One document matched: draft-ietf-ipfix-a9n-02.xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE rfc SYSTEM "rfc2629.dtd">
<rfc ipr="trust200902" category="std" docName="draft-ietf-ipfix-a9n-02.txt">
<?rfc compact="yes"?>
<?rfc subcompact="no"?>
<?rfc toc="yes"?>
<?rfc symrefs="yes"?>
<front>
<title abbrev="IPFIX Aggregation">
Flow Aggregation for the IP Flow Information Export (IPFIX) Protocol
</title>
<author initials="B." surname="Trammell" fullname="Brian Trammell">
<organization abbrev="ETH Zurich">
Swiss Federal Institute of Technology Zurich
</organization>
<address>
<postal>
<street>Gloriastrasse 35</street>
<city>8092 Zurich</city>
<country>Switzerland</country>
</postal>
<phone>+41 44 632 70 13</phone>
<email>trammell@tik.ee.ethz.ch</email>
</address>
</author>
<!--
<author initials="E." surname="Boschi" fullname="Elisa Boschi">
<organization abbrev="ETH Zurich">
Swiss Federal Institute of Technology Zurich
</organization>
<address>
<postal>
<street>Gloriastrasse 35</street>
<city>8092 Zurich</city>
<country>Switzerland</country>
</postal>
<email>boschie@tik.ee.ethz.ch</email>
</address>
</author>
-->
<author initials="A." surname="Wagner" fullname="Arno Wagner">
<organization abbrev="Consecom AG">
Consecom AG
</organization>
<address>
<postal>
<street>Bleicherweg 64a</street>
<city>8002 Zurich</city>
<country>Switzerland</country>
</postal>
<email>arno@wagner.name</email>
</address>
</author>
<author initials="B." surname="Claise" fullname="Benoit Claise">
<organization abbrev="Cisco Systems, Inc.">
Cisco Systems, Inc.
</organization>
<address>
<postal>
<street>De Kleetlaan 6a b1</street>
<city>1831 Diagem</city>
<country>Belgium</country>
</postal>
<phone>+32 2 704 5622</phone>
<email>bclaise@cisco.com</email>
</address>
</author> <date month="February" day="27" year="2012"></date>
<area>Operations</area>
<workgroup>IPFIX Working Group</workgroup>
<abstract>
<t>This document describes the export of aggregated Flow information using
IPFIX. An Aggregated Flow is essentially an IPFIX Flow representing
packets from multiple Original Flows sharing some set of common
properties. The document describes Aggregated Flow export within the
framework of IPFIX Mediators and defines an interoperable,
implementation-independent method for Aggregated Flow export.</t>
</abstract>
</front>
<middle>
<section title="Introduction">
<t>The assembly of packet data into Flows serves a variety of
different purposes, as noted in the <xref
target="RFC3917">requirements</xref> and <xref
target="RFC5472">applicability statement</xref> for the IP Flow
Information Export (IPFIX) <xref target="I-D.ietf-ipfix-protocol-rfc5101bis">protocol</xref>.
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. This applicability in large-scale
situations, in particular, led to the inclusion of aggregation as part
of the <xref target="RFC5982">IPFIX Mediators Problem
Statement</xref>, and the definition of an Intermediate Aggregation
Process in the <xref target="RFC6183">Mediator framework</xref>.</t>
<t>Aggregation is part of a wide variety of applications, including
traffic matrix calculation, generation of time series data for
visualizations or anomaly detection, or measurement data reduction.
Depending on the keys used for aggregation, it may additionally have
an anonymizing affect on the data: for example, aggregation operations
which eliminate IP addresses make it impossible to later identify
nodes using those addresses.</t>
<t>Aggregation as defined and described in this document covers the
applications defined in <xref target="RFC5982"/>, including 5.1
"Adjusting Flow Granularity", 5.4 "Time Composition", and 5.5 "Spatial
Composition". However, this document specifies a more flexible
architecture for an Intermediate Aggregation Process than that
envisioned by the original Mediator work, in <xref
target="sec-iap-arch"/>. Instead of a focus on these specific limited
use cases, the Intermediate Aggregation Process is specified to cover
any activity commonly described as "flow aggregation".</t>
<t>An Intermediate Aggregation Process may be applied to data
collected from multiple Observation Points, as aggregation is natural
to apply for data reduction when concentrating measurement data. This
document specifically does not address the protocol issues that arise
when combining IPFIX data from multiple Observation Points and
exporting from a single Mediator, as these issues are general to IPFIX
Mediation; they are therefore treated in detail in the <xref
target="I-D.ietf-ipfix-mediation-protocol">Mediator Protocol</xref>
document.</t>
<t>Since Aggregated Flows as defined in the following section are
essentially Flows, the IPFIX protocol <xref target="I-D.ietf-ipfix-protocol-rfc5101bis"/> can be
used to export, and the IPFIX File Format <xref target="RFC5655"/> can
be used to store, aggregated data "as-is"; there are no changes
necessary to the protocol. This document provides a common basis for
the application of IPFIX to the handling of aggregated data, through a
detailed terminology, Intermediate Aggregation Process architecture,
and methods for Original Flow counting and counter distribution across
intervals.</t>
<section title="IPFIX Protocol Overview">
<!-- <t>[EDITOR'S NOTE: this subsection is included at Benoit's suggestion; however, they may duplicate too much content from the main section, and should be reviewed.]</t> -->
<t>In the IPFIX protocol, { type, length, value } tuples are expressed
in templates containing { type, length } pairs, specifying which {
value } fields are present in data records conforming to the Template,
giving great flexibility as to what data is transmitted. Since
Templates are sent very infrequently compared with Data Records, this
results in significant bandwidth savings. Various different data
formats may be transmitted simply by sending new Templates specifying
the { type, length } pairs for the new data format. See <xref
target="I-D.ietf-ipfix-protocol-rfc5101bis"></xref> for more information.</t>
<t>The <xref target="iana-ipfix-assignments">IPFIX Information Element Registry</xref> defines a
large number of standard Information Elements which provide the
necessary { type } information for Templates. The use of standard
elements enables interoperability among different vendors'
implementations. Additionally, non-standard enterprise-specific
elements may be defined for private use.</t>
</section>
<section title="IPFIX Documents Overview" anchor="intro-docs">
<!-- <t>[EDITOR'S NOTE: this subsection is included at Benoit's suggestion; however, they may duplicate too much content from the main section, and should be reviewed.]</t> -->
<t><xref target="I-D.ietf-ipfix-protocol-rfc5101bis">"Specification of the IPFIX Protocol for the
Exchange of IP Traffic Flow Information"</xref> and its associated
documents define the IPFIX Protocol, which provides network engineers
and administrators with access to IP traffic flow information.</t>
<t><xref target="RFC5470">"Architecture for IP Flow Information
Export"</xref> defines the architecture for the export of measured IP
flow information out of an IPFIX Exporting Process to an IPFIX
Collecting Process, and the basic terminology used to describe the
elements of this architecture, per the requirements defined in <xref
target="RFC3917">"Requirements for IP Flow Information Export"</xref>.
The IPFIX Protocol document <xref target="I-D.ietf-ipfix-protocol-rfc5101bis"></xref> then covers
the details of the method for transporting IPFIX Data Records and
Templates via a congestion-aware transport protocol from an IPFIX
Exporting Process to an IPFIX Collecting Process.</t>
<t>This document specifies an Intermediate Process which may be
applied at an IPFIX Mediator. <xref target="RFC5982">"IP Flow
Information Export (IPFIX) Mediation: Problem Statement"</xref>
introduces the concept of IPFIX Mediators, and defines the use cases
for which they were designed; <xref target="RFC6183">"IP Flow
Information Export (IPFIX) Mediation: Framework"</xref> then provides
an architectural framework for Mediators. Protocol-level issues (e.g.,
template and observation domain handling across Mediators) are covered
by <xref target="I-D.ietf-ipfix-mediation-protocol">"Specification
of the Protocol for IPFIX Mediation"</xref>.</t>
</section>
</section>
<section title="Terminology" anchor="sec-terminology">
<t>Terms used in this document that are defined in the Terminology
section of the <xref target="I-D.ietf-ipfix-protocol-rfc5101bis">IPFIX Protocol</xref> document
are to be interpreted as defined there.</t>
<t>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 <xref
target="RFC2119"/>.</t>
<t>In addition, this document defines the following terms</t>:
<t><list style="hanging">
<t hangText="Aggregated Flow: ">A Flow, as defined by <xref
target="I-D.ietf-ipfix-protocol-rfc5101bis"/>, derived from a set of zero or more original
Flows within a defined Aggregation Interval. The two primary
differences between a Flow and an Aggregated Flow in the general
case are (1) that the time interval (i.e., the two-tuple of start
and end times) of a Flow is derived from information about the
timing of the packets comprising the Flow, while the time interval
of an Aggregated Flow are 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). Note that an Aggregated Flow is defined in the context
of an Intermediate Aggregation Process only. Once an Aggregated
Flow is exported, it is essentially a Flow as in <xref
target="I-D.ietf-ipfix-protocol-rfc5101bis"/> and can be treated as such.</t>
<t hangText="Intermediate Aggregation Process: ">an Intermediate
Process as in <xref target="RFC6183"/> that aggregates records,
based upon a set of Flow Keys or functions applied to fields from
the record.</t>
<t hangText="Aggregation Interval: ">A time interval imposed upon
an Aggregated Flow. Intermediate Aggregation Processes may use a
regular Aggregation Interval (e.g. "every five minutes", "every
calendar month"), though regularity is not necessary. Aggregation
intervals may also be derived from the time intervals of the
Original Flows being aggregated.</t>
<t hangText="Partially Aggregated Flow: ">A Flow during processing
within an Intermediate Aggregation Process; refers to an
intermediate data structure during aggregation within the
Intermediate Aggregation Process architecture detailed in <xref
target="sec-iap-arch"/>.</t>
<t hangText="Original Flow: ">A Flow given as input to an
Intermediate Aggregation Process in order to generate Aggregated
Flows.</t>
<t hangText="Contributing Flow: ">An Original Flow that is
partially or completely represented within an Aggregated Flow.
