribution |<--- Aggregation Interval
+------------------------+
| (Flows with modified intervals)
V
+------------------------+
| Key Aggregation and |<--- specification of keys
| Key Field replacement |
+------------------------+
| (Flows with modified keys/intervals)
V (Addition of new non-key values)
+------------------------+
| Combination of |
| contributing Flows and |
| Counter Distribution |
+------------------------+
|
V
Aggregated Flows
Figure 1: Conceptual model of aggregation operations
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5.2. Interval Distribution
Interval Distribution generally imposes a regular interval on the
resulting Aggregated Flows; the selection of an interval is a matter
for the specific aggregation application, and has tradeoffs. Shorter
intervals allow higher resolution aggregated data and, in streaming
applications, faster reaction time. Longer intervals lead to greater
data reduction and simplified counter distribution. Specifically,
counter distribution is greatly simplified by the choice of an
interval longer than the duration of longest original Flow, itself
generally determined by the original Flow's Metering Process active
timeout; in this case an original Flow can contribute to at most two
Aggregated Flows, and the more exotic value distribution methods
become inapplicable.
Aggregation intervals, however, need not be regular. The aggregation
interval can be chosen, for example, based on time of day, or on the
relative volume of the original Flows, in order to adapt the
aggregation to the conditions on the measured network.
| | | |
| |<--flow A-->| | | |
| |<--flow B-->| | |
| |<-------------flow C-------------->| |
| | | |
| interval 0 | interval 1 | interval 2 |
Figure 2: Illustration of interval distribution
In Figure 2, we illustrate three common possibilities for interval
distribution. For flow A, the start and end times lie within the
boundaries of a single interval 0; therefore, flow A contributes to
only one Aggregated Flow. Flow B, by contrast, has the same duration
but crosses the boundary between intervals 0 and 1; therefore, it
will contribute to two Aggregated Flows, and its counters must be
distributed among these flows, though in the two-interval case this
can be simplified somewhat simply by picking one of the two
intervals, or proportionally distributing between them. Only flows
like flow A and flow B will be produced when the interval is chosen
to be longer than the duration of longest original Flow, as above.
More complicated is the case of flow C, which contributes to more
than two flows, and must have its counters distributed according to
some policy as in Section 5.4.
5.3. Key Aggregation
Key Aggregation modifies the Flow Key of the original Flows, through
projection, replacement, and augmentation. For example, consider
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original Flows with a flow key containing the traditional five-tuple
of source and destination address and port, and transport protocol.
Aggregating by host pair would project the Flow Key down by
eliminating port and protocol fields. Aggregating by source /24
network would project the Flow Key down to just the source address,
then further applying a prefix mask to the source address.
During aggregation, new Flow Key fields may be added to original
Flows, or Flow Key Fields may be replaced with ancillary values
derived from the Flow. To continue the example from above, consider
an aggregation operation for counting traffic per source autonomous
system. Here, the Flow Key would be projected down to just the
source address, and the source address would be replaced with the
source AS number, looked up in a table maintained by the intermediate
Aggregation Process.
Original Flow Key
+---------+---------+----------+----------+-------+-----+
| src ip4 | dst ip4 | src port | dst port | proto | tos |
+---------+---------+----------+----------+-------+-----+
| | | | | |
retain mask /24 X X X X
V V
+---------+-------------+
| src ip4 | dst ip4 /24 |
+---------+-------------+
Aggregated Flow Key (by source address and destination class-C)
Figure 3: Illustration of key aggregation by simple masking
Figure 3 illustrates an example projection operation, aggregation by
source address and destination class C network. Here, the port,
protocol, and type-of-service information is removed from the flow
key, the source address is retained, and the destination address is
masked by dropping the low 8 bits.
