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authorThomas Voss <mail@thomasvoss.com> 2024-11-27 20:54:24 +0100
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+Internet Engineering Task Force (IETF) B. Trammell
+Request for Comments: 7015 ETH Zurich
+Category: Standards Track A. Wagner
+ISSN: 2070-1721 Consecom AG
+ B. Claise
+ Cisco Systems, Inc.
+ September 2013
+
+
+ Flow Aggregation for the IP Flow Information Export (IPFIX) Protocol
+
+Abstract
+
+ This document provides a common implementation-independent basis for
+ the interoperable application of the IP Flow Information Export
+ (IPFIX) protocol to the handling of Aggregated Flows, which are IPFIX
+ Flows representing packets from multiple Original Flows sharing some
+ set of common properties. It does this through a detailed
+ terminology and a descriptive Intermediate Aggregation Process
+ architecture, including a specification of methods for Original Flow
+ counting and counter distribution across intervals.
+
+Status of This Memo
+
+ This is an Internet Standards Track document.
+
+ This document is a product of the Internet Engineering Task Force
+ (IETF). It represents the consensus of the IETF community. It has
+ received public review and has been approved for publication by the
+ Internet Engineering Steering Group (IESG). Further information on
+ Internet Standards is available in Section 2 of RFC 5741.
+
+ Information about the current status of this document, any errata,
+ and how to provide feedback on it may be obtained at
+ http://www.rfc-editor.org/info/rfc7015.
+
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+Trammell, et al. Standards Track [Page 1]
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+RFC 7015 IPFIX Aggregation September 2013
+
+
+Copyright Notice
+
+ Copyright (c) 2013 IETF Trust and the persons identified as the
+ document authors. All rights reserved.
+
+ This document is subject to BCP 78 and the IETF Trust's Legal
+ Provisions Relating to IETF Documents
+ (http://trustee.ietf.org/license-info) in effect on the date of
+ publication of this document. Please review these documents
+ carefully, as they describe your rights and restrictions with respect
+ to this document. Code Components extracted from this document must
+ include Simplified BSD License text as described in Section 4.e of
+ the Trust Legal Provisions and are provided without warranty as
+ described in the Simplified BSD License.
+
+Table of Contents
+ 1. Introduction ....................................................3
+ 1.1. IPFIX Protocol Overview ....................................4
+ 1.2. IPFIX Documents Overview ...................................5
+ 2. Terminology .....................................................5
+ 3. Use Cases for IPFIX Aggregation .................................7
+ 4. Architecture for Flow Aggregation ...............................8
+ 4.1. Aggregation within the IPFIX Architecture ..................8
+ 4.2. Intermediate Aggregation Process Architecture .............12
+ 4.2.1. Correlation and Normalization ......................14
+ 5. IP Flow Aggregation Operations .................................15
+ 5.1. Temporal Aggregation through Interval Distribution ........15
+ 5.1.1. Distributing Values across Intervals ...............16
+ 5.1.2. Time Composition ...................................18
+ 5.1.3. External Interval Distribution .....................19
+ 5.2. Spatial Aggregation of Flow Keys ..........................19
+ 5.2.1. Counting Original Flows ............................21
+ 5.2.1. Counting Distinct Key Values .......................22
+ 5.3. Spatial Aggregation of Non-key Fields .....................22
+ 5.3.1. Counter Statistics .................................22
+ 5.3.2. Derivation of New Values from Flow Keys and
+ Non-key fields .....................................23
+ 5.4. Aggregation Combination ...................................23
+ 6. Additional Considerations and Special Cases in Flow
+ Aggregation ....................................................24
+ 6.1. Exact versus Approximate Counting during Aggregation ......24
+ 6.2. Delay and Loss Introduced by the IAP ......................24
+ 6.3. Considerations for Aggregation of Sampled Flows ...........24
+ 6.4. Considerations for Aggregation of Heterogeneous Flows .....25
+ 7. Export of Aggregated IP Flows Using IPFIX ......................25
+ 7.1. Time Interval Export ......................................25
+ 7.2. Flow Count Export .........................................25
+ 7.2.1. originalFlowsPresent ...............................26
+
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+ 7.2.2. originalFlowsInitiated .............................26
+ 7.2.3. originalFlowsCompleted .............................26
+ 7.2.4. deltaFlowCount .....................................26
+ 7.3. Distinct Host Export ......................................27
+ 7.3.1. distinctCountOfSourceIPAddress .....................27
+ 7.3.2. distinctCountOfDestinationIPAddress ................27
+ 7.3.3. distinctCountOfSourceIPv4Address ...................27
+ 7.3.4. distinctCountOfDestinationIPv4Address ..............28
+ 7.3.5. distinctCountOfSourceIPv6Address ...................28
+ 7.3.6. distinctCountOfDestinationIPv6Address ..............28
+ 7.4. Aggregate Counter Distribution Export .....................28
+ 7.4.1. Aggregate Counter Distribution Options Template ....29
+ 7.4.2. valueDistributionMethod Information Element ........29
+ 8. Examples .......................................................31
+ 8.1. Traffic Time Series per Source ............................32
+ 8.2. Core Traffic Matrix .......................................37
+ 8.3. Distinct Source Count per Destination Endpoint ............42
+ 8.4. Traffic Time Series per Source with Counter Distribution ..44
+ 9. Security Considerations ........................................46
+ 10. IANA Considerations ...........................................46
+ 11. Acknowledgments ...............................................46
+ 12. References ....................................................47
+ 12.1. Normative References .....................................47
+ 12.2. Informative References ...................................47
+
+1. Introduction
+
+ The assembly of packet data into Flows serves a variety of different
+ purposes, as noted in the requirements [RFC3917] and applicability
+ statement [RFC5472] for the IP Flow Information Export (IPFIX)
+ protocol [RFC7011]. 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 IPFIX Mediation Problem
+ Statement [RFC5982], and the definition of an Intermediate
+ Aggregation Process in the Mediator framework [RFC6183].
+
+ Aggregation is used for analysis and data reduction in a wide variety
+ of applications, for example, in traffic matrix calculation,
+ generation of time series data for visualizations or anomaly
+ detection, or data reduction for long-term trending and storage.
+ Depending on the keys used for aggregation, it may additionally have
+ an anonymizing effect on the data: for example, aggregation
+ operations that eliminate IP addresses make it impossible to later
+ directly identify nodes using those addresses.
+
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+ Aggregation, as defined and described in this document, covers the
+ applications defined in [RFC5982], including Sections 5.1 "Adjusting
+ Flow Granularity", 5.4 "Time Composition", and 5.5 "Spatial
+ Composition". However, Section 4.2 of this document specifies a more
+ flexible architecture for an Intermediate Aggregation Process than
+ that envisioned by the original Mediator work [RFC5982]. 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". This architecture is intended to
+ describe any such activity without reference to the specific
+ implementation of aggregation.
+
+ An Intermediate Aggregation Process may be applied to data collected
+ from multiple Observation Points, as it is natural to use aggregation
+ 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
+ Mediation Protocol document [IPFIX-MED-PROTO].
+
+ Since Aggregated Flows as defined in the following section are
+ essentially Flows, the IPFIX protocol [RFC7011] can be used to
+ export, and the IPFIX File Format [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. Note that Sections 5, 6, and 7 of this document are
+ normative.