Each Contributing Flow is made up of zero or more Contributing
Flows, and an Original Flow may contribute to zero or more
Aggregated Flows.</t>
<t hangText="Original Exporter: ">When the Intermediate
Aggregation Process is hosted in an IPFIX Mediator, the Original
Exporter is the Exporter from which the Original Flows are
received.</t>
</list></t>
<t>The terminology presented herein improves the precision of, but
does not supersede or contradict the terms related to mediation and
aggregation defined in the <xref target="RFC5982">problem
statement</xref> and the <xref target="RFC6183">framework</xref>
documents. Within this document, the terminology defined in this
section is to be considered normative.</t>
</section>
<section title="Use Cases for IPFIX Aggregation" anchor="sec-usecase">
<t>Aggregation, as a common data reduction method used in traffic data
analysis, has many applications. When used with a regular Aggregation
Interval, it generates time series data from a collection of Flows
with discrete intervals. This time series data is itself useful for a
wide variety of analysis tasks, such as generating input for network
anomaly detection systems, or driving visualizations of volume per
time for traffic with specific characteristics. As a second example,
traffic matrix calculation from flow data is inherently an aggregation
action, by aggregating the Flow Key down to input or output interface,
address prefix, or autonomous system.</t>
<t>Irregular or data-dependent Aggregation Intervals and key
aggregation operations can also be used to provide adaptive
aggregation of network flow data. Here, full Flow Records can be kept
for Flows of interest, while Flows deemed "less interesting" to a
given application can be aggregated. For example, in an IPFIX Mediator
equipped with traffic classification capabilities for security
purposes, potentially malicious Flows could be exported directly,
while known-good or probably-good Flows (e.g. normal web browsing)
could be exported simply as time series volumes per web server.</t>
<t>Note that an Intermediate Aggregation Process which removes
potentially sensitive information as identified in <xref
target="RFC6235"/> 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 <xref target="RFC6235"/> are addressed, specifically Section
4 "Anonymization of IP Flow Data", Section 7.2 "IPFIX-Specific
Anonymization Guidelines", and Section 9 "Security
Considerations".</t>
<t>While much of the discussion in this document, and all of the examples,
apply to the common case that the Original Flows to be aggregated are all of
the same underlying type (i.e., are represented with identical or compatible
Templates), and that each packet observed by the Metering Process on the far
side of the Original Exporter is represented, this is not a necessary
assumption. Aggregation can also be applied as part of a technique applying
both aggregation and correlation to pull together multiple views of the same
traffic from different Observation Points using different Templates. For
example, consider a set of applications running at different Observation
Points for different purposes -- one generating flows with round-trip-times
for passive performance measurement, and one generating billing records. Once
correlated, these flows could used to produce Aggregated Flows containing
both volume and performance information together. The correlation and
normalization operation described in <xref target="sec-iap-arch-correl"/>
handles this specific case of correlation. Flow correlation in the general
case is outside the scope of this document.</t>
</section>
<section title="Architecture for Flow Aggregation">
<t>This section specifies how an Intermediate Aggregation Process fits into the IPFIX Architecture, and the architecture of the Intermediate Aggregation Process itself.</t>
<section title="Aggregation within the IPFIX Architecture" anchor="sec-arch">
<t>An Intermediate Aggregation Process could be deployed at any of three places within the IPFIX Architecture. While aggregation is most commonly done within a Mediator which collects Original Flows from an Original Exporter and exports Aggregated Flows, aggregation can also occur before initial export, or after final collection, as shown in <xref target="loc-fig"/>. The presence of an IAP at any of these points is of course optional.</t>
<figure title="Potential Aggregation Locations" anchor="loc-fig">
<artwork><![CDATA[
+===========================================+
| IPFIX Exporter +----------------+ |
| | Metering Proc. | |
| +-----------------+ +----------------+ |
| | Metering Proc. | or | IAP | |
| +-----------------+----+----------------+ |
| | Exporting Process | |
| +-|----------------------------------|--+ |
+===|==================================|====+
| |
| (Aggregated Flow Export) |
| |
+===|===========================+ |
| | Mediator | |
+ +-V-------------------+ | |
| | Collecting Process | | |
+ +---------------------+ | |
| | IAP | | |
+ +---------------------+ | |
| | Exporting Process | | |
+ +-|-------------------+ | |
+===|===========================+ |
| |
| (Aggregating Mediator) |
| |
+===|==================================|=====+
| | Collector | |
| +-V----------------------------------V-+ |
| | Collecting Process | |
| +------------------+-------------------+ |
| | IAP | |
| +-------------------+ |
| | File Writer | |
| +-----------|-------+ |
+================================|===========+
|
(Aggregation for Storage) |
V
+------------------+
| IPFIX File |
+------------------+
]]></artwork>
</figure>
<t>The Mediator use case is further shown in Figures A and B in <xref
target="RFC6183"/>.</t>
<t>Aggregation can be applied for either intermediate or final
analytic purposes. In certain circumstances, it may make sense to
export Aggregated Flows directly from an original Exporting Process,
for example, if the Exporting Process is applied to drive a
time-series visualization, or when flow data export bandwidth is
restricted and flow or packet sampling is not an option. Note that
this case, where the Aggregation Process is essentially integrated
into the Metering Process, is essentially covered by the <xref
target="RFC5470">IPFIX architecture</xref>: the Flow Keys used are
simply a subset of those that would normally be used, and time
intervals may be chosen other than those available from the cache
policies customarily offered by the Metering Process. 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.</t>
<t>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.</t>
<t>Deployment of an Intermediate Aggregation Process within a <xref
target="RFC5982">Mediator</xref> is a much more flexible arrangement.
Here, the Mediator consumes Original Flows and produces Contributing
Flows; this arrangement is suited to any of the use cases detailed in
<xref target="sec-usecase"/>. 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 <xref
target="I-D.ietf-ipfix-mediation-protocol"/> for details.</t>
<t>The data paths into and out of an Intermediate Aggregation Process
are shown in <xref target="iap-dataflows"/>.</t>
<figure title="Data paths through the aggregation process" anchor="iap-dataflows">
<artwork><![CDATA[
packets --+ +- IPFIX Messages -+
| | |
V V V
+==================+ +====================+ +=============+
| Metering Process | | Collecting Process | | File Reader |
| | +====================+ +=============+
| (Original Flows | | Original Flows |
| or direct aggr.)| V V
+ - - - - - - - - -+======================================+
| Intermediate Aggregation Process (IAP) |
+=========================================================+
| Aggregated Aggregated |
| Flows Flows |
V V
+===================+ +=============+
| Exporting Process | | File Writer |
+===================+ +=============+
| |
+------------> IPFIX Messages <----------+
]]></artwork>
</figure>
<t>Aggregation may also need to correlate original flows from multiple Metering Processes, each according to a different Template with different Flow Keys and values. This arrangement is shown in <xref target="iap-correl-dataflows"/>; in this case, the correlation and normalization operation described in <xref target="sec-iap-arch-correl"/> handles merging the Original Flows before </t>
<figure title="Aggregating Original Flows from multiple Metering Processes" anchor="iap-correl-dataflows">
<artwork><![CDATA[
packets --+---------------------+------------------+
| | |
V V V
+====================+ +====================+ +====================+
| Metering Process 1 | | Metering Process 2 | | Metering Process n |
+====================+ +====================+ +====================+
| | Original Flows |
V V V
+==================================================================+
| Intermediate Aggregation Process + correlation / normalization |
+==================================================================+
| Aggregated Aggregated |
| Flows Flows |
V V
+===================+ +=============+
| Exporting Process | | File Writer |
+===================+ +=============+
| |
+------------> IPFIX Messages <----------+
]]></artwork>
</figure>
</section>
<section title="Intermediate Aggregation Process Architecture" anchor="sec-iap-arch">
<t>Within this document, an Intermediate Aggregation Process can be
seen as hosting a function composed of four types of operations on
Partially Aggregated Flows, as illustrated in <xref
target="iap-arch-diagram"/>. "Partially Contributing Flows" as defined
in <xref target="sec-terminology"/> are essentially the intermediate
results of aggregation, internal to the Intermediate Aggregation
Process.</t>
<figure title="Conceptual model of aggregation operations within an IAP" anchor="iap-arch-diagram"><artwork><![CDATA[
Original Flows / Original Flows requiring correlation
+=============|===================|===================|=============+
| | Intermediate | Aggregation | Process |
| | V V |
| | +-----------------------------------------------+ |
| | | (optional) correlation and normalization | |
| | +-----------------------------------------------+ |
| | | |
| V V |
| +--------------------------------------------------------------+ |
| | interval distribution (temporal) | |
| +--------------------------------------------------------------+ |
| | ^ | ^ | |
| | | Partially Aggregated | | Flows | |
| V | V | | |
| +-------------------+ +--------------------+ | |
| | key aggregation |<------| value aggregation | | |
| | (spatial) |------>| (spatial) | | |
| +-------------------+ +--------------------+ | |
| | | | |
| | Partially Aggregated | Flows | |
| V V V |
| +--------------------------------------------------------------+ |
| | aggregate combination | |
| +--------------------------------------------------------------+ |
| | |
+=======================================|===========================+
V
Aggregated Flows
]]></artwork></figure>
<t><list style="hanging">
<t hangText="Interval distribution"> is a temporal aggregation
operation which imposes an Aggregation Interval on the partially
Contributing Flow. This Aggregation Interval may be regular,
irregular, or derived from the timing of the Original Flows
themselves. Interval distribution is discussed in detail in <xref
target="sec-iap-interval"/>.</t>
<t hangText="Key aggregation "> is a spatial aggregation operation
which results in the addition, modification, or deletion of Flow
Key fields in the Partially Aggregated Flows. New Flow Keys
may be derived from existing Flow Keys (e.g., looking up an
AS number for an IP address), or "promoted" from non-Key fields
(e.g., when aggregating Flows by packet count per Flow). Key
aggregation can also add new non-Key fields derived from Flow Keys
that are deleted during key aggregation; mainly counters of
unique reduced keys. Key aggregation is discussed in detail in
<xref target="sec-iap-key"/>.</t>
<t hangText="Value aggregation "> is a spatial aggregation
operation which results in the addition, modification, or deletion
of non-Key fields in the Partially Aggregated Flows. These non-Key
fields may be "demoted" from existing Key fields, or derived from
existing Key or non-Key fields. Value aggregation is discussed in
detail in <xref target="sec-iap-value"/>.</t>
<t hangText="Aggregate combination "> combines multiple partially
Contributing Flows having undergone interval distribution, key
aggregation, and value aggregation which share Flow Keys and
Aggregation Intervals into a single Contributing Flow per set of
Flow Key values and Aggregation Interval. Aggregate combination is
discussed in detail in <xref target="sec-iap-combo"/>.</t>
<t hangText="Correlation and normalization"> is required when accepting
Original Flows from Metering Processes which export different views of
essentially the same Flows before aggregation; the details of correlation
and normalization are specified in <xref target="sec-iap-arch-correl"/>,
below.</t>
</list></t>
<t>The first three of these operations may be carried out any number
of times in any order, either on Original Flows or on the results of
one of the Operations (called Partially Aggregated Flows), with one
caveat: since Flows carry their own interval data, any spatial
aggregation operation implies a temporal aggregation operation, so at
least one interval distribution step, even if implicit, is required by
this architecture. This is shown as the first step for the sake of
simplicity in the diagram above. Once all aggregation operations are
complete, aggregate combination ensures that for a given Aggregation
Interval, set of Flow Key values, and Observation Domain, only one
Flow is produced by the Intermediate Aggregation Process.</t>
<t>This model describes the operations within a single Intermediate
Aggregation Process, and it is anticipated that most aggregation will
be applied within a single process. However, as the steps in the model
may be applied in any order and aggregate combination is idempotent,
any number of Intermediate Aggregation Processes operating in series
can be modeled as a single process. This allows aggregation operations
to be flexibly distributed across any number of processes, should
application or deployment considerations so dictate.</t>
<section title="Correlation and Normalization" anchor="sec-iap-arch-correl">
<t>When accepting Original Flows from multiple Metering Processes, each
of which provides a different view of the Original Flow as seen from the
point of view of the IAP, an optional correlation and normalization
operation combines each of these single Flow Records into a set of
unified partially aggregated Flows before applying interval distribution.