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Original Flow Key
+---------+---------+----------+----------+-------+-----+
| src ip4 | dst ip4 | src port | dst port | proto | tos |
+---------+---------+----------+----------+-------+-----+
| | | | | |
+-------------------+ X X X X
| ASN lookup table |
+-------------------+
V V
+---------+---------+
| src asn | dst asn |
+---------+---------+
Aggregated Flow Key (by source and dest ASN)
Figure 4: Illustration of key aggregation by replacement
Figure 4 illustrates an example projection operation with a
replacement function, aggregation by source and destination ASN
without ASN information available in the original Flow. Here, the
port, protocol, and type-of-service information is removed from the
flow key, while the source and destination addresses are run though
an IP address to ASN lookup table, and the Aggregated Flow key is
made up of the resulting source and destination ASNs.
5.4. Aggregating and Distributing Counters
In general, counters in Aggregated Flows are treated the same as in
any Flow: on a per-Information Element basis, the counters are
calculated as if they were derived from the set of packets in the
original flow. For the most part, when aggregating original Flows
into Aggregated Flows, this is simply done by summation.
When the Aggregation Interval is longer or much longer than the
longest original Flow, a Flow can cross at most one Interval
boundary, and will therefore contribute to at most two Aggregated
Flows. Most common in this case is to arbitrarily but consistently
choose to account the original Flow's counters either to the first or
the last aggregated Flow to which it could contribute.
However, this becomes more complicated when the Aggregation Interval
is shorter than the longest original Flow in the source data. In
such cases, each original Flow can incompletely cover one or more
time intervals, and apply to one or more Aggregated Flows; in this
case, the Aggregation Process must distribute the counters in the
original Flows across the multiple Aggregated Flows. There are
several methods for doing this, listed here in increasing order of
complexity and accuracy.
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End Interval: The counters for an original Flow are added to the
counters of the appropriate Aggregated Flow containing the end
time of the original Flow.
Start Interval: The counters for an original Flow are added to the
counters of the appropriate Aggregated Flow containing the start
time of the original Flow.
Mid Interval: The counters for an original Flow are added to the
counters of a single appropriate Aggregated Flow containing some
timestamp between start and end time of the original Flow.
Simple Uniform Distribution: Each counter for an original Flow is
divided by the number of time intervals the original Flow covers
(i.e., of appropriate Aggregated Flows sharing the same Flow Key),
and this number is added to each corresponding counter in each
Aggregated Flow.
Proportional Uniform Distribution: Each counter for an original
Flow is divided by the number of time _units_ the original Flow
covers, to derive a mean count rate. This mean count rate is then
multiplied by the number of time units in the intersection of the
duration of the original Flow and the time interval of each
Aggregated Flow. This is like simple uniform distribution, but
accounts for the fractional portions of a time interval covered by
an original Flow in the first and last time interval.
Simulated Process: Each counter of the original Flow is distributed
among the intervals of the Aggregated Flows according to some
function the Aggregation Process uses based upon properties of
Flows presumed to be like the original Flow. For example, bulk
transfer flows might follow a more or less proportional uniform
distribution, while interactive processes are far more bursty.
Direct: The Aggregation Process has access to the original packet
timings from the packets making up the original Flow, and uses
these to distribute or recalculate the counters.
A method for exporting the distribution of counters across multiple
Aggregated Flows is detailed in Section 7.3. In any case, counters
MUST be distributed across the multiple Aggregated Flows in such a
way that the total count is preserved, within the limits of accuracy
of the implementation (e.g., inaccuracy introduced by the use of
floating-point numbers is tolerable). This property allows data to
be aggregated and re-aggregated without any loss of original count
information. To avoid confusion in interpretation of the aggregated
data, all the counters for a set of given original Flows SHOULD be
distributed via the same method.
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5.5. Counting Original Flows
When aggregating multiple original Flows into an Aggregated Flow, it
is often useful to know how many original Flows are present in the
Aggregated Flow. This document introduces four new information
elements in Section 7.2 to export these counters.