+
+1.1. IPFIX Protocol Overview
+
+ 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 [RFC7011] for more information.
+
+ The IPFIX Information Element Registry [IANA-IPFIX] defines a large
+ number of standard Information Elements that provide the necessary {
+ type } information for Templates. The use of standard elements
+ enables interoperability among different vendors' implementations.
+
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+ Additionally, non-standard enterprise-specific elements may be
+ defined for private use.
+
+1.2. IPFIX Documents Overview
+
+ "Specification of the IP Flow Information Export (IPFIX) Protocol for
+ the Exchange of Flow Information" [RFC7011] and its associated
+ documents define the IPFIX protocol, which provides network engineers
+ and administrators with access to IP traffic Flow information.
+
+ IPFIX has a formal description of IPFIX Information Elements, their
+ names, types, and additional semantic information, as specified in
+ the IPFIX Information Model [RFC7012]. The IPFIX Information Element
+ registry [IANA-IPFIX] is maintained by IANA. New Information Element
+ definitions can be added to this registry subject to an Expert Review
+ [RFC5226], with additional process considerations described in
+ [RFC7013].
+
+ "Architecture for IP Flow Information Export" [RFC5470] 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 "Requirements for IP Flow Information
+ Export" [RFC3917]. The IPFIX protocol document [RFC7011] 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.
+
+ "IP Flow Information Export (IPFIX) Mediation: Problem Statement"
+ [RFC5982] introduces the concept of IPFIX Mediators, and defines the
+ use cases for which they were designed; "IP Flow Information Export
+ (IPFIX) Mediation: Framework" [RFC6183] then provides an
+ architectural framework for Mediators. Protocol-level issues (e.g.,
+ Template and Observation Domain handling across Mediators) are
+ covered by "Operation of the IP Flow Information Export (IPFIX)
+ Protocol on IPFIX Mediators" [IPFIX-MED-PROTO].
+
+ This document specifies an Intermediate Process for Flow aggregation
+ that may be applied at an IPFIX Mediator, as well as at an original
+ Observation Point prior to export, or for analysis and data reduction
+ purposes after receipt at a Collecting Process.
+
+2. Terminology
+
+ Terms used in this document that are defined in the Terminology
+ section of the IPFIX protocol document [RFC7011] are to be
+ interpreted as defined there.
+
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+ The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
+ "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
+ document are to be interpreted as described in [RFC2119].
+
+ In addition, this document defines the following terms:
+
+ Aggregated Flow: A Flow, as defined by [RFC7011], derived from a set
+ of zero or more Original Flows within a defined Aggregation
+ 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 [RFC7011] and can
+ be treated as such.
+
+ Intermediate Aggregation Process: an Intermediate Aggregation
+ Process (IAP), as in [RFC6183], that aggregates records, based
+ upon a set of Flow Keys or functions applied to fields from the
+ record.
+
+ 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.
+
+ 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 Section 4.2.
+
+ Original Flow: A Flow given as input to an Intermediate Aggregation
+ Process in order to generate Aggregated Flows.
+
+ Contributing Flow: An Original Flow that is partially or completely
+ represented within an Aggregated Flow. Each Aggregated Flow is
+ made up of zero or more Contributing Flows, and an Original Flow
+ may contribute to zero or more Aggregated Flows.
+
+ Original Exporter: The Exporter from which the Original Flows are
+ received; meaningful only when an IAP is deployed at a Mediator.
+
+ The terminology presented herein improves the precision of, but does
+ not supersede or contradict the terms related to, Mediation and
+ aggregation defined in the Mediation Problem Statement [RFC5982] and
+ the Mediation Framework [RFC6183] documents. Within this document,
+ the terminology defined in this section is to be considered
+ normative.
+
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+3. Use Cases for IPFIX Aggregation
+
+ Aggregation, as a common data reduction method used in traffic data
+ analysis, has many applications. When used with a regular
+ Aggregation Interval and Original Flows containing timing
+ information, it generates time series data from a collection of Flows
+ with discrete intervals, as in the example in Section 8.1. 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, as shown in Section 8.2 is inherently an aggregation
+ action, by spatially aggregating the Flow Key down to input or output
+ interface, address prefix, or autonomous system (AS).
+
+ 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.
+
+ Aggregation can also be applied to final analysis of stored Flow
+ data, as shown in the example in Section 8.3. All such aggregation
+ applications in which timing information is not available or not
+ important can be treated as if an infinite Aggregation Interval
+ applies.
+
+ Note that an Intermediate Aggregation Process that removes
+ potentially sensitive information as identified in [RFC6235] may tend
+ to have an anonymizing 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 [RFC6235] are
+ addressed, specifically Sections 4 ("Anonymization of IP Flow Data"),
+ 7.2 ("IPFIX-Specific Anonymization Guidelines"), and 9 ("Security
+ Considerations").
+
+ 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 Templates or compatible Templates containing a core
+ set Information Elements that can be freely converted to one
+ another), and that each packet observed by the Metering Process
+ associated with the Original Exporter is represented, this is not a
+ necessary assumption. Aggregation can also be applied as part of a
+
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+ technique using 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 be used to produce Aggregated Flows
+ containing both volume and performance information together. The
+ correlation and normalization operation described in Section 4.2.1
+ handles this specific case of correlation. Flow correlation in the
+ general case is outside the scope of this document.
+
+4. Architecture for Flow Aggregation
+
+ This section specifies the architecture of the Intermediate
+ Aggregation Process and how it fits into the IPFIX architecture.
+
+4.1. Aggregation within the IPFIX Architecture
+
+ 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 that 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
+ Figure 1. The presence of an IAP at any of these points is, of
+ course, optional.
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+ +===========================================+
+ | IPFIX Exporter +----------------+ |
+ | | Metering Proc. | |
+ | +-----------------+ +----------------+ |
+ | | Metering Proc. | or | IAP | |
+ | +-----------------+----+----------------+ |
+ | | Exporting Process | |
+ | +-|----------------------------------|--+ |
+ +===|==================================|====+
+ | |
+ +===|===========================+ |
+ | | Aggregating Mediator | |
+ + +-V-------------------+ | |
+ | | Collecting Process | | |
+ + +---------------------+ | |
+ | | IAP | | |
+ + +---------------------+ | |
+ | | Exporting Process | | |
+ + +-|-------------------+ | |
+ +===|===========================+ |
+ | |
+ +===|==================================|=====+
+ | | Collector | |
+ | +-V----------------------------------V-+ |
+ | | Collecting Process | |
+ | +------------------+-------------------+ |
+ | | IAP | |
+ | +-------------------+ |
+ | (Aggregation | File Writer | |
+ for Storage) +-----------|-------+ |
+ +================================|===========+
+ |
+ +------V-----------+
+ | IPFIX File |
+ +------------------+
+
+ Figure 1: Potential Aggregation Locations
+
+ The Mediator use case is further shown in Figures A and B in
+ [RFC6183].
+
+ Aggregation can be applied for either intermediate or final analytic
+ purposes. In certain circumstances, it may make sense to export
+ Aggregated Flows directly after metering, 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
+
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+ Process, is basically covered by the IPFIX architecture [RFC5470]:
+ 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 Intermediate
+ Aggregation Process deployed in a separate location.
+
+ In the specific case that an Intermediate 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.