These unified Flows appear as if they had been measured at a single
Metering Process which used the union of the set of Flow Keys and non-key
fields of all Metering Processes sending Original Flows to the IAP.</t>
<t>Since, due to export errors or other slight irregularities in flow
metering, the multiple views may not be completely consistent;
normalization involves applying a set of aggregation application specific
corrections in order to ensure consistency in the unified Flows.</t>
<t>In general, correlation and normalization should take multiple views
of essentially the same Flow, as determined by the configuration of the
operation itself, and render them into a single unified Flow. Flows which
are essentially different should not be unified by the correlation and
normalization operation. This operation therefore requires enough
information about the configuration and deployment of Metering Processes
from which it correlates Original Flows in order to make this distinction
correctly and consistently.</t>
<t>The exact steps performed to correlate and normalize flows in this
step are application-, implementation-, and deployment-specific, and will
not be further specified in this document.</t>
</section>
</section>
</section>
<section title="IP Flow Aggregation Operations">
<t>As stated in <xref target="sec-terminology"/>, an Aggregated Flow
is simply an IPFIX Flow generated from Original Flows by an
Intermediate Aggregation Process. Here, we detail the operations by
which this is achieved within an Intermediate Aggregation Process.</t>
<section title="Temporal Aggregation through Interval Distribution" anchor="sec-iap-interval">
<t>Interval distribution imposes a time interval on the resulting
Aggregated Flows. The selection of an interval is specific to the
given aggregation application. Intervals may be derived from the
Original Flows themselves (e.g., an interval may be selected to
cover the entire interval containing the set of all Flows sharing
a given Key, as in Time Composition describe in <xref
target="sec-timecomp"/>) or externally imposed; in the latter case
the externally imposed interval may be regular (e.g., every five
minutes) or irregular (e.g., to allow for different time
resolutions at different times of day, under different network
conditions, or indeed for different sets of Original Flows).</t>
<t>The length of the imposed interval itself 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 complex value distribution methods become
inapplicable.</t>
<figure title="Illustration of interval distribution" anchor="intdist-fig">
<artwork><![CDATA[
| | | |
| |<--Flow A-->| | | |
| |<--Flow B-->| | |
| |<-------------Flow C-------------->| |
| | | |
| interval 0 | interval 1 | interval 2 |
]]></artwork>
</figure>
<t>In <xref target="intdist-fig"/>, we illustrate three common
possibilities for interval distribution as applies with regular
intervals to a set of three Original Flows. 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 Aggregated Flows, and must have its counters distributed
according to some policy as in <xref target="sec-distro"/>.</t>
<section title="Distributing Values Across Intervals" anchor="sec-distro">
<t>In general, counters in Aggregated Flows are treated the same
as in any Flow. Each counter is independently calculated as
if it 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.</t>
<t>When the Aggregation Interval is guaranteed to be 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 Contributing Flow to which it
could contribute.</t>
<t>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
roughly increasing order of complexity and accuracy; most of
these are necessary only in specialized cases.</t>
<t><list style="hanging">
<t hangText="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.</t>
<t hangText="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.</t>
<t hangText="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.</t>
<t hangText="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 Keys), and this number is added
to each corresponding counter in each Aggregated Flow.</t>
<t hangText="Proportional Uniform Distribution: "> 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. 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 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.</t>
<t hangText="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, Flow Records
representing bulk transfer might follow a more or less
proportional uniform distribution, while interactive
processes are far more bursty.</t>
<t hangText="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.</t>
</list></t>
<t>A method for exporting the distribution of counters across
multiple Aggregated Flows is detailed in <xref
target="sec-ex-distro"/>. 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.</t>
<t>More complex counter distribution methods generally require
that the interval distribution process track multiple "current"
time intervals at once. This may introduce some delay into the
aggregation operation, as an interval should only expire and be
available for export when no additional Original Flows applying
to the interval are expected to arrive at the Intermediate
Aggregation Process.</t>
<t>Note, however, that since there is no guarantee that Flows
from the Original Exporter will arrive in any given order,
whether for transport-specific reasons (i.e. UDP reordering) or
Metering Process implementation-specific reasons, even simpler
distribution methods may need to deal with flows arriving in
other than start time or end time order. Therefore, the use of
larger intervals does not obviate the need to buffer Partially
Aggregated Flows within "current" time intervals, to ensure it
can accept flow time intervals in any arrival order. More
generally, the interval distribution process SHOULD accept flow
start and end times in the Original Flows in any reasonable
order. The expiration of intervals in interval distribution
operations is dependent on implementation and deployment
requirements, and SHOULD be made configurable in contexts in
which "reasonable order" is not obvious at implementation
time. This operation may lead to delay and loss introduced by the IAP, as detailed in <xref target="sec-lossdelay"/>.</t>
</section>
<section title="Time Composition" anchor="sec-timecomp">
<t>Time Composition as in Section 5.4 of <xref
target="RFC5982"/> (or interval combination) is a special case
of aggregation, where interval distribution imposes longer
intervals on Flows with matching keys and "chained" start and
end times, without any key reduction, in order to join
long-lived Flows which may have been split (e.g., due to an
active timeout shorter than the actual duration of the Flow.)
Here, no Key aggregation is applied, 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.</t>
</section>
</section>
<section title="Spatial Aggregation of Flow Keys" anchor="sec-iap-key">
<t>Key aggregation generates a new set of Flow Key values for the
Aggregated Flows from the Original Flow Keys, non-Key fields in
the Original Flows, or from correlation of the Original Flow
information with some external source. There are two basic
operations here. First, Aggregated Flow Keys may be derived
directly from Original Flow Keys through reduction, or the
dropping of fields or precision in the Original Flow Keys. Second,
Aggregated Flow Keys may be derived through replacement, e.g. by
removing one or more fields from the Original Flow and replacing
them with fields derived from the removed fields. Replacement may
refer to external information (e.g., IP to AS number mappings).
Replacement may apply to Flow Keys as well as non-key fields. For
example, consider an application which aggregates Original Flows
by packet count (i.e., generating an Aggregated Flow for all
one-packet Flows, one for all two-packet Flows, and so on). This
application would promote the packet count to a Flow Key.</t>
<t>Key aggregation may also result in the addition of new non-Key
fields to the Aggregated Flows, namely Original Flow counters and
unique reduced key counters; these are treated in more detail in
<xref target="sec-flowcount"/> and <xref target="sec-distinct"/>,
respectively.</t>
<t>In any key aggregation operation, reduction and/or replacement
may be applied any number of times in any order. Which of these
operations are supported by a given implementation is
implementation- and application-dependent. Key aggregation may
aggregate Original Flows with different sets of Flow Keys;
only the Flow Keys of the resulting Aggregated Flows of any given
Key aggregation operation need to contain the same set of
fields.</t>
<figure title="Illustration of key aggregation by reduction" anchor="keyagg-simple-fig">
<artwork><![CDATA[
Original Flow Keys
+---------+---------+----------+----------+-------+-----+
| 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 Keys (by source address and destination class-C)
]]></artwork>
</figure>
<t><xref target="keyagg-simple-fig"/> illustrates an example
reduction 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.</t>
<figure title="Illustration of key aggregation by reduction and replacement" anchor="keyagg-replace-fig">
<artwork><![CDATA[
Original Flow Keys
+---------+---------+----------+----------+-------+-----+
| 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 Keys (by source and dest ASN)
]]></artwork>
</figure>
<t><xref target="keyagg-replace-fig"/> illustrates an example
reduction and replacement operation, aggregation by source and
destination Border Gateway Protocol (BGP) Autonomous System Number
(ASN) without ASN information available in the Original Flow.