There are two possible ways to count original Flows, which we call
here conservative and non-conservative. Conservative flow counting
has the property that each original Flow contributes exactly one to
the total flow count within a set of aggregated Flows. In other
words, conservative flow counters are distributed just as any other
counter, except each original Flow is assumed to have a flow count of
one. When a count for an original Flow must be distributed across a
set of Aggregated Flows, and a distribution method is used which does
not account for that original Flow completely within a single
Aggregated Flow, conservative flow counting requires a fractional
representation.
By contrast, non-conservative flow counting is used to count how many
flows are represented in an Aggregated Flow. Flow counters are not
distributed in this case. An original Flow which is present within N
Aggregated Flows would add N to the sum of non-conservative flow
counts, one to each Aggregated Flow. In other words, the sum of
conservative flow counts over a set of Aggregated Flows is always
equal to the number of original Flows, while the sum of non-
conservative flow counts is strictly greater than or equal to the
number of original Flows.
For example, consider flows A, B, and C as illustrated in Figure 2.
Assume that the key aggregation step aggregates the keys of these
three flows to the same aggregated flow key, and that start interval
counter distribution is in effect. The conservative flow count for
interval 0 is 3 (since flows A, B, and C all begin in this interval),
and for the other two intervals is 0. The non-conservative flow
count for interval 0 is also 3 (due to the presence of flows A, B,
and C), for interval 1 is 2 ( flows B and C), and for interval 2 is 1
(flow 0). The sum of the conservative counts 3 + 0 + 0 = 3, the
number of original Flows; while the sum of the non-conservative
counts 3 + 2 + 1 = 6.
5.6. Counting Distinct Key Values
One common case in aggregation is counting distinct values that were
projected out during key aggregation. For example, consider an
application counting destinations contacted per host, a common case
in host characterization or anomaly detection. Here, the Aggregation
Process needs a way to export this distinct key count information.
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For such applications, a distinctCountOf(key name) Information
Element should be registered with IANA to represent these cases.
[EDITOR'S NOTE: There is an open question as to the best way to do
this: either through the registration of Information Elements for
common cases in this draft, the registration of Information Elements
on demand, or the definition of a new Information Element space for
distinct counts bound to a PEN, as in [RFC5103].]
5.7. Exact versus Approximate Counting during Aggregation
In certain circumstances, particularly involving aggregation by
devices with limited resources, and in situations where exact
aggregated counts are less important than relative magnitudes (e.g.
driving graphical displays), counter distribution during key
aggregation may be performed by approximate counting means (e.g.
Bloom filters).
5.8. Interval Combination
One special case of aggregation uses adaptive Aggregation Intervals
without any projection in order to join long-lived Flows which may
have been split (e.g., due to an active timeout shorter than the
Flow.) This is referred to as "Time Composition" in section 5.4 of
[RFC5982]. Here, the Flow Key is unmodified, and the Aggregation
Interval is chosen on a per-Flow basis to cover the interval spanned
by the set of aggregated Flows. This may be applied alone in order
to normalize split Flows, or in combination with other aggregation
functions in order to obtain more accurate original Flow counts.
6. Aggregation in the IPFIX Architecture
The techniques described in this document can be applied to IPFIX
data at three stages within the collection infrastructure: on initial
export, within a mediator, or after collection, as shown in Figure 5.
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+==========================================+
| Exporting Process |
+==========================================+
| |
| (Aggregated Flow Export) |
V |
+=============================+ |
| Mediator | |
+=============================+ |
| |
| (Aggregating Mediator) |
V V
+==========================================+
| Collecting Process |
+==========================================+
|
| (Aggregation for Storage)
V
+--------------------+
| IPFIX File Storage |
+--------------------+
Figure 5: Potential Aggregation Locations
Aggregation can be applied for either intermediate or final analytic
purposes. In certain circumstances, it may make sense to export
Aggregated Flows from an Exporting Process, for example, if the
Exporting Process is designed to drive a time-series visualization
directly. Note that this case, where the Aggregation Process is
essentially integrated into the Metering Process, is essentially
covered by the IPFIX architecture [RFC5470]: the flow keys used are
simply a subset of those that would normally be used. A Metering
Process in this arrangement MAY choose to simulate the generation of
larger flows in order to generate original flow counts, if the
application calls for compatibility with an Aggregation Process
deployed in a separate location.