+
+ Deployment of an Intermediate Aggregation Process within a Mediator
+ [RFC5982] is a much more flexible arrangement. Here, the Mediator
+ consumes Original Flows and produces Aggregated Flows; this
+ arrangement is suited to any of the use cases detailed in Section 3.
+ In a Mediator, Original Flows from multiple sources can also be
+ aggregated 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 [IPFIX-MED-PROTO] for details.
+
+ The data paths into and out of an Intermediate Aggregation Process
+ are shown in Figure 2.
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+ packets --+ IPFIX Messages IPFIX Files
+ | | |
+ V V V
+ +==================+ +====================+ +=============+
+ | Metering Process | | Collecting Process | | File Reader |
+ | | +====================+ +=============+
+ | (Original Flows | | |
+ | or direct | | Original Flows |
+ | aggregation) | V V
+ + - - - - - - - - -+======================================+
+ | Intermediate Aggregation Process (IAP) |
+ +=========================================================+
+ | Aggregated Aggregated |
+ | Flows Flows |
+ V V
+ +===================+ +=============+
+ | Exporting Process | | File Writer |
+ +===================+ +=============+
+ | |
+ V V
+ IPFIX Messages IPFIX Files
+
+ Figure 2: Data Paths through the Aggregation Process
+
+ Note that as Aggregated Flows are IPFIX Flows, an Intermediate
+ Aggregation Process may aggregate already Aggregated Flows from an
+ upstream IAP as well as Original Flows from an upstream Original
+ Exporter or Metering Process.
+
+ 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 Figure
+ 3; in this case, the correlation and normalization operation
+ described in Section 4.2.1 handles merging the Original Flows before
+ aggregation.
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+ 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 <----------+
+
+ Figure 3: Aggregating Original Flows from Multiple Metering Processes
+
+4.2. Intermediate Aggregation Process Architecture
+
+ 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 Figure 4: interval
+ distribution (temporal), key aggregation (spatial), value aggregation
+ (spatial), and aggregate combination. "Partially Aggregated Flows",
+ as defined in Section 2, are essentially the intermediate results of
+ aggregation, internal to the Intermediate Aggregation Process.
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+ Original Flows / Original Flows requiring correlation
+ +=============|===================|===================|=============+
+ | | Intermediate | Aggregation | Process |
+ | | V V |
+ | | +-----------------------------------------------+ |
+ | | | (optional) correlation and normalization | |
+ | | +-----------------------------------------------+ |
+ | | | |
+ | V V |
+ | +--------------------------------------------------------------+ |
+ | | interval distribution (temporal) | |
+ | +--------------------------------------------------------------+ |
+ | | ^ | ^ | |
+ | | | Partially Aggregated | | | |
+ | V | Flows V | | |
+ | +-------------------+ +--------------------+ | |
+ | | key aggregation |<------| value aggregation | | |
+ | | (spatial) |------>| (spatial) | | |
+ | +-------------------+ +--------------------+ | |
+ | | | | |
+ | | Partially Aggregated | | |
+ | V Flows V V |
+ | +--------------------------------------------------------------+ |
+ | | aggregate combination | |
+ | +--------------------------------------------------------------+ |
+ | | |
+ +=======================================|===========================+
+ V
+ Aggregated Flows
+
+ Figure 4: Conceptual Model of Aggregation Operations within an IAP
+
+ Interval distribution: a temporal aggregation operation that imposes
+ an Aggregation Interval on the Partially Aggregated 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 Section 5.1.
+
+ Key aggregation: a spatial aggregation operation that 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 (ASN) for an IP
+ address), or "promoted" from specific 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 Section 5.2.
+
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+ Value aggregation: a spatial aggregation operation that 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 Section 5.3.
+
+ Aggregate combination: an operation combining multiple Partially
+ Aggregated Flows having undergone interval distribution, key
+ aggregation, and value aggregation that share Flow Keys and
+ Aggregation Intervals into a single Aggregated Flow per set of
+ Flow Key values and Aggregation Interval. Aggregate combination
+ is discussed in detail in Section 5.4.
+
+ Correlation and normalization: an optional operation that applies
+ when accepting Original Flows from Metering Processes that export
+ different views of essentially the same Flows before aggregation.
+ The details of correlation and normalization are specified in
+ Section 4.2.1, below.
+
+ 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 above, 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.
+
+ 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.
+
+4.2.1. Correlation and Normalization
+
+ 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
+
+
+
+
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+
+
+ 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 that used the union of the
+ set of Flow Keys and non-key fields of all Metering Processes sending
+ Original Flows to the IAP.
+
+ 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 corrections that are
+ specific to the aggregation application in order to ensure
+ consistency in the unified Flows.
+
+ 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 that 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.
+
+ 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.
+
+5. IP Flow Aggregation Operations
+
+ As stated in Section 2, 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.
+
+5.1. Temporal Aggregation through Interval Distribution
+
+ 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 time containing the set of all Flows sharing a given Key,
+ as in Time Composition, described in Section 5.1.2) 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).
+
+ The length of the imposed interval itself has trade-offs. Shorter
+ intervals allow higher-resolution aggregated data and, in streaming
+ applications, faster reaction time. Longer intervals generally lead
+
+
+
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+
+ 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.
+
+ | | | |
+ | |<--Flow A-->| | | |
+ | |<--Flow B-->| | |
+ | |<-------------Flow C-------------->| |
+ | | | |
+ | interval 0 | interval 1 | interval 2 |
+
+ Figure 5: Illustration of Interval Distribution
+
+ In Figure 5, 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 Section 5.1.1.
+
+5.1.1. Distributing Values across Intervals
+
+ 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 example,
+ delta counters are summed, the most recent total count for each
+ Original Flow taken then summed across Flows, and so on.
+
+ 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
+ to the last Aggregated Flow to which it could contribute.
+
+
+
+
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+
+
+ 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 Intermediate Aggregation Process must distribute the
+ counters in the Original Flows across one or more resulting
+ 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.
+
+ End Interval: The counters for an Original Flow are added to the
+ counters of the appropriate Aggregated Flow containing the end
+ time of the Original Flow.
+
+ Start Interval: The counters for an Original Flow are added to the
+ counters of the appropriate Aggregated Flow containing the start
+ time of the Original Flow.
+
+ Mid Interval: The counters for an Original Flow are added to the
+ counters of a single appropriate Aggregated Flow containing some
+ timestamp between start and end time of the Original Flow.
+
+ Simple Uniform Distribution: Each counter for an Original Flow is
+ divided by the number of time intervals the Original Flow covers
+ (i.e., of appropriate Aggregated Flows sharing the same Flow
+ Keys), and this number is added to each corresponding counter in
+ each Aggregated Flow.
+
+ 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.
+
+ Simulated Process: Each counter of the Original Flow is distributed
+ among the intervals of the Aggregated Flows according to some
+ function the Intermediate 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.
+
+ Direct: The Intermediate 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.
+
+
+
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+
+
+ A method for exporting the distribution of counters across multiple
+ Aggregated Flows is detailed in Section 7.4. 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. This property allows data to be aggregated
+ and re-aggregated with negligible loss of original count information.
+ To avoid confusion in interpretation of the aggregated data, all the
+ counters in a given Aggregated Flow MUST be distributed via the same
+ method.
+
+ 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.