Here, the port, protocol, and type-of-service information is
removed from the Flow Keys, while the source and destination
addresses are run though an IP address to ASN lookup table, and
the Aggregated Flow Keys are made up of the resulting source and
destination ASNs.</t>
<section title="Counting Original Flows" anchor="sec-flowcount">
<t>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 <xref target="sec-ex-flowcount"/> to
export these counters.</t>
<t>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
Contributing Flows. In other words, conservative flow counters are
distributed just as any other counter during interval
distribution, 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.</t>
<t>By contrast, non-conservative flow counting is used to count
how many Contributing 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.</t>
<t>For example, consider Flows A, B, and C as illustrated in
<xref target="intdist-fig"/>. 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 C). 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.</t>
<t>Note that the active and inactive timeouts used to generate
Original Flows, as well as the cache policy used to generate
those Flows, have an effect on how meaningful either the
conservative or non-conservative flow count will be during
aggregation. In general, all the Original Exporters producing
Original Flows to be aggregated SHOULD be aggregated using
caches configured identically or similarly. Original Exporters
using the IPFIX Configuration Model SHOULD be configured to
export Flows with equal or similar activeTimeout and
inactiveTimeout configuration values, and the same cacheMode, as
defined in section 4.3 of <xref
target="I-D.ietf-ipfix-configuration-model"/>.</t>
</section>
<section title="Counting Distinct Key Values" anchor="sec-distinct">
<t>One common case in aggregation is counting distinct key
values that were reduced away during key aggregation. The most
common use case for this is counting distinct hosts per Flow
Key; for example, in host characterization or anomaly detection,
distinct sources per destination or distinct destinations per
source are common metrics. These new non-Key fields are added
during key aggregation.</t>
<t>For such applications, Information Elements for distinct
counts of IPv4 and IPv6 addresses are defined in <xref
target="sec-ex-distinct"/>. These are named
distinctCountOf(KeyName). Additional such Information Elements
SHOULD be registered with IANA on an as-needed basis.</t>
</section>
</section>
<section title="Spatial Aggregation of Non-Key Fields" anchor="sec-iap-value">
<t>Aggregation operations may also lead to the addition of value
fields demoted from key fields, or derived from other value fields
in the Original Flows. Specific cases of this are treated in the
subsections below. </t>
<section title="Counter Statistics" anchor="sec-countstats">
<t>Some applications of aggregation may benefit from
computing different statistics than those native to each
non-key field (i.e., union for flags, sum for counters). For
example, minimum and maximum packet counts per Flow, mean
bytes per packet per Contributing Flow, and so on. Certain
Information Elements for these applications are already
provided in the IANA IPFIX Information Elements registry
(http://www.iana.org/assignments/ipfix/ipfix.html (e.g.
minimumIpTotalLength).</t>
<t>A complete specification of additional aggregate counter
statistics is outside the scope of this document, and should
be added in the future to the IANA IPFIX Information
Elements registry on a per-application, as-needed basis.</t>
</section>
<section title="Derivation of New Values from Flow Keys and non-Key fields" anchor="sec-derived">
<t>More complex operations may lead to other derived fields
being generated from the set of values or Flow Keys reduced away
during aggregation. A prime example of this is sample entropy
calculation. This counts distinct values and frequency, so is
similar to distinct key counting as in <xref
target="sec-distinct"/>, but may be applied to the distribution
of values for any flow field.</t>
<t> Sample entropy calculation provides a one-number normalized
representation of the value spread and is useful for annomaly
detection. The behaviour of entropy statistics is such that a
small number of keys showing up very often drives the entropy
value down towards zero, while a large number of keys, each
showing up with lower frequency drives the entropy value up.</t>
<t>Entropy statistics are generally useful for address-like
keys, like IP addresses, port numbers, AS numbers, etc. They can
also be done on flow length, flow duration fields and the like,
even if this generally yields less distinct value shifts when
the traffic mix changes.</t>
<t>As a practical example, one host scanning a lot of other
hosts will drive source IP entropy down and target IP entropy
up. A similar effect can be observed for ports. This pattern can
also be caused by the scan-traffic of a fast Internet worm. A
second example would be a DDoS flooding attack against a single
target (or small number of targets) which drives source IP
entropy up and target IP entropy down.</t>
<t>A complete specification of additional derived values or
entropy information elements is outside the scope of this
document. Any such Information Elements should be added in the
future to the IANA IPFIX Information Elements registry on a
per-application, as-needed basis. However, in the special case
of entropy calculations, to support comparability of entropies
of fields with different bit sizes, entropy SHOULD be
represented as a float32 or float64 value normalized to the
range [0..1].</t>
</section>
</section>
<section title="Aggregation Combination" anchor="sec-iap-combo">
<t>Interval distribution and key aggregation together may generate
multiple Partially Aggregated Flows covering the same time
interval with the same set of Flow Key values. The process of
combining these Partially Aggregated Flows into a single
Aggregated Flow is called aggregation combination. In general,
non-Key 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. Counters are summed,
averages are averaged, flags are unioned, and so on.</t>
</section>
</section>
<section title="Additional Considerations and Special Cases in Flow Aggregation">
<section title="Exact versus Approximate Counting during Aggregation"
anchor="sec-lowfi">
<t>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). The choice to use approximate counting is
implementation- and application-dependent.</t>
<!-- <t>In certain cases, the magnitude of error for a given
Information Element due to approximate counting may be known. An
Exporting Process MAY use the Error Magnitude Options Template
defined in <xref target="sec-ex-error"/> to export this
information.</t> -->
</section>
<section title="Delay and Loss introduced by the IAP"
anchor="sec-lossdelay">
<t>When accepting Original Flows in export order from traffic
captured live, the Intermediate Aggregation Process wait for all
Original Flows which may contribute to a given interval during
interval distribution. This is generally dominated by the active
timeout of the Metering Process measuring the Original Flows. For
example, with Metering Processes configured with a 5 minute active
timeout, the Intermediate Aggregation Process introduces a delay of
at least 5 minutes to all exported Aggregated Flows to ensure it
has received all Original Flows.</t>
<t>In certain circumstances, additional delay at the original
Exporter may cause an IAP to close an interval before the last
Original Flow(s) accountable to the interval arrives; in this case
the IAP SHOULD drop the late Original Flow(s). Accounting of flows
lost at an Intermediate Process due to such issues is covered in
<xref target="I-D.ietf-ipfix-mediation-protocol"/>.</t>
</section>
<section title="Considerations for Aggregation of Sampled Flows">
<t>The accuracy of Aggregated Flows may also be affected by
sampling of the Original Flows, or sampling of packets making up
the Original Flows. The effect of sampling on flow aggregation is
still an open research question. However, to maximize the
comparability of Aggregated Flows, aggregation of sampled Flows
SHOULD only use Original Flows sampled using the same sampling
rate and sampling algorithm, or Flows created from packets sampled
using the same sampling rate and sampling algorithm. For more on
packet sampling within IPFIX, see <xref target="RFC5476"/>. For
more on Flow sampling within the IPFIX Mediator Framework, see
<xref target="I-D.ietf-ipfix-flow-selection-tech"/>.</t>
</section>
<section title="Considerations for Aggregation of Heterogeneous Flows">
<t>Aggregation may be applied to Original Flows from different
sources and of different types (i.e., represented using different,
perhaps wildly-different Templates). When the goal is to separate
the heterogeneous Original Flows and aggregate them into
heterogeneous Aggregated Flows, each aggregation should be done at
its own Intermediate Aggregation Process. The Observation Domain ID
on the Messages containing the output Aggregated Flows can be used
to identify the different Processes, and to segregate the
output.</t>
<t>However, when the goal is to aggregate these Flows into a single
stream of Aggregated Flows representing one type of data, and if the
Original Flows may represent the same original packet at two different
Observation Points, the Original Flows should be correlated by the
correlation and normalization operation within the IAP to ensure that
each packet is only represented in a single Aggregated Flow or set of
Aggregated Flows differing only by aggregation interval.</t>
</section>
</section>
<section title="Export of Aggregated IP Flows using IPFIX" anchor="sec-export">
<t>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.</t>
<section title="Time Interval Export">
<t>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.</t>
<t>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.</t>
</section>
<section title="Flow Count Export" anchor="sec-ex-flowcount">
<t>The following four Information Elements are defined to count Original Flows as discussed in <xref target="sec-flowcount"/>.</t>
<section title="originalFlowsPresent" anchor="ie-noncon-flowcount">
<t><list style="hanging">
<t hangText="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.
</t>
<t hangText="Abstract Data Type: ">unsigned64</t>
<t hangText="ElementId: ">TBD1</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
<section title="originalFlowsInitiated" anchor="ie-con-flowstartcount">
<t><list style="hanging">
<t hangText="Description: ">
The conservative count of Original Flows whose first packet is
represented within this Aggregated Flow. Conservative counts
must sum to the original count on re-aggregation.
</t>
<t hangText="Abstract Data Type: ">unsigned64</t>
<t hangText="ElementId: ">TBD2</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
<section title="originalFlowsCompleted" anchor="ie-con-flowendcount">
<t><list style="hanging">
<t hangText="Description: ">
The conservative count of Original Flows whose last packet is
represented within this Aggregated Flow. Conservative counts
must sum to the original count on re-aggregation.
