IPFIX Working Group B. Trammell Internet-Draft E. Boschi Intended status: Standards Track ETH Zurich Expires: December 31, 2011 A. Wagner Consecom AG B. Claise Cisco Systems, Inc. June 29, 2011 Exporting Aggregated Flow Data using the IP Flow Information Export (IPFIX) Protocol draft-trammell-ipfix-a9n-03.txt Abstract This document describes the export of aggregated Flow information using IPFIX. An Aggregated Flow is essentially an IPFIX Flow representing packets from multiple original Flows sharing some set of common properties. The document describes Aggregated Flow export within the framework of IPFIX Mediators and defines an interoperable, implementation-independent method for Aggregated Flow export. Status of this Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at http://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." This Internet-Draft will expire on December 31, 2011. Copyright Notice Copyright (c) 2011 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 Trammell, et al. Expires December 31, 2011 [Page 1] Internet-Draft IPFIX Aggregation June 2011 publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 5 3. Use Cases for IPFIX Aggregation . . . . . . . . . . . . . . . 6 4. Architecture for Flow Aggregation . . . . . . . . . . . . . . 6 4.1. Aggregation within the IPFIX Architecture . . . . . . . . 7 4.2. Intermediate Aggregation Process Architecture . . . . . . 8 5. IP Flow Aggregation Operations . . . . . . . . . . . . . . . . 10 5.1. Temporal Aggregation through Interval Distribution . . . . 10 5.1.1. Distributing Values Across Intervals . . . . . . . . . 11 5.1.2. Time Composition . . . . . . . . . . . . . . . . . . . 13 5.2. Spatial Aggregation of Flow Keys . . . . . . . . . . . . . 13 5.2.1. Counting Distinct Key Values . . . . . . . . . . . . . 15 5.2.2. Counting Original Flows . . . . . . . . . . . . . . . 15 5.3. Spatial Aggregation of Non-Key Fields . . . . . . . . . . 16 5.3.1. Counter Statistics . . . . . . . . . . . . . . . . . . 16 5.4. Aggregation Combination . . . . . . . . . . . . . . . . . 17 6. Additional Considerations and Special Cases in Flow Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . 17 6.1. Exact versus Approximate Counting during Aggregation . . . 17 6.2. Considerations for Aggregation of Sampled Flows . . . . . 17 7. Export of Aggregated IP Flows using IPFIX . . . . . . . . . . 17 7.1. Time Interval Export . . . . . . . . . . . . . . . . . . . 18 7.2. Flow Count Export . . . . . . . . . . . . . . . . . . . . 18 7.2.1. originalFlowsPresent . . . . . . . . . . . . . . . . . 18 7.2.2. originalFlowsInitiated . . . . . . . . . . . . . . . . 18 7.2.3. originalFlowsCompleted . . . . . . . . . . . . . . . . 19 7.2.4. originalFlows . . . . . . . . . . . . . . . . . . . . 19 7.3. Distinct Host Export . . . . . . . . . . . . . . . . . . . 19 7.3.1. distinctCountOfSourceIPv4Address . . . . . . . . . . . 19 7.3.2. distinctCountOfDestinationIPv4Address . . . . . . . . 20 7.3.3. distinctCountOfSourceIPv6Address . . . . . . . . . . . 20 7.3.4. distinctCountOfDestinationIPv6Address . . . . . . . . 20 7.4. Aggregate Counter Distribution Export . . . . . . . . . . 20 7.4.1. Aggregate Counter Distribution Options Template . . . 21 7.4.2. valueDistributionMethod Information Element . . . . . 21 8. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 8.1. Traffic Time-Series per Source . . . . . . . . . . . . . . 24 8.2. Core Traffic Matrix . . . . . . . . . . . . . . . . . . . 28 Trammell, et al. Expires December 31, 2011 [Page 2] Internet-Draft IPFIX Aggregation June 2011 8.3. Distinct Source Count per Destination Endpoint . . . . . . 28 8.4. Traffic Time-Series per Source with Counter Distribution . . . . . . . . . . . . . . . . . . . . . . . 29 9. Security Considerations . . . . . . . . . . . . . . . . . . . 29 10. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 29 11. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 29 12. References . . . . . . . . . . . . . . . . . . . . . . . . . . 30 12.1. Normative References . . . . . . . . . . . . . . . . . . . 30 12.2. Informative References . . . . . . . . . . . . . . . . . . 30 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 31 Trammell, et al. Expires December 31, 2011 [Page 3] Internet-Draft IPFIX Aggregation June 2011 1. Introduction The aggregation 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 [RFC5101]. 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 Mediators Problem Statement [RFC5982], and the definition of an Intermediate Aggregation Process in the Mediator framework [I-D.ietf-ipfix-mediators-framework]. Aggregation is part of a wide variety of applications, including traffic matrix calculation, generation of time series data for visualizations or anomaly detection, or measurement data reduction. Depending on the keys used for aggregation, it may additionally have an anonymising affect on the data: for example, aggregation operations which eliminate IP addresses make it impossible to later identify nodes using those addresses. Aggregation as defined and described in this document covers the applications defined in [RFC5982], including 5.1 "Adjusting Flow Granularity", 5.4 "Time Composition", and 5.5 "Spatial Composition". However, this document specifies a more flexible architecture for an Intermediate Aggregation Process in Section 4.2, which supports a superset of these applications. An Intermediate Aggregation