Request for Comments: 7015
Category: Standards Track
ISSN: 2070-1721
ETH Zurich
A. Wagner
Consecom AG
B. Claise
Cisco Systems, Inc.
September 2013
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Flow Aggregation for the IP Flow Information Export (IPFIX) Protocol
Abstract
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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
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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.
Copyright Notice
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Copyright © 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
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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 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
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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.
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
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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.
Additionally, non-standard enterprise-specific elements may be defined for private use.
1.2. IPFIX Documents Overview
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"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
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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.
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.
3. Use Cases for IPFIX Aggregation
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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 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
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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
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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.
+===========================================+ | 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
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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
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.
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
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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.
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
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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.
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 FlowsFigure 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. 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
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When accepting Original Flows from multiple Metering Processes, each of which provides a different view of the Original Flow as seen from the point of view of the IAP, an optional correlation and normalization operation combines each of these single Flow Records into a set of unified Partially Aggregated Flows before applying interval distribution. These unified Flows appear as if they had been measured at a single Metering Process 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
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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
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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 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
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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
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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.
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.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
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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.
5.1.3. External Interval Distribution
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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
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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.
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
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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
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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 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
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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.
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
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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
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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
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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.
5.3.2. Derivation of New Values from Flow Keys and Non-key fields
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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
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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.
6. Additional Considerations and Special Cases in Flow Aggregation
6.1. Exact versus Approximate Counting during Aggregation
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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
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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
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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].
6.4. Considerations for Aggregation of Heterogeneous Flows
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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
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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
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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
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The following four Information Elements are defined to count Original Flows as discussed in Section 5.2.1.
7.2.1. originalFlowsPresent
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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
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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
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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
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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
7.3. Distinct Host Export
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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
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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
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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
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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
7.3.4. distinctCountOfDestinationIPv4Address
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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
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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
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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
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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.
7.4.1. Aggregate Counter Distribution Options Template
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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
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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: +-------+-----------------------------------------------------------+ | 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. | | | --------------------------------------------------------- | | | --------------------------------------------------------- | | 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
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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
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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 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
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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.
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
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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.
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
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Figure 13: Interval Imposition for Time Series per Source
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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.
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
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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.
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
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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
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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.
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
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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
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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.
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
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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.
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
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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.
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
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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
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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.
8.3. Distinct Source Count per Destination Endpoint
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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.
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
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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
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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
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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
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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.
template ID | value distribution method
257 4 (simple uniform)
Figure 27: Aggregate Counter Distribution Record
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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 "*".
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
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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
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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
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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 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
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[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
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[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. [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.
Authors' Addresses
-
Brian Trammell
Swiss Federal Institute of Technology Zurich
Gloriastrasse 35
8092 Zurich
SwitzerlandPhone: +41 44 632 70 13 EMail: trammell@tik.ee.ethz.ch
Arno Wagner
Consecom AG
Bleicherweg 64a
8002 Zurich
SwitzerlandEMail:
arno@wagner.name
Benoit Claise
Cisco Systems, Inc.
De Kleetlaan 6a b1
1831 Diegem
BelgiumPhone: +32 2 704 5622 EMail: bclaise@cisco.com