</t>
<t hangText="Abstract Data Type: ">unsigned64</t>
<t hangText="ElementId: ">TBD3</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
<section title="deltaFlowCount" anchor="ie-con-flowcount">
<t><list style="hanging">
<t hangText="Description: ">
The conservative count of Original Flows contributing to this
Aggregated Flow; may be distributed via any of the methods
described in <xref target="sec-distro"/>. This Information
Element is compatible with Information Element 3 as used in
NetFlow version 9. </t>
<t hangText="Abstract Data Type: ">float64</t>
<t hangText="ElementId: ">3</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
</section>
<section title="Distinct Host Export" anchor="sec-ex-distinct">
<t>The following four Information Elements represent the distinct counts of source and destination network-layer addresses, used to export distinct host counts reduced away during key aggregation.</t>
<section title="distinctCountOfSourceIPAddress" anchor="ie-dsip">
<t><list style="hanging">
<t hangText="Description: ">The count of distinct source IP address values for Original Flows contributing to this Aggregated Flow, without regard to version. This Information Element is preferred to the IP-version-specific counters, unless it is important to separate the counts by version.</t>
<t hangText="Abstract Data Type: ">unsigned64</t>
<t hangText="ElementId: ">TBD4</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
<section title="distinctCountOfDestinationIPAddress" anchor="ie-ddip">
<t><list style="hanging">
<t hangText="Description: ">The count of distinct destination IP address values for Original Flows contributing to this Aggregated Flow, without regard to version. This Information Element is preferred to the version-specific counters below, unless it is important to separate the counts by version.</t>
<t hangText="Abstract Data Type: ">unsigned64</t>
<t hangText="ElementId: ">TBD5</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
<section title="distinctCountOfSourceIPv4Address" anchor="ie-dsip4">
<t><list style="hanging">
<t hangText="Description: ">The count of distinct source IPv4 address values for Original Flows contributing to this Aggregated Flow.</t>
<t hangText="Abstract Data Type: ">unsigned32</t>
<t hangText="ElementId: ">TBD6</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
<section title="distinctCountOfDestinationIPv4Address" anchor="ie-ddip4">
<t><list style="hanging">
<t hangText="Description: ">The count of distinct destination IPv4 address values for Original Flows contributing to this Aggregated Flow.</t>
<t hangText="Abstract Data Type: ">unsigned32</t>
<t hangText="ElementId: ">TBD7</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
<section title="distinctCountOfSourceIPv6Address" anchor="ie-dsip6">
<t><list style="hanging">
<t hangText="Description: ">The count of distinct source IPv6 address values for Original Flows contributing to this Aggregated Flow.</t>
<t hangText="Abstract Data Type: ">unsigned64</t>
<t hangText="ElementId: ">TBD8</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
<section title="distinctCountOfDestinationIPv6Address" anchor="ie-ddip6">
<t><list style="hanging">
<t hangText="Description: ">The count of distinct destination IPv6 address values for Original Flows contributing to this Aggregated Flow.</t>
<t hangText="Abstract Data Type: ">unsigned64</t>
<t hangText="ElementId: ">TBD9</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
</section>
<section title="Aggregate Counter Distribution Export" anchor="sec-ex-distro">
<t>When exporting counters distributed among Aggregated Flows, as
described in <xref target='sec-distro'/>, 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.</t>
<section title="Aggregate Counter Distribution Options Template">
<t>This Options Template defines the Aggregate Counter
Distribution Record, which allows the binding of a value
distribution method to a Template ID. Note that this Options
Template causes the valueDistributionMethod to be implicitly
scoped to the Observation Domain ID of the IPFIX Message
containing the Aggregate Counter Distribution Record. This is
used to signal to the Collecting Process how the counters were
distributed. The fields are as below:</t>
<texttable>
<ttcol align="left">IE</ttcol>
<ttcol align="left">Description</ttcol>
<c>templateId [scope]</c>
<c>
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.
</c>
<c>valueDistributionMethod</c>
<c>
The method used to distribute the counters for the
Aggregated Flows defined by the associated Template.
</c>
</texttable>
</section>
<section title="valueDistributionMethod Information Element" anchor="ie-errmag">
<t><list style="hanging">
<t hangText="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 <xref target='sec-distro'/> and
encoded as follows:
<texttable>
<ttcol align="left">Value</ttcol>
<ttcol align="left">Description</ttcol>
<c>1</c><c>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.</c>
<c>2</c><c>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.</c>
<c>3</c><c>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.</c>
<c>4</c><c>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.</c>
<c>5</c><c>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.</c>
<c>6</c><c>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.</c>
<c>7</c><c>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.</c>
</texttable>
</t>
<t hangText="Abstract Data Type: ">unsigned8</t>
<t hangText="ElementId: ">TBD10</t>
<t hangText="Status: ">Current</t>
</list></t>
</section>
</section>
</section>
<section title="Examples">
<t>In these examples, the same data, described by the same template, will be aggregated multiple different ways; this illustrates the various different functions which could be implemented by Intermediate Aggregation Processes. Templates are shown in iespec format as introduced in <xref target="I-D.ietf-ipfix-ie-doctors"/>. The source data format is a simplified flow: timestamps, traditional 5-tuple, and octet count. The template is shown in <xref target="ex-tmpl-in"/>.</t>
<figure title="Input template for examples" anchor="ex-tmpl-in">
<artwork><![CDATA[
flowStartMilliseconds(152)[8]
flowEndMilliseconds(153)[8]
sourceIPv4Address(8)[4]
destinationIPv4Address(12)[4]
sourceTransportPort(7)[2]
destinationTransportPort(11)[2]
protocolIdentifier(4)[1]
octetDeltaCount(1)[8]
]]></artwork>
</figure>
<t>The data records given as input to the examples in this section are shown below, in the format "flowStartMilliseconds-flowEndMilliseconds sourceIPv4Address:sourceTransportPort -> destinationIPv4Address:destinationTransportPort (protocolIdentifier) octetDeltaCount"; timestamps are given in H:MM:SS.sss format.</t>
<figure title="Input data for examples" anchor="ex-data-in">
<artwork><![CDATA[
9:00:00.138-9:00:00.138 192.0.2.2:47113 -> 192.0.2.131:53 (17) 119
9:00:03.246-9:00:03.246 192.0.2.2:22153 -> 192.0.2.131:53 (17) 83
9:00:00.478-9:00:03.486 192.0.2.2:52420 -> 198.51.100.2:443 (6) 1637
9:00:07.172-9:00:07.172 192.0.2.3:56047 -> 192.0.2.131:53 (17) 111
9:00:07.309-9:00:14.861 192.0.2.3:41183 -> 198.51.100.67:80 (6) 16838
9:00:03.556-9:00:19.876 192.0.2.2:17606 -> 198.51.100.68:80 (6) 11538
9:00:25.210-9:00:25.210 192.0.2.3:47113 -> 192.0.2.131:53 (17) 119
9:00:26.358-9:00:30.198 192.0.2.3:48458 -> 198.51.100.133:80 (6) 2973
9:00:29.213-9:01:00.061 192.0.2.4:61295 -> 198.51.100.2:443 (6) 8350
9:04:00.207-9:04:04.431 203.0.113.3:41256 -> 198.51.100.133:80 (6) 778
9:03:59.624-9:04:06.984 203.0.113.3:51662 -> 198.51.100.3:80 (6) 883
9:00:30.532-9:06:15.402 192.0.2.2:37581 -> 198.51.100.2:80 (6) 15420
9:06:56.813-9:06:59.821 203.0.113.3:52572 -> 198.51.100.2:443 (6) 1637
9:06:30.565-9:07:00.261 203.0.113.3:49914 -> 197.51.100.133:80 (6) 561
9:06:55.160-9:07:05.208 192.0.2.2:50824 -> 198.51.100.2:443 (6) 1899
9:06:49.322-9:07:05.322 192.0.2.3:34597 -> 198.51.100.3:80 (6) 1284
9:07:05.849-9:07:09.625 203.0.113.3:58907 -> 198.51.100.4:80 (6) 2670
9:10:45.161-9:10:45.161 192.0.2.4:22478 -> 192.0.2.131:53 (17) 75
9:10:45.209-9:11:01.465 192.0.2.4:49513 -> 198.51.100.68:80 (6) 3374
9:10:57.094-9:11:00.614 192.0.2.4:64832 -> 198.51.100.67:80 (6) 138
9:10:59.770-9:11:02.842 192.0.2.3:60833 -> 198.51.100.69:443 (6) 2325
9:02:18.390-9:13:46.598 203.0.113.3:39586 -> 198.51.100.17:80 (6) 11200
9:13:53.933-9:14:06.605 192.0.2.2:19638 -> 198.51.100.3:80 (6) 2869
9:13:02.864-9:14:08.720 192.0.2.3:40429 -> 198.51.100.4:80 (6) 18289
]]></artwork>
</figure>
<section title="Traffic Time-Series per Source" anchor="ex-srcip">
<t>Aggregating flows by source IP address in time series (i.e., with a
regular interval) can be used in subsequent heavy-hitter analysis and as
a source parameter for statistical anomaly detection techniques. Here,
the Intermediate Aggregation Process imposes an interval, aggregates the
key to remove all key fields other than the source IP address, then
combines the result into a stream of Aggregated Flows. The imposed interval of 5 minutes is longer than the majority of flows; for those flows crossing interval boundaries, the entire flow is accounted to the interval containing the start time of the flow.</t>
<t>In this example the Partially Aggregated Flows after each conceptual