Process may be applied to data collected from multiple Observation Points, as aggregation is natural to apply for data reduction when concentrating measurement data. This document specifically does not address the protocol issues that arise when combining IPFIX data from multiple Observation Points and exporting from a single Mediator, as these issues are general to IPFIX Mediation; they are therefore treated in detail in the Mediator Protocol [I-D.claise-ipfix-mediation-protocol] document. Since Aggregated Flows as defined in the following section are essentially Flows, the IPFIX protocol [RFC5101] 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. Trammell, et al. Expires December 31, 2011 [Page 4] Internet-Draft IPFIX Aggregation June 2011 2. Terminology Terms used in this document that are defined in the Terminology section of the IPFIX Protocol [RFC5101] document are to be interpreted as defined there. The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in [RFC2119]. In addition, this document defines the following terms Aggregated Flow: A Flow, as defined by [RFC5101], derived from a set of zero or more original Flows within a defined Aggregation Interval. The two primary differences between a Flow and an Aggregated Flow are (1) that the time interval of a Flow is generally derived from information about the timing of the packets comprising the Flow, while the time interval of an Aggregated Flow are generally externally imposed; and (2) that an Aggregated Flow may represent zero packets (i.e., an assertion that no packets were seen for a given Flow Key in a given time interval). Note that an Aggregated Flow is defined within the context of an Intermediate Aggregation Process only. once an Aggregated Flow is exported, it is essentially a Flow as in [RFC5101] and can be treated as such. Intermediate Aggregation Function: A mapping from a set of zero or more original Flows into a set of Aggregated Flows across one or more Aggregation Intervals. This function is hosted by an Intermediate Aggregation Process, defined below. Intermediate Aggregation Process: an Intermediate Process as in [I-D.ietf-ipfix-mediators-framework] that aggregates records based upon a set of Flow Keys or functions applied to fields from the record; this is itself defined in [I-D.ietf-ipfix-mediators-framework]. Aggregation Interval: A time interval imposed upon an Aggregated Flow. Aggregation Functions 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. Trammell, et al. Expires December 31, 2011 [Page 5] Internet-Draft IPFIX Aggregation June 2011 original Flow: A Flow given as input to an Aggregation Function in order to generate Aggregated Flows. contributing Flow: An original Flow that is partially or completely represented within an Aggregated Flow. Each aggregated Flow is made up of zero or more contributing Flows, and an original Flow may contribute to zero or more Aggregated Flows. 3. Use Cases for IPFIX Aggregation Aggregation, as a common data analysis method, has many applications. When used with a regular Aggregation Interval, it generates time series data from a collection of Flows with discrete intervals. Time series data is itself useful for a wide variety of analysis tasks, such as generating input for network anomaly detection systems, or driving visualizations of volume per time for traffic with specific characteristics. Traffic matrix calculation from flow data is inherently an aggregation action, by aggregating the Flow Key down to input or output interface, address prefix, or autonomous system. 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. Note that an Intermediate Aggregation Function which removes potentially sensitive information as identified in [I-D.ietf-ipfix-anon] may tend to have an anonymising effect on the Aggregated Flows, as well; however, any application of aggregation as part of a data protection scheme should ensure that all the issues raised in Section 4 of [I-D.ietf-ipfix-anon] are addressed. 4. Architecture for Flow Aggregation This section specifies how an Intermediate Aggregation Process fits into the IPFIX Architecture, and the architecture of the Intermediate Aggregation Process itself. Trammell, et al. Expires December 31, 2011 [Page 6] Internet-Draft IPFIX Aggregation June 2011 4.1. Aggregation within the IPFIX Architecture An Intermediate Aggregation Process may be deployed at three places within the IPFIX Architecture. While aggregation applications are most commonly deployed within a Mediator which collects original Flows from an original Exporter and exports Aggregated Flows, aggregation can also occur before initial export, or after final collection, as shown in Figure 1. +==========================================+ | Exporting Process | +==========================================+ | | | (Aggregated Flow Export) | V | +=============================+ | | Mediator | | +=============================+ | | | | (Aggregating Mediator) | V V +==========================================+ | Collecting Process | +==========================================+ | | (Aggregation for Storage) V +--------------------+ | IPFIX File Storage | +--------------------+ Figure 1: Potential Aggregation Locations The Mediator use case is further shown in Figures A and B in [I-D.ietf-ipfix-mediators-framework]. Aggregation can be applied for either intermediate or final analytic purposes. In certain circumstances, it