Deployment of an Intermediate Aggregation Process within a Mediator
[RFC5982] is a much more flexible arrangement. Here, the Mediator
consumes original Flows and produces aggregated Flows; this
arrangement is suited to any of the use cases detailed in Section 4.
In a mediator, aggregation can be applied as well to aggregating
original Flows from multiple sources into a single stream of
aggregated Flows; the architectural specifics of this arrangement are
not addressed in this document, which is concerned only with the
aggregation operation itself; see
[I-D.claise-ipfix-mediation-protocol] for details.
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In the specific case that an Aggregation Process is employed for data
reduction for storage purposes, it can take original Flows from a
Collecting Process or File Reader and pass Aggregated Flows to a File
Writer for storage.
The data flows into and out of an Intermediate Aggregation Process
are showin in Figure 6.
packets --+ +- IPFIX Messages -+
| | |
V V V
+==================+ +====================+ +=============+
| Metering Process | | Collecting Process | | File Reader |
| | +====================+ +=============+
| | | original Flows |
| | V V
+ - - - - - - - - -+======================================+
| Intermediate Aggregation Process (IAP) |
+=========================================================+
| Aggregated Aggregated |
| Flows Flows |
V V
+===================+ +=============+
| Exporting Process | | File Writer |
+===================+ +=============+
| |
+------------> IPFIX Messages <----------+
Figure 6: Data flows through the aggregation process
7. Export of Aggregated IP Flows using IPFIX
In general, Aggregated Flows are exported in IPFIX as any normal
Flow. However, certain aspects of aggregated flow export benefit
from additional guidelines, or new Information Elements to represent
aggregation metadata or information generated during aggregation.
These are detailed in the following subsections.
7.1. Time Interval Export
Since an Aggregated Flow is simply a Flow, the existing timestamp
Information Elements in the IPFIX Information Model (e.g.,
flowStartMilliseconds, flowEndNanoseconds) are sufficient to specify
the time interval for aggregation. Therefore, this document
specifies no new aggregation-specific Information Elements for
exporting time interval information.
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Each Aggregated Flow SHOULD contain both an interval start and
interval end timestamp. If an exporter of Aggregated Flows omits the
interval end timestamp from each Aggregated Flow, the time interval
for Aggregated Flows within an Observation Domain and Transport
Session MUST be regular and constant. However, note that this
approach might lead to interoperability problems when exporting
Aggregated Flows to non-aggregation-aware Collecting Processes and
downstream analysis tasks; therefore, an Exporting Process capable of
exporting only interval start timestamps MUST provide a configuration
option to export interval end timestamps as well.
7.2. Flow Count Export
The following four Information Elements are defined to count original
Flows as discussed in Section 5.5.
7.2.1. originalFlowsPresent Information Element
Description: The non-conservative count of original Flows
contributing to this Aggregated Flow. Non-conservative counts
need not sum to the original count on re-aggregation.
Abstract Data Type: unsigned64
ElementId: TBD1
Status: Proposed
7.2.2. originalFlowsInitiated InformationElement
Description: The conservative count of original Flows whose first
packet is represented within this Aggregated Flow. Conservative
counts must some to the original count on re-aggregation.
Abstract Data Type: unsigned64
ElementId: TBD2
Status: Proposed
7.2.3. originalFlowsCompleted InformationElement
Description: The conservative count of original Flows whose last
packet is represented within this Aggregated Flow. Conservative
counts must some to the original count on re-aggregation.