operation in the Intermediate Aggregation Process are shown. These are
meant to be illustrative of the conceptual operations only, and not to
suggest an implementation (indeed, the example shown here would not
necessarily be the most efficient method for performing these
operations). Subsequent examples will omit the Partially Aggregated
Flows for brevity.</t>
<t>The input to this process could be any Flow Record containing a
source IP address and octet counter; consider for this example the
template and data from the introduction. The Intermediate Aggregation
Process would then output records containing just timestamps, source IP,
and octetDeltaCount, as in <xref target="ex-srcip-tmpl-out"/>.</t>
<figure title="Output template for time series per source" anchor="ex-srcip-tmpl-out">
<artwork><![CDATA[
flowStartMilliseconds(152)[8]
flowEndMilliseconds(153)[8]
sourceIPv4Address(8)[4]
octetDeltaCount(1)[8]
]]></artwork>
</figure>
<t>Assume the goal is to get 5-minute time series of octet counts per source IP address. The aggregation operations would then be arranged as in <xref target="ex-srcip-iap"/>.</t>
<figure title="Aggregation operations for time series per source" anchor="ex-srcip-iap"><artwork><![CDATA[
Original Flows
|
V
+-----------------------+
| interval distribution |
| * impose uniform |
| 300s time interval |
+-----------------------+
|
| Partially Aggregated Flows
V
+------------------------+
| key aggregation |
| * reduce key to only |
| sourceIPv4Address |
+------------------------+
|
| Partially Aggregated Flows
V
+-------------------------+
| aggregate combination |
| * sum octetDeltaCount |
+-------------------------+
|
V
Aggregated Flows
]]></artwork></figure>
<t>After the interval distribution step, only the time intervals have changed; the Partially Aggregated flows are shown in <xref target="ex-srcip-pa-int"/>. Note that interval distribution follows the default Start Interval policy; that is, the entire flow is accounted to the interval containing the flow's start time.</t>
<figure title="Interval imposition for time series per source" anchor="ex-srcip-pa-int">
<artwork><![CDATA[
9:00:00.000-9:05:00.000 192.0.2.2:47113 -> 192.0.2.131:53 (17) 119
9:00:00.000-9:05:00.000 192.0.2.2:22153 -> 192.0.2.131:53 (17) 83
9:00:00.000-9:05:00.000 192.0.2.2:52420 -> 198.51.100.2:443 (6) 1637
9:00:00.000-9:05:00.000 192.0.2.3:56047 -> 192.0.2.131:53 (17) 111
9:00:00.000-9:05:00.000 192.0.2.3:41183 -> 198.51.100.67:80 (6) 16838
9:00:00.000-9:05:00.000 192.0.2.2:17606 -> 198.51.100.68:80 (6) 11538
9:00:00.000-9:05:00.000 192.0.2.3:47113 -> 192.0.2.131:53 (17) 119
9:00:00.000-9:05:00.000 192.0.2.3:48458 -> 198.51.100.133:80 (6) 2973
9:00:00.000-9:05:00.000 192.0.2.4:61295 -> 198.51.100.2:443 (6) 8350
9:00:00.000-9:05:00.000 203.0.113.3:41256 -> 198.51.100.133:80 (6) 778
9:00:00.000-9:05:00.000 203.0.113.3:51662 -> 198.51.100.3:80 (6) 883
9:00:00.000-9:05:00.000 192.0.2.2:37581 -> 198.51.100.2:80 (6) 15420
9:00:00.000-9:05:00.000 203.0.113.3:39586 -> 198.51.100.17:80 (6) 11200
9:05:00.000-9:10:00.000 203.0.113.3:52572 -> 198.51.100.2:443 (6) 1637
9:05:00.000-9:10:00.000 203.0.113.3:49914 -> 197.51.100.133:80 (6) 561
9:05:00.000-9:10:00.000 192.0.2.2:50824 -> 198.51.100.2:443 (6) 1899
9:05:00.000-9:10:00.000 192.0.2.3:34597 -> 198.51.100.3:80 (6) 1284
9:05:00.000-9:10:00.000 203.0.113.3:58907 -> 198.51.100.4:80 (6) 2670
9:10:00.000-9:15:00.000 192.0.2.4:22478 -> 192.0.2.131:53 (17) 75
9:10:00.000-9:15:00.000 192.0.2.4:49513 -> 198.51.100.68:80 (6) 3374
9:10:00.000-9:15:00.000 192.0.2.4:64832 -> 198.51.100.67:80 (6) 138
9:10:00.000-9:15:00.000 192.0.2.3:60833 -> 198.51.100.69:443 (6) 2325
9:10:00.000-9:15:00.000 192.0.2.2:19638 -> 198.51.100.3:80 (6) 2869
9:10:00.000-9:15:00.000 192.0.2.3:40429 -> 198.51.100.4:80 (6) 18289
]]></artwork>
</figure>
<t>After the key aggregation step, all Flow Keys except the source IP address have been discarded, as shown in <xref target="ex-srcip-pa-key"/>. This leaves duplicate Partially Aggregated flows to be combined in the final operation.</t>
<figure title="Key aggregation for time series per source" anchor="ex-srcip-pa-key">
<artwork><![CDATA[
9:00:00.000-9:05:00.000 192.0.2.2 119
9:00:00.000-9:05:00.000 192.0.2.2 83
9:00:00.000-9:05:00.000 192.0.2.2 1637
9:00:00.000-9:05:00.000 192.0.2.3 111
9:00:00.000-9:05:00.000 192.0.2.3 16838
9:00:00.000-9:05:00.000 192.0.2.2 11538
9:00:00.000-9:05:00.000 192.0.2.3 119
9:00:00.000-9:05:00.000 192.0.2.3 2973
9:00:00.000-9:05:00.000 192.0.2.4 8350
9:00:00.000-9:05:00.000 203.0.113.3 778
9:00:00.000-9:05:00.000 203.0.113.3 883
9:05:00.000-9:10:00.000 203.0.113.3 1637
9:05:00.000-9:10:00.000 203.0.113.3 561
9:05:00.000-9:10:00.000 192.0.2.2 1899
9:05:00.000-9:10:00.000 192.0.2.3 1284
9:05:00.000-9:10:00.000 203.0.113.3 2670
9:10:00.000-9:15:00.000 192.0.2.4 75
9:10:00.000-9:15:00.000 192.0.2.4 3374
9:10:00.000-9:15:00.000 192.0.2.4 138
9:10:00.000-9:15:00.000 192.0.2.3 2325
9:10:00.000-9:15:00.000 192.0.2.2 2869
9:10:00.000-9:15:00.000 192.0.2.3 18289
]]></artwork>
</figure>
<t>Aggregate combination sums the counters per key and interval; the summations of the first two keys and intervals are shown in detail in <xref target="ex-srcip-sum"/>.</t>
<figure title="Summation during aggregate combination" anchor="ex-srcip-sum">
<artwork><![CDATA[
9:00:00.000-9:05:00.000 192.0.2.2 119
9:00:00.000-9:05:00.000 192.0.2.2 83
9:00:00.000-9:05:00.000 192.0.2.2 1637
9:00:00.000-9:05:00.000 192.0.2.2 11538
+ 9:00:00.000-9:05:00.000 192.0.2.2 15420
-----
= 9:00:00.000-9:05:00.000 192.0.2.2 28797
9:00:00.000-9:05:00.000 192.0.2.3 111
9:00:00.000-9:05:00.000 192.0.2.3 16838
9:00:00.000-9:05:00.000 192.0.2.3 119
+ 9:00:00.000-9:05:00.000 192.0.2.3 2973
-----
= 9:00:00.000-9:05:00.000 192.0.2.3 20041
]]></artwork>
</figure>
<t>Applying this to each set of Partially Aggregated Flows to produce the final Aggregated Flows shown in <xref target="ex-srcip-agg"/> to be exported by the template in <xref target="ex-srcip-tmpl-out"/>.</t>
<figure title="Aggregated Flows for time series per source" anchor="ex-srcip-agg">
<artwork><![CDATA[
9:00:00.000-9:05:00.000 192.0.2.2 28797
9:00:00.000-9:05:00.000 192.0.2.3 20041
9:00:00.000-9:05:00.000 192.0.2.4 8350
9:00:00.000-9:05:00.000 203.0.113.3 12861
9:05:00.000-9:10:00.000 192.0.2.2 1899
9:05:00.000-9:10:00.000 192.0.2.3 1284
9:05:00.000-9:10:00.000 203.0.113.3 4868
9:10:00.000-9:15:00.000 192.0.2.2 2869
9:10:00.000-9:15:00.000 192.0.2.3 20594
9:10:00.000-9:15:00.000 192.0.2.4 3587
]]></artwork>
</figure>
</section>
<section title="Core Traffic Matrix">
<t>Aggregating flows by source and destination autonomous system number
in time series is used to generate core traffic matrices. The core
traffic matrix provides a view of the state of the routes within a
network, and can be used for long-term planning of changes to network
design based on traffic demand. Here, imposed time intervals are
generally much longer than active flow timeouts. The traffic matrix is
reported in terms of octets, packets, and flows, as each of these values
may have a subtly different effect on capacity planning.</t>
<t>This example demonstrates key aggregation using derived keys and
original flow counting. While some Original Flows may be generated by
Exporting Processes on forwarding devices, and therefore contain the
bgpSourceAsNumber and bgpDestinationAsNumber Information Elements,
Original Flows from Exporting Processes on dedicated measurement devices
will contain only a destinationIPv[46]Address. For these flows, the
Mediator must look up a next hop AS from a IP to AS table, replacing
source and destination addresses with AS numbers. The table used in this
example is shown in <xref target="ex-asn-map"/>. (Note that due to
limited example address space, in this example we ignore the common
practice of routing only blocks of /24 or larger).</t>
<figure title="Example Autonomous system number map" anchor="ex-asn-map">
<artwork><![CDATA[
prefix ASN
192.0.2.0/25 64496
192.0.2.128/25 64497
198.51.100/24 64498
203.0.113.0/24 64499
]]></artwork>
</figure>
<t>The template for Aggregated Flows produced by this example is shown in <xref target="ex-asn-tmpl-out"/>.</t>
<figure title="Output template for traffic matrix" anchor="ex-asn-tmpl-out">
<artwork><![CDATA[
flowStartMilliseconds(152)[8]
flowEndMilliseconds(153)[8]
bgpSourceAsNumber(16)[4]
bgpDestinationAsNumber(17)[4]
octetDeltaCount(1)[8]
]]></artwork>
</figure>
<t>Assume the goal is to get 60-minute time series of octet counts per source/destination ASN pair. The aggregation operations would then be arranged as in <xref target="ex-asn-iap"/>.</t>
<figure title="Aggregation operations for traffic matrix" anchor="ex-asn-iap"><artwork><![CDATA[
Original Flows
|
V
+-----------------------+
| interval distribution |
| * impose uniform |
| 3600s time interval |
+-----------------------+
|
| Partially Aggregated Flows
V
+------------------------+
| key aggregation |
| * reduce key to only |
| sourceIPv4Address + |
| destIPv4Address |
+------------------------+
|
V
+------------------------+
| key aggregation |
| * replace addresses |
| with ASN from map |
+------------------------+
|
| Partially Aggregated Flows
V
+-------------------------+
| aggregate combination |
| * sum octetDeltaCount |
+-------------------------+