+
+ 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 reasons specific
+ to the implementation of the Metering Process or Exporting Process,
+ even simpler distribution methods may need to deal with Flows
+ arriving in an order other than start time or end time. Therefore,
+ the use of larger intervals does not obviate the need to buffer
+ Partially Aggregated Flows within "current" time intervals, to ensure
+ the IAP 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 it MUST
+ 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 Section 6.2.
+
+5.1.2. Time Composition
+
+ Time Composition, as in Section 5.4 of [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 that 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 it may be applied in combination with other
+ aggregation functions in order to obtain more accurate Original Flow
+ counts.
+
+
+
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+
+
+5.1.3. External Interval Distribution
+
+ Note that much of the difficulty of interval distribution at an IAP
+ can be avoided simply by configuring the original Exporters to
+ synchronize the time intervals in the Original Flows with the desired
+ aggregation interval. The resulting Original Flows would then be
+ split to align perfectly with the time intervals imposed during
+ interval imposition, as shown in Figure 6, though this may reduce
+ their usefulness for non-aggregation purposes. This approach allows
+ the Intermediate Aggregation Process to use Start Interval or End
+ Interval distribution, while having equivalent information to that
+ available to direct interval distribution.
+
+ | | | |
+ |<----Flow D---->|<----Flow E---->|<----Flow F---->|
+ | | | |
+ | interval 0 | interval 1 | interval 2 |
+
+ Figure 6: Illustration of External Interval Distribution
+
+5.2. Spatial Aggregation of Flow Keys
+
+ Key aggregation generates a new set of Flow Key values for the
+ Aggregated Flows from the Original Flow Key and 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 they may be derived by 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 that 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.
+
+ 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 Sections
+ 5.2.1 and 5.2.2, respectively.
+
+ 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.
+
+
+
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+
+
+ 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 /24 network)
+
+ Figure 7: Illustration of Key Aggregation by Reduction
+
+ Figure 7 illustrates an example reduction operation, aggregation by
+ source address and destination /24 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 lower 8 bits.
+
+ Original Flow Keys
+
+ +---------+---------+----------+----------+-------+-----+
+ | src ip4 | dst ip4 | src port | dst port | proto | tos |
+ +---------+---------+----------+----------+-------+-----+
+ | | | | | |
+ V V | | | |
+ +-------------------+ X X X X
+ | ASN lookup table |
+ +-------------------+
+ | |
+ V V
+ +---------+---------+
+ | src asn | dst asn |
+ +---------+---------+
+
+ Aggregated Flow Keys (by source and destination ASN)
+
+ Figure 8: Illustration of Key Aggregation
+ by Reduction and Replacement
+
+ Figure 8 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
+
+
+
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+
+
+ 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.
+
+5.2.1. Counting Original Flows
+
+ When aggregating multiple Original Flows into an Aggregated Flow, it
+ is often useful to know how many Original Flows are present in the
+ Aggregated Flow. Section 7.2 introduces four new Information Elements
+ to export these counters.
+
+ There are two possible ways to count Original Flows, which we call
+ conservative and non-conservative. Conservative Flow counting has
+ the property that each Original Flow contributes exactly one to the
+ total Flow count within a set of Aggregated Flows. In other words,
+ conservative Flow counters are distributed just as any other counter
+ 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 that does not account for that Original Flow
+ completely within a single Aggregated Flow, conservative Flow
+ counting requires a fractional representation.
+
+ By contrast, non-conservative Flow counting is used to count how many
+ Contributing Flows are represented in an Aggregated Flow. Flow
+ counters are not distributed in this case. An Original Flow that is
+ present within N Aggregated Flows would add N to the sum of non-
+ conservative Flow counts, one to each Aggregated Flow. In other
+ words, the sum of conservative Flow counts over a set of Aggregated
+ Flows is always equal to the number of Original Flows, while the sum
+ of non-conservative Flow counts is strictly greater than or equal to
+ the number of Original Flows.
+
+ For example, consider Flows A, B, and C as illustrated in Figure 5.
+ 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.
+
+
+
+
+
+
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+
+
+ 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,
+ 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 [RFC6728]. Original Exporters not using the IPFIX
+ Configuration Model SHOULD be configured equivalently.
+
+5.2.2. Counting Distinct Key Values
+
+ 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.
+
+ For such applications, Information Elements for distinct counts of
+ IPv4 and IPv6 addresses are defined in Section 7.3. These are named
+ distinctCountOf(KeyName). Additional such Information Elements
+ should be registered with IANA on an as-needed basis.
+
+5.3. Spatial Aggregation of Non-key Fields
+
+ Aggregation operations may also lead to the addition of value fields
+ that are demoted from key fields or are derived from other value
+ fields in the Original Flows. Specific cases of this are treated in
+ the subsections below.
+
+5.3.1. Counter Statistics
+
+ Some applications of aggregation may benefit from computing different
+ statistics than those native to each non-key field (e.g., flags are
+ natively combined via union and delta counters by summing). 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 [IANA-IPFIX] (e.g.,
+ minimumIpTotalLength).
+
+ 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.
+
+
+
+
+
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+
+
+5.3.2. Derivation of New Values from Flow Keys and Non-key fields
+
+ 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 it is similar to
+ distinct key counting as in Section 5.2.2; however, it may be applied
+ to the distribution of values for any Flow field.
+
+ Sample entropy calculation provides a one-number normalized
+ representation of the value spread and is useful for anomaly
+ detection. The behavior 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.
+
+ Entropy statistics are generally useful for identifier keys, such as
+ IP addresses, port numbers, AS numbers, etc. They can also be
+ calculated on Flow length, Flow duration fields, and the like, even
+ if this generally yields less distinct value shifts when the traffic
+ mix changes.
+
+ 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 Distributed Denial of Service (DDoS) flooding attack against a
+ single target (or small number of targets) that drives source IP
+ entropy up and target IP entropy down.
+
+ 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.
+
+5.4. Aggregation Combination
+
+ 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. Delta counters are summed, flags are unioned, and so on.
+
+
+
+
+
+
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+
+
+6. Additional Considerations and Special Cases in Flow Aggregation
+
+6.1. Exact versus Approximate Counting during Aggregation
+
+ In certain circumstances, particularly involving aggregation by
+ devices with limited resources, and in situations where exact
+ aggregated counts are less important than relative magnitudes (e.g.,
+ driving graphical displays), counter distribution during key
+ aggregation may be performed by approximate counting means (e.g.,
+ Bloom filters). The choice to use approximate counting is
+ implementation and application dependent.
+
+6.2. Delay and Loss Introduced by the IAP
+
+ When accepting Original Flows in export order from traffic captured
+ live, the Intermediate Aggregation Process waits for all Original
+ Flows that 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 five-minute active timeout, the
+ Intermediate Aggregation Process introduces a delay of at least five
+ minutes to all exported Aggregated Flows to ensure it has received
+ all Original Flows. Note that when aggregating Flows from multiple
+ Metering Processes with different active timeouts, the delay is
+ determined by the maximum active timeout.
+
+ 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
+ MAY drop the late Original Flow(s). Accounting of Flows lost at an
+ Intermediate Process due to such issues is covered in
+ [IPFIX-MED-PROTO].