may make sense to export Aggregated Flows directly from an original Exporting Process, for example, if the Exporting Process is applied to drive a time-series visualization, or when flow data export bandwidth is restricted and flow or packet sampling is not an option. Note that this case, where the Aggregation Process is essentially integrated into the Metering Process, is essentially covered by the IPFIX architecture [RFC5470]: the Flow Keys used are simply a subset of those that would normally be used. A Metering Process in this arrangement MAY choose to simulate the generation of larger Flows in order to generate original Flow counts, if the application calls for compatibility with an Trammell, et al. Expires December 31, 2011 [Page 7] Internet-Draft IPFIX Aggregation June 2011 Aggregation Process deployed in a separate location. In the specific case that an Aggregation Process is employed for data reduction for storage purposes, it can take original Flows from a Collecting Process or File Reader and pass Aggregated Flows to a File Writer for storage. 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, aggregation can be applied as well to aggregating original Flows from multiple sources into a single stream of aggregated Flows; the architectural specifics of this arrangement are not addressed in this document, which is concerned only with the aggregation operation itself; see [I-D.claise-ipfix-mediation-protocol] for details. The data paths into and out of an Intermediate Aggregation Process are showin in Figure 2. packets --+ +- IPFIX Messages -+ | | | V V V +==================+ +====================+ +=============+ | Metering Process | | Collecting Process | | File Reader | | | +====================+ +=============+ | | | original Flows | | | V V + - - - - - - - - -+======================================+ | Intermediate Aggregation Process (IAP) | +=========================================================+ | Aggregated Aggregated | | Flows Flows | V V +===================+ +=============+ | Exporting Process | | File Writer | +===================+ +=============+ | | +------------> IPFIX Messages <----------+ Figure 2: Data paths through the aggregation process 4.2. Intermediate Aggregation Process Architecture Within this document, an Intermediate Aggregation Process can be seen as hosting an Intermediate Aggregation Function composed of four types of operations on the intermediate results of aggregation, which Trammell, et al. Expires December 31, 2011 [Page 8] Internet-Draft IPFIX Aggregation June 2011 are called partially aggregated Flows in this document, as illustrated in Figure 3. original Flows | V +-----------------------+ | interval distribution | +-->| (temporal) |<--+ | +-----------------------+ | | | | | | |(*) |(*) |(*) |(*) |(*) | | | | | | V | V | +-------------------+ | +--------------------+ | key aggregation | | | value aggregation | | (spatial) | | | (spatial) | +-------------------+ | +--------------------+ ^ | | | ^ | |(*) | |(*) | +-------|-------|-------|-------+ V V V +-------------------------+ | aggregate combination | +-------------------------+ | V Aggregated Flows (*) partially aggregated Flows Figure 3: Conceptual model of aggregation operations Interval distribution is a temporal aggregation operation which 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 is a spatial aggregation operation which results in the addition, modification, or deletion of Flow Key fields in the partially aggregated Flows. New Flow Key fields may be derived from existing Flow Key fields (e.g., looking up an AS number for an IP address), or "promoted" from non-Key fields (e.g., when aggregating Flows by packet count per Flow). Key aggregation can also add new non-Key fields derived from Key Fields that are deleted during key aggregation; mainly counters of unique reduced keys. Key aggregation is discussed in detail in Section 5.2. Trammell, et al. Expires December 31, 2011 [Page 9] Internet-Draft IPFIX Aggregation June 2011 Value aggregation is a spatial aggregation operation which results in the addition, modification, or deletion of non-Key fields in the partially aggregated Flows. These non-Key fields may be "demoted" from existing Key fields, or derived from existing Key or non-Key fields. Value aggregation is discussed in detail in Section 5.3. Aggregate combination combines multiple partially aggregated Flows having undergone interval distribution, key aggregation, and value aggregation which share Flow Keys and Aggregation Intervals into a single aggregated Flow per Flow Key and Aggregation Interval. Aggregate combination is discussed in detail in Section 5.4. The first three of these operations may be carried out any number of times in any order, either on original Flows or on the results of one of the Operations (called partially aggregated Flows), with one caveat. Since Flows carry their own interval data, any spatial aggregation operation implies a temporal aggregation operation, so at least one interval distribution step, even if implicit, is required by this architecture. This is shown as the first step for the sake of simplicity in the diagram above. Once all aggregation operations are complete, aggregate combination ensures that for a given Aggregation Interval, Flow Key, and Observation Domain, only one Flow is produced by the Intermediate Aggregation Process. 