|
V
Aggregated Flows
]]></artwork></figure>
<t>After the interval distribution step, only the time intervals have
changed; the Partially Aggregated flows are shown in <xref
target="ex-asn-pa-int"/>. Note that the flows are identical to those in
interval distribution step in the previous example, except the chosen
interval (1 hour, 3600 seconds) is different; therefore, all the flows
fit into a single interval.</t>
<figure title="Interval imposition for traffic matrix" anchor="ex-asn-pa-int">
<artwork><![CDATA[
9:00:00.000-10:00:00.000 192.0.2.2:47113 -> 192.0.2.131:53 (17) 119
9:00:00.000-10:00:00.000 192.0.2.2:22153 -> 192.0.2.131:53 (17) 83
9:00:00.000-10:00:00.000 192.0.2.2:52420 -> 198.51.100.2:443 (6) 1637
9:00:00.000-10:00:00.000 192.0.2.3:56047 -> 192.0.2.131:53 (17) 111
9:00:00.000-10:00:00.000 192.0.2.3:41183 -> 198.51.100.67:80 (6) 16838
9:00:00.000-10:00:00.000 192.0.2.2:17606 -> 198.51.100.68:80 (6) 11538
9:00:00.000-10:00:00.000 192.0.2.3:47113 -> 192.0.2.131:53 (17) 119
9:00:00.000-10:00:00.000 192.0.2.3:48458 -> 198.51.100.133:80 (6) 2973
9:00:00.000-10:00:00.000 192.0.2.4:61295 -> 198.51.100.2:443 (6) 8350
9:00:00.000-10:00:00.000 203.0.113.3:41256 -> 198.51.100.133:80 (6) 778
9:00:00.000-10:00:00.000 203.0.113.3:51662 -> 198.51.100.3:80 (6) 883
9:00:00.000-10:00:00.000 192.0.2.2:37581 -> 198.51.100.2:80 (6) 15420
9:00:00.000-10:00:00.000 203.0.113.3:52572 -> 198.51.100.2:443 (6) 1637
9:00:00.000-10:00:00.000 203.0.113.3:49914 -> 197.51.100.133:80 (6) 561
9:00:00.000-10:00:00.000 192.0.2.2:50824 -> 198.51.100.2:443 (6) 1899
9:00:00.000-10:00:00.000 192.0.2.3:34597 -> 198.51.100.3:80 (6) 1284
9:00:00.000-10:00:00.000 203.0.113.3:58907 -> 198.51.100.4:80 (6) 2670
9:00:00.000-10:00:00.000 192.0.2.4:22478 -> 192.0.2.131:53 (17) 75
9:00:00.000-10:00:00.000 192.0.2.4:49513 -> 198.51.100.68:80 (6) 3374
9:00:00.000-10:00:00.000 192.0.2.4:64832 -> 198.51.100.67:80 (6) 138
9:00:00.000-10:00:00.000 192.0.2.3:60833 -> 198.51.100.69:443 (6) 2325
9:00:00.000-10:00:00.000 203.0.113.3:39586 -> 198.51.100.17:80 (6) 11200
9:00:00.000-10:00:00.000 192.0.2.2:19638 -> 198.51.100.3:80 (6) 2869
9:00:00.000-10:00:00.000 192.0.2.3:40429 -> 198.51.100.4:80 (6) 18289
]]></artwork>
</figure>
<t>The next step is to discard irrelevant key fields, and replace the source and destination addresses with source and destination AS numbers in the map; the results of these key aggregation steps are shown in <xref target="ex-asn-pa-key"/>.</t>
<figure title="Key aggregation for traffic matrix: reduction and replacement" anchor="ex-asn-pa-key">
<artwork><![CDATA[
9:00:00.000-10:00:00.000 AS64496 -> AS64497 119
9:00:00.000-10:00:00.000 AS64496 -> AS64497 83
9:00:00.000-10:00:00.000 AS64496 -> AS64498 1637
9:00:00.000-10:00:00.000 AS64496 -> AS64497 111
9:00:00.000-10:00:00.000 AS64496 -> AS64498 16838
9:00:00.000-10:00:00.000 AS64496 -> AS64498 11538
9:00:00.000-10:00:00.000 AS64496 -> AS64497 119
9:00:00.000-10:00:00.000 AS64496 -> AS64498 2973
9:00:00.000-10:00:00.000 AS64496 -> AS64498 8350
9:00:00.000-10:00:00.000 AS64499 -> AS64498 778
9:00:00.000-10:00:00.000 AS64499 -> AS64498 883
9:00:00.000-10:00:00.000 AS64496 -> AS64498 15420
9:00:00.000-10:00:00.000 AS64499 -> AS64498 1637
9:00:00.000-10:00:00.000 AS64499 -> AS64498 561
9:00:00.000-10:00:00.000 AS64496 -> AS64498 1899
9:00:00.000-10:00:00.000 AS64496 -> AS64498 1284
9:00:00.000-10:00:00.000 AS64499 -> AS64498 2670
9:00:00.000-10:00:00.000 AS64496 -> AS64497 75
9:00:00.000-10:00:00.000 AS64496 -> AS64498 3374
9:00:00.000-10:00:00.000 AS64496 -> AS64498 138
9:00:00.000-10:00:00.000 AS64496 -> AS64498 2325
9:00:00.000-10:00:00.000 AS64499 -> AS64498 11200
9:00:00.000-10:00:00.000 AS64496 -> AS64498 2869
9:00:00.000-10:00:00.000 AS64496 -> AS64498 18289
]]></artwork>
</figure>
<t>Finally, aggregate combination sums the counters per key and interval. The resulting Aggregated Flows containing the traffic matrix, shown in <xref target="ex-asn-agg"/>, are then exported using the template in <xref target="ex-asn-tmpl-out"/>. Note that these aggregated flows represent a sparse matrix: AS pairs for which no traffic was received have no corresponding record in the output.</t>
<figure title="Aggregated Flows for traffic matrix" anchor="ex-asn-agg">
<artwork><![CDATA[
9:00:00.000-10:00:00.000 AS64496 -> AS64497 507
9:00:00.000-10:00:00.000 AS64496 -> AS64498 86934
9:00:00.000-10:00:00.000 AS64499 -> AS64498 17729
]]></artwork>
</figure>
<t>The output of this operation is suitable for re-aggregation: that is, traffic matrices from single links or observarion points can be aggregated through the same interval imposition and aggregate combination steps in order to build a traffic matrix for an entire network.</t>
</section>
<section title="Distinct Source Count per Destination Endpoint">
<t>Aggregating flows by destination address and port, and counting
distinct sources aggregated away, can be used as part of passive service
inventory and host characterization approaches. This example shows
aggregation as an analysis technique, performed on source data stored in
an IPFIX File. As the Transport Session in this File is bounded, removal
of all timestamp information allows summarization of the entire time
interval contained within the interval. Removal of timing information
during interval imposition is equivalent to an infinitely long imposed
time interval. This demonstrates both how infinite intervals work, and
how unique counters work. The aggregation operations are summarized in <xref target="ex-ds-iap"/>.</t>
<figure title="Aggregation operations for source count" anchor="ex-ds-iap"><artwork><![CDATA[
Original Flows
|
V
+-----------------------+
| interval distribution |
| * discard timestamps |
+-----------------------+
|
| Partially Aggregated Flows
V
+----------------------------+
| value aggregation |
| * discard octetDeltaCount |
+----------------------------+
|
| Partially Aggregated Flows
V
+----------------------------+
| key aggregation |
| * reduce key to only |
| destIPv4Address + |
| destTransportPort, |
| * count distinct sources |
+----------------------------+
|
| Partially Aggregated Flows
V
+----------------------------------------------+
| aggregate combination |
| * no-op (distinct sources already counted) |
+----------------------------------------------+
|
V
Aggregated Flows
]]></artwork></figure>
<t>The template for Aggregated Flows produced by this example is shown in <xref target="ex-ds-tmpl-out"/>.</t>
<figure title="Output template for source count" anchor="ex-ds-tmpl-out">
<artwork><![CDATA[
destinationIPv4Address(12)[4]
destinationTransportPort(11)[2]
distinctCountOfSourceIPAddress(TBD4)[8]
]]></artwork>
</figure>
<t>Interval distribution, in this case, merely discards the timestamp information from the Original Flows, and as such is not shown. Likewise, the value aggregation step simply discards the octetDeltaCount value field. The key aggregation step reduces the key to the destinationIPv4Address and destinationTransportPort, counting the distinct source addresses. Since this is essentially the output of this aggregation function, the aggregate combination operation is a no-op; the resulting Aggregated Flows are shown in <xref target="ex-ds-agg"/>.</t>
<figure title="Aggregated flows for source count" anchor="ex-ds-agg">
<artwork><![CDATA[
destination 192.0.2.131:53 3 sources
destination 198.51.100.2:80 1 source
destination 198.51.100.2:443 3 sources
destination 198.51.100.67:80 2 sources
destination 198.51.100.68:80 2 sources
destination 198.51.100.133:80 2 sources
destination 198.51.100.3:80 3 sources
destination 198.51.100.4:80 2 sources
destination 198.51.100.17:80 1 source
destination 198.51.100.69:443 1 source
]]></artwork>
</figure>
</section>
<section title="Traffic Time-Series per Source with Counter Distribution" anchor="ex-distro">
<t>Returning to the example in <xref target="ex-srcip"/>, note that our source data contains some flows with durations longer than the imposed interval of five minutes. The default method for dealing with such flows is to account them to the interval containing the flow's start time.</t>
<t>In this example, the same data is aggregated using the same arrangement of operations and the same output template as the as in <xref target="ex-srcip"/>, but using a different counter distribution policy, Simple Uniform Distribution, as described in <xref target="sec-distro"/>. In order to do this, the Exporting Process first exports the Aggregate Counter Distribution Options Template, as in <xref target="ex-acd-tmpl"/>.</t>
<figure title="Aggregate Counter Distribution Options Template" anchor="ex-acd-tmpl">
<artwork><![CDATA[
templateId(12)[2]{scope}
valueDistribtutionMethod(TBD10)[1]
]]></artwork>
</figure>
<t>This is followed by an Aggregate Counter Distribution Record described by this Template; assuming the output template in <xref target="ex-srcip-tmpl-out"/> has ID 257, this would appear as in <xref target="ex-acd-rec"/>.</t>
<figure title="Aggregate Counter Distribution Record" anchor="ex-acd-rec">
<artwork><![CDATA[
templateId 257: valueDistributionMethod 4 (Simple Uniform)
]]></artwork>
</figure>
<t>[EDITOR'S NOTE: redo these in boxdiagrams?]</t>
<t>Following metadata export, the aggregation steps follow as before.