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Abstract Data Type: unsigned64
ElementId: TBD3
Status: Proposed
7.2.4. originalFlows InformationElement
Description: The conservative count of original Flows contributing
to this Aggregated Flow; may be distributed via any of the methods
described in Section 5.4.
Abstract Data Type: float64
ElementId: TBD4
Status: Proposed
7.3. Aggregate Counter Distibution Export
When exporting counters distributed among Aggregated Flows, as
described in Section 5.4, the Exporting Process MAY export an
Aggregate Counter Distribution Record for each Template describing
Aggregated Flow records; this Options Template is described below.
It uses the valueDistributionMethod Information Element, also defined
below. Since in many cases distribution is simple, accounting the
counters from contributing Flows to the first Interval to which they
contribute, this is default situation, for which no Aggregate Counter
Distribution Record is necessary; Aggregate Counter Distribution
Records are only applicable in more exotic situations, such as using
an Aggregation Interval smaller than the durations of original Flows.
7.3.1. Aggregate Counter Distribution Options Template
This Options Template defines the Aggregate Counter Distribution
Record, which allows the binding of a value distribution method to a
Template ID. This is used to signal to the Collecting Process how
the counters were distributed. The fields are as below:
+-------------------------+-----------------------------------------+
| IE | Description |
+-------------------------+-----------------------------------------+
| templateId [scope] | The Template ID of the Template |
| | defining the Aggregated Flows to which |
| | this distribution option applies. This |
| | Information Element MUST be defined as |
| | a Scope Field. |
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| valueDistributionMethod | The method used to distribute the |
| | counters for the Aggregated Flows |
| | defined by the associated Template. |
+-------------------------+-----------------------------------------+
7.3.2. valueDistributionMethod Information Element
Description: A description of the method used to distribute the
counters from contributing Flows into the Aggregated Flow records
described by an associated Template. The method is deemed to
apply to all the non-key Information Elements in the referenced
Template for which value distribution is a valid operation; if the
originalFlowsInitiated and/or originalFlowsCompleted Information
Elements appear in the Template, they are not subject to this
distribution method, as they each infer their own distribution
method. The distribution methods are taken from Section 5.4 and
encoded as follows:
+-------+-----------------------------------------------------------+
| Value | Description |
+-------+-----------------------------------------------------------+
| 1 | Start Interval: The counters for an original Flow are |
| | added to the counters of the appropriate Aggregated Flow |
| | containing the start time of the original Flow. This |
| | should be assumed the default if value distribution |
| | information is not available at a Collecting Process for |
| | an Aggregated Flow. |
| 2 | End Interval: The counters for an original Flow are added |
| | to the counters of the appropriate Aggregated Flow |
| | containing the end time of the original Flow. |
| 3 | Mid Interval: The counters for an original Flow are added |
| | to the counters of a single appropriate Aggregated Flow |
| | containing some timestamp between start and end time of |
| | the original Flow. |
| 4 | Simple Uniform Distribution: Each counter for an original |
| | Flow is divided by the number of time intervals the |
| | original Flow covers (i.e., of appropriate Aggregated |
| | Flows sharing the same Flow Key), and this number is |
| | added to each corresponding counter in each Aggregated |
| | Flow. |
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| 5 | Proportional Uniform Distribution: Each counter for an |
| | original Flow is divided by the number of time _units_ |
| | the original Flow covers, to derive a mean count rate. |
| | This mean count rate is then multiplied by the number of |
| | time units in the intersection of the duration of the |
| | original Flow and the time interval of each Aggregated |
| | Flow. This is like simple uniform distribution, but |
| | accounts for the fractional portions of a time interval |
| | covered by an original Flow in the first and last time |
| | interval. |
| 6 | Simulated Process: Each counter of the original Flow is |
| | distributed among the intervals of the Aggregated Flows |
| | according to some function the Aggregation Process uses |
| | based upon properties of Flows presumed to be like the |
| | original Flow. This is essentially an assertion that the |
| | Aggregation Process has no direct packet timing |
| | information but is nevertheless not using one of the |
| | other simpler distribution methods. The Aggregation |
| | Process specifically makes no assertion as to the |
| | correctness of the simulation. |
| 7 | Direct: The Aggregation Process has access to the |
| | original packet timings from the packets making up the |
| | original Flow, and uses these to distribute or |
| | recalculate the counters. |
+-------+-----------------------------------------------------------+
Abstract Data Type: unsigned8
ElementId: TBD5
Status: Proposed
8. Examples
[TODO]
9. Security Considerations
[TODO]
10. IANA Considerations
[TODO: add all IEs defined in Section 6.]