+
+6.3. Considerations for Aggregation of Sampled Flows
+
+ The accuracy of Aggregated Flows may also be affected by sampling of
+ the Original Flows, or sampling of packets making up the Original
+ Flows. At the time of writing, 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 be applied to Original Flows sampled using the same
+ sampling rate and sampling algorithm, Flows created from packets
+ sampled using the same sampling rate and sampling algorithm, or
+ Original Flows that have been normalized as if they had the same
+ sampling rate and algorithm before aggregation. For more on packet
+ sampling within IPFIX, see [RFC5476]. For more on Flow sampling
+ within the IPFIX Mediator framework, see [RFC7014].
+
+
+
+
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+
+
+6.4. Considerations for Aggregation of Heterogeneous Flows
+
+ 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.
+
+ 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.
+
+7. Export of Aggregated IP Flows Using IPFIX
+
+ In general, Aggregated Flows are exported in IPFIX as any other Flow.
+ However, certain aspects of Aggregated Flow export benefit from
+ additional guidelines or new Information Elements to represent
+ aggregation metadata or information generated during aggregation.
+ These are detailed in the following subsections.
+
+7.1. Time Interval Export
+
+ Since an Aggregated Flow is simply a Flow, the existing timestamp
+ Information Elements in the IPFIX Information Model (e.g.,
+ flowStartMilliseconds, flowEndNanoseconds) are sufficient to specify
+ the time interval for aggregation. Therefore, no new aggregation-
+ specific Information Elements for exporting time interval information
+ are necessary.
+
+ Each Aggregated Flow carrying timing information SHOULD contain both
+ an interval start and interval end timestamp.
+
+7.2. Flow Count Export
+
+ The following four Information Elements are defined to count Original
+ Flows as discussed in Section 5.2.1.
+
+
+
+
+
+
+
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+
+
+7.2.1. originalFlowsPresent
+
+ Description: The non-conservative count of Original Flows
+ contributing to this Aggregated Flow. Non-conservative counts
+ need not sum to the original count on re-aggregation.
+
+ Abstract Data Type: unsigned64
+
+ Data Type Semantics: deltaCounter
+
+ ElementID: 375
+
+7.2.2. originalFlowsInitiated
+
+ 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.
+
+ Abstract Data Type: unsigned64
+
+ Data Type Semantics: deltaCounter
+
+ ElementID: 376
+
+7.2.3. originalFlowsCompleted
+
+ 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.
+
+ Abstract Data Type: unsigned64
+
+ Data Type Semantics: deltaCounter
+
+ ElementID: 377
+
+7.2.4. deltaFlowCount
+
+ Description: The conservative count of Original Flows contributing
+ to this Aggregated Flow; may be distributed via any of the methods
+ expressed by the valueDistributionMethod Information Element.
+
+ Abstract Data Type: unsigned64
+
+ Data Type Semantics: deltaCounter
+
+ ElementID: 3
+
+
+
+
+Trammell, et al. Standards Track [Page 26]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+7.3. Distinct Host Export
+
+ The following six Information Elements represent the distinct counts
+ of source and destination network-layer addresses used to export
+ distinct host counts reduced away during key aggregation.
+
+7.3.1. distinctCountOfSourceIPAddress
+
+ Description: The count of distinct source IP address values for
+ Original Flows contributing to this Aggregated Flow, without
+ regard to IP version. This Information Element is preferred to
+ the IP-version-specific counters, unless it is important to
+ separate the counts by version.
+
+ Abstract Data Type: unsigned64
+
+ Data Type Semantics: totalCounter
+
+ ElementID: 378
+
+7.3.2. distinctCountOfDestinationIPAddress
+
+ Description: The count of distinct destination IP address values for
+ Original Flows contributing to this Aggregated Flow, without
+ regard to IP version. This Information Element is preferred to
+ the version-specific counters below, unless it is important to
+ separate the counts by version.
+
+ Abstract Data Type: unsigned64
+
+ Data Type Semantics: totalCounter
+
+ ElementID: 379
+
+7.3.3. distinctCountOfSourceIPv4Address
+
+ Description: The count of distinct source IPv4 address values for
+ Original Flows contributing to this Aggregated Flow.
+
+ Abstract Data Type: unsigned32
+
+ Data Type Semantics: totalCounter
+
+ ElementID: 380
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 27]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+7.3.4. distinctCountOfDestinationIPv4Address
+
+ Description: The count of distinct destination IPv4 address values
+ for Original Flows contributing to this Aggregated Flow.
+
+ Abstract Data Type: unsigned32
+
+ Data Type Semantics: totalCounter
+
+ ElementID: 381
+
+7.3.5. distinctCountOfSourceIPv6Address
+
+ Description: The count of distinct source IPv6 address values for
+ Original Flows contributing to this Aggregated Flow.
+
+ Abstract Data Type: unsigned64
+
+ Data Type Semantics: totalCounter
+
+ ElementID: 382
+
+7.3.6. distinctCountOfDestinationIPv6Address
+
+ Description: The count of distinct destination IPv6 address values
+ for Original Flows contributing to this Aggregated Flow.
+
+ Abstract Data Type: unsigned64
+
+ Data Type Semantics: totalCounter
+
+ ElementID: 383
+
+7.4. Aggregate Counter Distribution Export
+
+ When exporting counters distributed among Aggregated Flows, as
+ described in Section 5.1.1, the Exporting Process MAY export an
+ Aggregate Counter Distribution Option 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 the 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.