However, two long flows are distributed across multiple invervals in the
interval imposition step, as indicated with "*" in <xref
target="ex-acd-pa-int"/>. Note the uneven distribution of the
three-interval, 11200-octet flow into three Partially Aggregated Flows of
3733, 3733, and 3734 octets; this ensures no cumulative error is injected
by the interval distribution step.</t>
<figure title="Distirbuted interval imposition for time series per source" anchor="ex-acd-pa-int">
<artwork><![CDATA[
9:00:00.000-9:05:00.000 192.0.2.2:47113 -> 192.0.2.131:53 (17) 119
9:00:00.000-9:05:00.000 192.0.2.2:22153 -> 192.0.2.131:53 (17) 83
9:00:00.000-9:05:00.000 192.0.2.2:52420 -> 198.51.100.2:443 (6) 1637
9:00:00.000-9:05:00.000 192.0.2.3:56047 -> 192.0.2.131:53 (17) 111
9:00:00.000-9:05:00.000 192.0.2.3:41183 -> 198.51.100.67:80 (6) 16838
9:00:00.000-9:05:00.000 192.0.2.2:17606 -> 198.51.100.68:80 (6) 11538
9:00:00.000-9:05:00.000 192.0.2.3:47113 -> 192.0.2.131:53 (17) 119
9:00:00.000-9:05:00.000 192.0.2.3:48458 -> 198.51.100.133:80 (6) 2973
9:00:00.000-9:05:00.000 192.0.2.4:61295 -> 198.51.100.2:443 (6) 8350
9:00:00.000-9:05:00.000 203.0.113.3:41256 -> 198.51.100.133:80 (6) 778
9:00:00.000-9:05:00.000 203.0.113.3:51662 -> 198.51.100.3:80 (6) 883
* 9:00:00.000-9:05:00.000 192.0.2.2:37581 -> 198.51.100.2:80 (6) 7710
* 9:00:00.000-9:05:00.000 203.0.113.3:39586 -> 198.51.100.17:80 (6) 3733
9:05:00.000-9:10:00.000 203.0.113.3:52572 -> 198.51.100.2:443 (6) 1637
9:05:00.000-9:10:00.000 203.0.113.3:49914 -> 197.51.100.133:80 (6) 561
9:05:00.000-9:10:00.000 192.0.2.2:50824 -> 198.51.100.2:443 (6) 1899
9:05:00.000-9:10:00.000 192.0.2.3:34597 -> 198.51.100.3:80 (6) 1284
9:05:00.000-9:10:00.000 203.0.113.3:58907 -> 198.51.100.4:80 (6) 2670
* 9:05:00.000-9:10:00.000 192.0.2.2:37581 -> 198.51.100.2:80 (6) 7710
* 9:05:00.000-9:10:00.000 203.0.113.3:39586 -> 198.51.100.17:80 (6) 3733
9:10:00.000-9:15:00.000 192.0.2.4:22478 -> 192.0.2.131:53 (17) 75
9:10:00.000-9:15:00.000 192.0.2.4:49513 -> 198.51.100.68:80 (6) 3374
9:10:00.000-9:15:00.000 192.0.2.4:64832 -> 198.51.100.67:80 (6) 138
9:10:00.000-9:15:00.000 192.0.2.3:60833 -> 198.51.100.69:443 (6) 2325
* 9:10:00.000-9:15:00.000 203.0.113.3:39586 -> 198.51.100.17:80 (6) 3734
9:10:00.000-9:15:00.000 192.0.2.2:19638 -> 198.51.100.3:80 (6) 2869
9:10:00.000-9:15:00.000 192.0.2.3:40429 -> 198.51.100.4:80 (6) 18289
]]></artwork>
</figure>
<t>Subsequent steps are as in <xref target="ex-srcip"/>; the results, to be exported using <xref target="ex-srcip-tmpl-out"/>, are shown in <xref target="ex-acd-agg"/>, with Aggregated Flows differing from the previous example indicated by "*".</t>
<figure title="Aggregated Flows for time series per source with counter distribution" anchor="ex-acd-agg">
<artwork><![CDATA[
* 9:00:00.000-9:05:00.000 192.0.2.2 21087
9:00:00.000-9:05:00.000 192.0.2.3 20041
9:00:00.000-9:05:00.000 192.0.2.4 8350
* 9:00:00.000-9:05:00.000 203.0.113.3 9394
* 9:05:00.000-9:10:00.000 192.0.2.2 9609
9:05:00.000-9:10:00.000 192.0.2.3 1284
* 9:05:00.000-9:10:00.000 203.0.113.3 8601
9:10:00.000-9:15:00.000 192.0.2.2 2869
9:10:00.000-9:15:00.000 192.0.2.3 20594
9:10:00.000-9:15:00.000 192.0.2.4 3587
* 9:10:00.000-9:15:00.000 203.0.113.3 3734
]]></artwork>
</figure>
</section>
</section>
<section title="Security Considerations">
<t>This document specifies the operation of an Intermediate
Aggregation Process with the IPFIX Protocol; the Security
Considerations for the protocol itself in Section 11 [RFC-EDITOR NOTE:
verify section number] of <xref
target="I-D.ietf-ipfix-protocol-rfc5101bis"/> therefore apply. In the
common case that aggregation is performed on a Mediator, the Security
Considerations for Mediators in Section 9 of <xref target="RFC6183"/>
apply as well.</t>
<t>As mentioned in <xref target="sec-usecase"/>, certain aggregation
operations may tend to have an anyonymizing effect on flow data by
obliterating sensitive identifiers. Aggregation may also be combined
with anonymization within a Mediator, or as part of a chain of
Mediators, to further leverage this effect. In any case in which an
Intermediate Aggregation Process is applied as part of a data
anonymization or protection scheme, or is used together with
anonymization as described in <xref target="RFC6235"/>, the Security
Considerations in Section 9 of <xref target="RFC6235"/> apply.</t>
</section>
<section title="IANA Considerations">
<t>This document specifies the creation of new IPFIX
Information Elements in the IPFIX Information Element registry located
at http://www.iana.org/assignments/ipfix, as defined in <xref
target="sec-export"></xref> above. IANA has assigned Information
Element numbers to these Information Elements, and entered them into
the registry.</t>
<t>[NOTE for IANA: The text TBDn should be replaced with the
respective assigned Information Element numbers where they appear in
this document. Note that the deltaFlowCount Information Element has
been assigned the number 3, as it is compatible with the corresponding
existing (reserved) NetFlow v9 Information Element. Other Information
Element numbers should be assigned outside the NetFlow V9
compatibility range, as these Information Elements are not supported
by NetFlow V9.]</t>
</section>
<section title="Acknowledgments">
<t>Special thanks to Elisa Boschi for early work on the concepts laid
out in this document. Thanks to Lothar Braun and Christian Henke for
their reviews. This work is materially supported by the European Union
Seventh Framework Programme under grant agreement 257315 (DEMONS).</t>
</section>
</middle>
<back>
<references title="Normative References">
<?rfc include="reference.I-D.ietf-ipfix-protocol-rfc5101bis" ?>
<?rfc include="reference.RFC.2119" ?>
</references>
<references title="Informative References">
<?rfc include="reference.RFC.3917" ?>
<?rfc include="reference.RFC.5103" ?>
<?rfc include="reference.RFC.5153" ?>
<?rfc include="reference.RFC.5470" ?>
<?rfc include="reference.RFC.5472" ?>
<?rfc include="reference.RFC.5476" ?>
<?rfc include="reference.RFC.5610" ?>
<?rfc include="reference.RFC.5655" ?>
<?rfc include="reference.RFC.5835" ?>
<?rfc include="reference.RFC.5982" ?>
<?rfc include="reference.RFC.6183" ?>
<?rfc include="reference.RFC.6235" ?>
<?rfc include="reference.I-D.ietf-ipfix-mediation-protocol" ?>
<?rfc include="reference.I-D.ietf-ipfix-ie-doctors" ?>
<?rfc include="reference.I-D.ietf-ipfix-configuration-model" ?>
<?rfc include="reference.I-D.ietf-ipfix-flow-selection-tech" ?>
<reference anchor='iana-ipfix-assignments'>
<front>
<title>IP Flow Information Export Information Elements (http://www.iana.org/assignments/ipfix/ipfix.xml)</title>
<author surname="Internet Assigned Numbers Authority"/>
<date/>
</front>
</reference>
</references>
</back>
</rfc>
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