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11. References
11.1. Normative References
[RFC5101] Claise, B., "Specification of the IP Flow Information
Export (IPFIX) Protocol for the Exchange of IP Traffic
Flow Information", RFC 5101, January 2008.
[RFC5102] Quittek, J., Bryant, S., Claise, B., Aitken, P., and J.
Meyer, "Information Model for IP Flow Information Export",
RFC 5102, January 2008.
11.2. Informative References
[RFC5103] Trammell, B. and E. Boschi, "Bidirectional Flow Export
Using IP Flow Information Export (IPFIX)", RFC 5103,
January 2008.
[RFC5470] Sadasivan, G., Brownlee, N., Claise, B., and J. Quittek,
"Architecture for IP Flow Information Export", RFC 5470,
March 2009.
[RFC5472] Zseby, T., Boschi, E., Brownlee, N., and B. Claise, "IP
Flow Information Export (IPFIX) Applicability", RFC 5472,
March 2009.
[RFC5153] Boschi, E., Mark, L., Quittek, J., Stiemerling, M., and P.
Aitken, "IP Flow Information Export (IPFIX) Implementation
Guidelines", RFC 5153, April 2008.
[RFC5610] Boschi, E., Trammell, B., Mark, L., and T. Zseby,
"Exporting Type Information for IP Flow Information Export
(IPFIX) Information Elements", RFC 5610, July 2009.
[RFC5655] Trammell, B., Boschi, E., Mark, L., Zseby, T., and A.
Wagner, "Specification of the IP Flow Information Export
(IPFIX) File Format", RFC 5655, October 2009.
[RFC5982] Kobayashi, A. and B. Claise, "IP Flow Information Export
(IPFIX) Mediation: Problem Statement", RFC 5982,
August 2010.
[RFC3917] Quittek, J., Zseby, T., Claise, B., and S. Zander,
"Requirements for IP Flow Information Export (IPFIX)",
RFC 3917, October 2004.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
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[I-D.ietf-ipfix-anon]
Boschi, E. and B. Trammell, "IP Flow Anonymisation
Support", draft-ietf-ipfix-anon-03 (work in progress),
April 2010.
[I-D.ietf-ipfix-mediators-framework]
Kobayashi, A., Claise, B., Muenz, G., and K. Ishibashi,
"IPFIX Mediation: Framework",
draft-ietf-ipfix-mediators-framework-08 (work in
progress), August 2010.
[I-D.claise-ipfix-mediation-protocol]
Claise, B., Kobayashi, A., and B. Trammell, "Specification
of the Protocol for IPFIX Mediations",
draft-claise-ipfix-mediation-protocol-01 (work in
progress), March 2010.
Authors' Addresses
Brian Trammell
Swiss Federal Institute of Technology Zurich
Gloriastrasse 35
8092 Zurich
Switzerland
Phone: +41 44 632 70 13
Email: trammell@tik.ee.ethz.ch
Elisa Boschi
Swiss Federal Institute of Technology Zurich
Gloriastrasse 35
8092 Zurich
Switzerland
Email: boschie@tik.ee.ethz.ch
Arno Wagner
Consecom AG
Bellariastrasse 11
8002 Zurich
Switzerland
Email: arno@wagner.name
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