+
+
+
+
+Trammell, et al. Standards Track [Page 28]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+7.4.1. Aggregate Counter Distribution Options Template
+
+ This Options Template defines the Aggregate Counter Distribution
+ Record, which allows the binding of a value distribution method to a
+ Template ID. The scope is the Template ID, whose uniqueness, per
+ [RFC7011], is local to the Transport Session and Observation Domain
+ that generated the Template ID. This is used to signal to the
+ Collecting Process how the counters were distributed. The fields are
+ as below:
+
+ +-----------------------------+-------------------------------------+
+ | IE | Description |
+ +-----------------------------+-------------------------------------+
+ | templateId [scope] | The Template ID of the Template |
+ | | defining the Aggregated Flows to |
+ | | which this distribution option |
+ | | applies. This Information Element |
+ | | MUST be defined as a Scope field. |
+ | valueDistributionMethod | The method used to distribute the |
+ | | counters for the Aggregated Flows |
+ | | defined by the associated Template. |
+ +-----------------------------+-------------------------------------+
+
+7.4.2. valueDistributionMethod Information Element
+
+ Description: A description of the method used to distribute the
+ counters from Contributing Flows into the Aggregated Flow records
+ described by an associated scope, generally a Template. The
+ method is deemed to apply to all the non-Key Information Elements
+ in the referenced scope 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. This is intended
+ to be a complete set of possible value distribution methods; it is
+ encoded as follows:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 29]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ +-------+-----------------------------------------------------------+
+ | Value | Description |
+ +-------+-----------------------------------------------------------+
+ | 0 | Unspecified: The counters for an Original Flow are |
+ | | explicitly not distributed according to any other method |
+ | | defined for this Information Element; use for arbitrary |
+ | | distribution, or distribution algorithms not described by |
+ | | any other codepoint. |
+ | | --------------------------------------------------------- |
+ | | |
+ | 1 | Start Interval: The counters for an Original Flow are |
+ | | added to the counters of the appropriate Aggregated Flow |
+ | | containing the start time of the Original Flow. This |
+ | | should be assumed the default if value distribution |
+ | | information is not available at a Collecting Process for |
+ | | an Aggregated Flow. |
+ | | --------------------------------------------------------- |
+ | | |
+ | 2 | End Interval: The counters for an Original Flow are added |
+ | | to the counters of the appropriate Aggregated Flow |
+ | | containing the end time of the Original Flow. |
+ | | --------------------------------------------------------- |
+ | | |
+ | 3 | Mid Interval: The counters for an Original Flow are added |
+ | | to the counters of a single appropriate Aggregated Flow |
+ | | containing some timestamp between start and end time of |
+ | | the Original Flow. |
+ | | --------------------------------------------------------- |
+ | | |
+ | 4 | Simple Uniform Distribution: Each counter for an Original |
+ | | Flow is divided by the number of time intervals the |
+ | | Original Flow covers (i.e., of appropriate Aggregated |
+ | | Flows sharing the same Flow Key), and this number is |
+ | | added to each corresponding counter in each Aggregated |
+ | | Flow. |
+ | | --------------------------------------------------------- |
+ | | |
+ | 5 | Proportional Uniform Distribution: Each counter for an |
+ | | Original Flow is divided by the number of time units the |
+ | | Original Flow covers, to derive a mean count rate. This |
+ | | mean count rate is then multiplied by the number of time |
+ | | units in the intersection of the duration of the Original |
+ | | Flow and the time interval of each Aggregated Flow. |
+ | | This is like simple uniform distribution, but accounts |
+ | | for the fractional portions of a time interval covered by |
+ | | an Original Flow in the first and last time interval. |
+ | | --------------------------------------------------------- |
+
+
+
+
+Trammell, et al. Standards Track [Page 30]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ | | --------------------------------------------------------- |
+ | 6 | Simulated Process: Each counter of the Original Flow is |
+ | | distributed among the intervals of the Aggregated Flows |
+ | | according to some function the Intermediate Aggregation |
+ | | Process uses based upon properties of Flows presumed to |
+ | | be like the Original Flow. This is essentially an |
+ | | assertion that the Intermediate Aggregation Process has |
+ | | no direct packet timing information but is nevertheless |
+ | | not using one of the other simpler distribution methods. |
+ | | The Intermediate Aggregation Process specifically makes |
+ | | no assertion as to the correctness of the simulation. |
+ | | --------------------------------------------------------- |
+ | | |
+ | 7 | Direct: The Intermediate Aggregation Process has access |
+ | | to the original packet timings from the packets making up |
+ | | the Original Flow, and uses these to distribute or |
+ | | recalculate the counters. |
+ +-------+-----------------------------------------------------------+
+
+ Abstract Data Type: unsigned8
+
+ ElementID: 384
+
+8. Examples
+
+ In these examples, the same data, described by the same Template,
+ will be aggregated multiple different ways; this illustrates the
+ various different functions that could be implemented by Intermediate
+ Aggregation Processes. Templates are shown in IESpec format as
+ introduced in [RFC7013]. The source data format is a simplified
+ Flow: timestamps, traditional 5-tuple, and octet count; the Flow Key
+ fields are the 5-tuple. The Template is shown in Figure 9.
+
+ flowStartMilliseconds(152)[8]
+ flowEndMilliseconds(153)[8]
+ sourceIPv4Address(8)[4]{key}
+ destinationIPv4Address(12)[4]{key}
+ sourceTransportPort(7)[2]{key}
+ destinationTransportPort(11)[2]{key}
+ protocolIdentifier(4)[1]{key}
+ octetDeltaCount(1)[8]
+
+ Figure 9: Input Template for Examples
+
+ The data records given as input to the examples in this section are
+ shown below; timestamps are given in H:MM:SS.sss format. In this and
+ subsequent figures, flowStartMilliseconds is shown in H:MM:SS.sss
+ format as 'start time', flowEndMilliseconds is shown in H:MM:SS.sss
+
+
+
+Trammell, et al. Standards Track [Page 31]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ format as 'end time', sourceIPv4Address is shown as 'source ip4' with
+ the following 'port' representing sourceTransportPort,
+ destinationIPv4Address is shown as 'dest ip4' with the following
+ 'port' representing destinationTransportPort, protocolIdentifier is
+ shown as 'pt', and octetDeltaCount as 'oct'.
+
+ start time |end time |source ip4 |port |dest ip4 |port|pt| oct
+ 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 198.51.100.133 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
+
+ Figure 10: Input Data for Examples
+
+8.1. Traffic Time Series per Source
+
+ 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 five 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.
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 32]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ 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.
+
+ 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 Figure 11.
+
+ flowStartMilliseconds(152)[8]
+ flowEndMilliseconds(153)[8]
+ sourceIPv4Address(8)[4]
+ octetDeltaCount(1)[8]
+
+ Figure 11: Output Template for Time Series per Source
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 33]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ Assume the goal is to get 5-minute (300 s) time series of octet
+ counts per source IP address. The aggregation operations would then
+ be arranged as in Figure 12.
+
+ 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
+
+ Figure 12: Aggregation Operations for Time Series per Source
+
+ After applying the interval distribution step to the source data in
+ Figure 10, only the time intervals have changed; the Partially
+ Aggregated Flows are shown in Figure 13. 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.
+
+
+
+
+
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 34]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ start time |end time |source ip4 |port |dest ip4 |port|pt| oct
+ 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
+
+ Figure 13: Interval Imposition for Time Series per Source
+
+ After the key aggregation step, all Flow Keys except the source IP
+ address have been discarded, as shown in Figure 14. This leaves
+ duplicate Partially Aggregated Flows to be combined in the final
+ operation.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 35]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ start time |end time |source ip4 |octets
+ 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
+ 9:00:00.000 9:05:00.000 192.0.2.2 15420
+ 9:00:00.000 9:05:00.000 203.0.113.3 11200
+ 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
+
+ Figure 14: Key Aggregation for Time Series per Source
+
+ Aggregate combination sums the counters per key and interval; the
+ summations of the first two keys and intervals are shown in detail in
+ Figure 15.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 36]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ start time |end time |source ip4 |octets
+ 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
+
+ Figure 15: Summation during Aggregate Combination
+
+ This can be applied to each set of Partially Aggregated Flows to
+ produce the final Aggregated Flows that are shown in Figure 16, as
+ exported by the Template in Figure 11.
+
+ start time |end time |source ip4 |octets
+ 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 20614
+ 9:10:00.000 9:15:00.000 192.0.2.4 3587
+
+ Figure 16: Aggregated Flows for Time Series per Source
+
+8.2. Core Traffic Matrix
+
+ Aggregating Flows by source and destination ASN 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 it
+ 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.
+
+
+
+
+
+Trammell, et al. Standards Track [Page 37]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ 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 without routing data contain only a
+ destinationIPv[46]Address. For these Flows, the Mediator must look
+ up a next-hop AS from an IP-to-AS table, replacing source and
+ destination addresses with ASNs. The table used in this example is
+ shown in Figure 17. (Note that due to limited example address space,
+ in this example we ignore the common practice of routing only blocks
+ of /24 or larger.)
+
+ 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
+
+ Figure 17: Example ASN Map
+
+ The Template for Aggregated Flows produced by this example is shown
+ in Figure 18.
+
+ flowStartMilliseconds(152)[8]
+ flowEndMilliseconds(153)[8]
+ bgpSourceAsNumber(16)[4]
+ bgpDestinationAsNumber(17)[4]
+ octetDeltaCount(1)[8]
+
+ Figure 18: Output Template for Traffic Matrix
+
+ 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 Figure 19.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 38]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ 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
+
+ Figure 19: Aggregation Operations for Traffic Matrix
+
+ After applying the interval distribution step to the source data in
+ Figure 10, the Partially Aggregated Flows are shown in Figure 20.
+ Note that the Flows are identical to those in the 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.
+
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 39]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ start time |end time |source ip4 |port |dest ip4 |port|pt| oct
+ 9:00:00 10:00:00 192.0.2.2 47113 192.0.2.131 53 17 119
+ 9:00:00 10:00:00 192.0.2.2 22153 192.0.2.131 53 17 83
+ 9:00:00 10:00:00 192.0.2.2 52420 198.51.100.2 443 6 1637
+ 9:00:00 10:00:00 192.0.2.3 56047 192.0.2.131 53 17 111
+ 9:00:00 10:00:00 192.0.2.3 41183 198.51.100.67 80 6 16838
+ 9:00:00 10:00:00 192.0.2.2 17606 198.51.100.68 80 6 11538
+ 9:00:00 10:00:00 192.0.2.3 47113 192.0.2.131 53 17 119
+ 9:00:00 10:00:00 192.0.2.3 48458 198.51.100.133 80 6 2973
+ 9:00:00 10:00:00 192.0.2.4 61295 198.51.100.2 443 6 8350
+ 9:00:00 10:00:00 203.0.113.3 41256 198.51.100.133 80 6 778
+ 9:00:00 10:00:00 203.0.113.3 51662 198.51.100.3 80 6 883
+ 9:00:00 10:00:00 192.0.2.2 37581 198.51.100.2 80 6 15420
+ 9:00:00 10:00:00 203.0.113.3 52572 198.51.100.2 443 6 1637
+ 9:00:00 10:00:00 203.0.113.3 49914 197.51.100.133 80 6 561
+ 9:00:00 10:00:00 192.0.2.2 50824 198.51.100.2 443 6 1899
+ 9:00:00 10:00:00 192.0.2.3 34597 198.51.100.3 80 6 1284
+ 9:00:00 10:00:00 203.0.113.3 58907 198.51.100.4 80 6 2670
+ 9:00:00 10:00:00 192.0.2.4 22478 192.0.2.131 53 17 75
+ 9:00:00 10:00:00 192.0.2.4 49513 198.51.100.68 80 6 3374
+ 9:00:00 10:00:00 192.0.2.4 64832 198.51.100.67 80 6 138
+ 9:00:00 10:00:00 192.0.2.3 60833 198.51.100.69 443 6 2325
+ 9:00:00 10:00:00 203.0.113.3 39586 198.51.100.17 80 6 11200
+ 9:00:00 10:00:00 192.0.2.2 19638 198.51.100.3 80 6 2869
+ 9:00:00 10:00:00 192.0.2.3 40429 198.51.100.4 80 6 18289
+
+ Figure 20: Interval Imposition for Traffic Matrix
+
+ The next steps are to discard irrelevant key fields and to replace
+ the source and destination addresses with source and destination ASNs
+ in the map; the results of these key aggregation steps are shown in
+ Figure 21.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 40]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ start time |end time |source ASN |dest ASN |octets
+ 9:00:00 10:00:00 AS64496 AS64497 119
+ 9:00:00 10:00:00 AS64496 AS64497 83
+ 9:00:00 10:00:00 AS64496 AS64498 1637
+ 9:00:00 10:00:00 AS64496 AS64497 111
+ 9:00:00 10:00:00 AS64496 AS64498 16838
+ 9:00:00 10:00:00 AS64496 AS64498 11538
+ 9:00:00 10:00:00 AS64496 AS64497 119
+ 9:00:00 10:00:00 AS64496 AS64498 2973
+ 9:00:00 10:00:00 AS64496 AS64498 8350
+ 9:00:00 10:00:00 AS64499 AS64498 778
+ 9:00:00 10:00:00 AS64499 AS64498 883
+ 9:00:00 10:00:00 AS64496 AS64498 15420
+ 9:00:00 10:00:00 AS64499 AS64498 1637
+ 9:00:00 10:00:00 AS64499 AS64498 561
+ 9:00:00 10:00:00 AS64496 AS64498 1899
+ 9:00:00 10:00:00 AS64496 AS64498 1284
+ 9:00:00 10:00:00 AS64499 AS64498 2670
+ 9:00:00 10:00:00 AS64496 AS64497 75
+ 9:00:00 10:00:00 AS64496 AS64498 3374
+ 9:00:00 10:00:00 AS64496 AS64498 138
+ 9:00:00 10:00:00 AS64496 AS64498 2325
+ 9:00:00 10:00:00 AS64499 AS64498 11200
+ 9:00:00 10:00:00 AS64496 AS64498 2869
+ 9:00:00 10:00:00 AS64496 AS64498 18289
+
+ Figure 21: Key Aggregation for Traffic Matrix:
+ Reduction and Replacement
+
+ Finally, aggregate combination sums the counters per key and
+ interval. The resulting Aggregated Flows containing the traffic
+ matrix, shown in Figure 22, are then exported using the Template in
+ Figure 18. Note that these Aggregated Flows represent a sparse
+ matrix: AS pairs for which no traffic was received have no
+ corresponding record in the output.
+
+ start time end time source ASN dest ASN octets
+ 9:00:00 10:00:00 AS64496 AS64497 507
+ 9:00:00 10:00:00 AS64496 AS64498 86934
+ 9:00:00 10:00:00 AS64499 AS64498 17729
+
+ Figure 22: Aggregated Flows for Traffic Matrix
+
+ The output of this operation is suitable for re-aggregation: that is,
+ traffic matrices from single links or Observation Points can be
+ aggregated through the same interval imposition and aggregate
+ combination steps in order to build a traffic matrix for an entire
+ network.
+
+
+
+Trammell, et al. Standards Track [Page 41]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+8.3. Distinct Source Count per Destination Endpoint
+
+ 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. 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 Figure 23.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 42]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ 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
+
+ Figure 23: Aggregation Operations for Source Count
+
+ The Template for Aggregated Flows produced by this example is shown
+ in Figure 24.
+
+ destinationIPv4Address(12)[4]
+ destinationTransportPort(11)[2]
+ distinctCountOfSourceIPAddress(378)[8]
+
+ Figure 24: Output Template for Source Count
+
+
+
+
+
+
+Trammell, et al. Standards Track [Page 43]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ Interval distribution, in this case, merely discards the timestamp
+ information from the Original Flows in Figure 10, 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
+ Figure 25.
+
+ dest ip4 |port |dist src
+ 192.0.2.131 53 3
+ 198.51.100.2 80 1
+ 198.51.100.2 443 3
+ 198.51.100.67 80 2
+ 198.51.100.68 80 2
+ 198.51.100.133 80 2
+ 198.51.100.3 80 3
+ 198.51.100.4 80 2
+ 198.51.100.17 80 1
+ 198.51.100.69 443 1
+
+ Figure 25: Aggregated Flows for Source Count
+
+8.4. Traffic Time Series per Source with Counter Distribution
+
+ Returning to the example in Section 8.1, 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.
+
+ In this example, the same data is aggregated using the same
+ arrangement of operations and the same output Template as in
+ Section 8.1, but using a different counter distribution policy,
+ Simple Uniform Distribution, as described in Section 5.1.1. In order
+ to do this, the Exporting Process first exports the Aggregate Counter
+ Distribution Options Template, as in Figure 26.
+
+ templateId(12)[2]{scope}
+ valueDistributionMethod(384)[1]
+
+ Figure 26: Aggregate Counter Distribution Options Template
+
+ This Template is followed by an Aggregate Counter Distribution Record
+ described by this Template; assuming the output Template in Figure 11
+ has ID 257, this record would appear as in Figure 27.
+
+
+
+
+
+Trammell, et al. Standards Track [Page 44]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ template ID | value distribution method
+ 257 4 (simple uniform)
+
+ Figure 27: Aggregate Counter Distribution Record
+
+ Following metadata export, the aggregation steps follow as before.
+ However, two long Flows are distributed across multiple intervals in
+ the interval imposition step, as indicated with "*" in Figure 28.
+ 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.
+
+ start time |end time |source ip4 |port |dest ip4 |port|pt| oct
+ 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 9:15:00.000 192.0.2.2 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
+ 9:10:00.000 9:15:00.000 203.0.113.3 39586 198.51.100.17 80 6 3734*
+
+ Figure 28: Distributed Interval Imposition for Time Series per Source
+
+ Subsequent steps are as in Section 8.1; the results, to be exported
+ using the Template shown in Figure 11, are shown in Figure 29, with
+ Aggregated Flows differing from the example in Section 8.1 indicated
+ by "*".
+
+
+
+Trammell, et al. Standards Track [Page 45]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ start time |end time |source ip4 |octets
+ 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 5394*
+ 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 20614
+ 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*
+
+ Figure 29: Aggregated Flows for Time Series per Source
+ with Counter Distribution
+
+9. Security Considerations
+
+ This document specifies the operation of an Intermediate Aggregation
+ Process with the IPFIX protocol; the Security Considerations for the
+ protocol itself in Section 11 of [RFC7011] therefore apply. In the
+ common case that aggregation is performed on a Mediator, the Security
+ Considerations for Mediators in Section 9 of [RFC6183] apply as well.
+
+ As mentioned in Section 3, certain aggregation operations may tend to
+ have an anonymizing 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 [RFC6235], the Security Considerations in Section 9 of
+ [RFC6235] apply.
+
+10. IANA Considerations
+
+ This document specifies the creation of new IPFIX Information
+ Elements in the IPFIX Information Element registry [IANA-IPFIX], as
+ defined in Section 7 above. IANA has assigned Information Element
+ numbers to these Information Elements, and entered them into the
+ registry.
+
+11. Acknowledgments
+
+ Special thanks to Elisa Boschi for early work on the concepts laid
+ out in this document. Thanks to Lothar Braun, Christian Henke, and
+ Rahul Patel for their reviews and valuable feedback, with special
+
+
+
+
+Trammell, et al. Standards Track [Page 46]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ thanks to Paul Aitken for his multiple detailed reviews. This work
+ is materially supported by the European Union Seventh Framework
+ Programme under grant agreement 257315 (DEMONS).
+
+12. References
+
+12.1. Normative References
+
+ [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
+ Requirement Levels", BCP 14, RFC 2119, March 1997.
+
+ [RFC5226] Narten, T. and H. Alvestrand, "Guidelines for Writing an
+ IANA Considerations Section in RFCs", BCP 26, RFC 5226,
+ May 2008.
+
+ [RFC7011] Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
+ "Specification of the IP Flow Information Export (IPFIX)
+ Protocol for the Exchange of Flow Information", STD 77,
+ RFC 7011, September 2013.
+
+12.2. Informative References
+
+ [RFC3917] Quittek, J., Zseby, T., Claise, B., and S. Zander,
+ "Requirements for IP Flow Information Export (IPFIX)", RFC
+ 3917, October 2004.
+
+ [RFC5470] Sadasivan, G., Brownlee, N., Claise, B., and J. Quittek,
+ "Architecture for IP Flow Information Export", RFC 5470,
+ March 2009.
+
+ [RFC5472] Zseby, T., Boschi, E., Brownlee, N., and B. Claise, "IP
+ Flow Information Export (IPFIX) Applicability", RFC 5472,
+ March 2009.
+
+ [RFC5476] Claise, B., Johnson, A., and J. Quittek, "Packet Sampling
+ (PSAMP) Protocol Specifications", RFC 5476, March 2009.
+
+ [RFC5655] Trammell, B., Boschi, E., Mark, L., Zseby, T., and A.
+ Wagner, "Specification of the IP Flow Information Export
+ (IPFIX) File Format", RFC 5655, October 2009.
+
+ [RFC5982] Kobayashi, A. and B. Claise, "IP Flow Information Export
+ (IPFIX) Mediation: Problem Statement", RFC 5982, August
+ 2010.
+
+ [RFC6183] Kobayashi, A., Claise, B., Muenz, G., and K. Ishibashi,
+ "IP Flow Information Export (IPFIX) Mediation: Framework",
+ RFC 6183, April 2011.
+
+
+
+Trammell, et al. Standards Track [Page 47]
+
+RFC 7015 IPFIX Aggregation September 2013
+
+
+ [RFC6235] Boschi, E. and B. Trammell, "IP Flow Anonymization
+ Support", RFC 6235, May 2011.
+
+ [RFC6728] Muenz, G., Claise, B., and P. Aitken, "Configuration Data
+ Model for the IP Flow Information Export (IPFIX) and
+ Packet Sampling (PSAMP) Protocols", RFC 6728, October
+ 2012.
+
+ [RFC7012] Claise, B., Ed. and B. Trammell, Ed., "Information Model
+ for IP Flow Information Export (IPFIX)", RFC 7012,
+ September 2013.
+
+ [RFC7013] Trammell, B. and B. Claise, "Guidelines for Authors and
+ Reviewers of IP Flow Information Export (IPFIX)
+ Information Elements", BCP 184, RFC 7013, September 2013.
+
+ [RFC7014] D'Antonio, S., Zseby, T., Henke, C., and L. Peluso, "Flow
+ Selection Techniques", RFC 7014, September 2013.
+
+ [IANA-IPFIX]
+ IANA, "IP Flow Information Export (IPFIX) Entities",
+ <http://www.iana.org/assignments/ipfix>.
+
+ [IPFIX-MED-PROTO]
+ Claise, B., Kobayashi, A., and B. Trammell, "Operation of
+ the IP Flow Information Export (IPFIX) Protocol on IPFIX
+ Mediators", Work in Progress, July 2013.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+RFC 7015 IPFIX Aggregation September 2013
+
+
+Authors' Addresses
+
+ Brian Trammell
+ Swiss Federal Institute of Technology Zurich
+ Gloriastrasse 35
+ 8092 Zurich
+ Switzerland
+
+ Phone: +41 44 632 70 13
+ EMail: trammell@tik.ee.ethz.ch
+
+
+ Arno Wagner
+ Consecom AG
+ Bleicherweg 64a
+ 8002 Zurich
+ Switzerland
+
+ EMail: arno@wagner.name
+
+
+ Benoit Claise
+ Cisco Systems, Inc.
+ De Kleetlaan 6a b1
+ 1831 Diegem
+ Belgium
+
+ Phone: +32 2 704 5622
+ EMail: bclaise@cisco.com
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+