5. IP Flow Aggregation Operations As stated in Section 2, an Aggregated Flow is simply an IPFIX Flow generated from original Flows by an Aggregation Function. Here, we detail the operations by which this is achieved within an Intermediate Aggregation Process. 5.1. Temporal Aggregation through Interval Distribution Interval distribution imposes a time interval on the resulting Aggregated Flows. The selection of an interval is specific to the given aggregation application. Intervals may be derived from the original Flows themselves (e.g., an interval may be selected to cover the entire interval containing the set of all Flows sharing a given Key, as in Time Composition describe 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 tradeoffs. Shorter intervals allow higher resolution aggregated data and, in streaming Trammell, et al. Expires December 31, 2011 [Page 10] Internet-Draft IPFIX Aggregation June 2011 applications, faster reaction time. Longer intervals lead to greater data reduction and simplified counter distribution. Specifically, counter distribution is greatly simplified by the choice of an interval longer than the duration of longest original Flow, itself generally determined by the original Flow's Metering Process active timeout; in this case an original Flow can contribute to at most two Aggregated Flows, and the more complex value distribution methods become inapplicable. | | | | | |<--Flow A-->| | | | | |<--Flow B-->| | | | |<-------------Flow C-------------->| | | | | | | interval 0 | interval 1 | interval 2 | Figure 4: Illustration of interval distribution In Figure 4, 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. [EDITOR'S NOTE: per Lothar: some implementation guidance here would be good. specifically, advise that you need multiple rotating intervals to do this right.] 5.1.1. Distributing Values Across Intervals In general, counters in Aggregated Flows are treated the same as in any Flow. Each counter is independently is calculated as if it were derived from the set of packets in the original Flow. For the most part, when aggregating original Flows into Aggregated Flows, this is simply done by summation. When the Aggregation Interval is guaranteed to be longer than the longest original Flow, a Flow can cross at most one Interval Trammell, et al. Expires December 31, 2011 [Page 11] Internet-Draft IPFIX Aggregation June 2011 boundary, and will therefore contribute to at most two Aggregated Flows. Most common in this case is to arbitrarily but consistently choose to account the original Flow's counters either to the first or the last aggregated Flow to which it could contribute. However, this becomes more complicated when the Aggregation Interval is shorter than the longest original Flow in the source data. In such cases, each original Flow can incompletely cover one or more time intervals, and apply to one or more Aggregated Flows; in this case, the Aggregation Process must distribute the counters in the original Flows across the multiple Aggregated Flows. There are several methods for doing this, listed here in 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 Key), and this number is added to each corresponding counter in each Aggregated Flow. Proportional Uniform Distribution: Each counter for an original Flow is divided by the number of time _units_ the original Flow covers, to derive a mean count rate. This mean count rate is then multiplied by the number of time units in the intersection of the duration of the original Flow and the time interval of each Aggregated Flow. This is like simple uniform distribution, but accounts for the fractional portions of a time interval covered by an original Flow in the first and last time interval. Simulated Process: Each counter of the original Flow is distributed among the intervals of the Aggregated Flows according to some function the Aggregation Process uses based upon properties of Flows presumed to be like the original Flow. For example, Flow Records representing bulk transfer might follow a more or less proportional uniform distribution, while interactive processes are Trammell, et al. Expires December 31, 2011 [Page 12] Internet-Draft IPFIX Aggregation June 2011 far more bursty. Direct: The Aggregation Process has access to the original packet timings from the packets making up the original Flow, and uses these to distribute or recalculate the counters. A method for exporting the distribution of counters across multiple Aggregated Flows is detailed in Section 7.4. 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 (e.g., inaccuracy introduced by the use of floating-point numbers is tolerable). This property allows data to be aggregated and re-aggregated without any loss of original count information. To avoid confusion in interpretation of the aggregated data, all the counters for a set of given original Flows SHOULD be distributed via the same method. 5.1.2. Time Composition Time Composition as in section 5.4 of [RFC5982] (or interval combination) is a special case of aggregation, where interval distribution imposes longer intervals on Flows with matching keys and "chained" start and end times, without any key reduction, in order to join long-lived Flows which may have been split (e.g., due to an active timeout shorter than the Flow.) Here, no Key aggregation is applied, and the Aggregation Interval is chosen on a per-Flow basis to cover the interval spanned by the set of aggregated Flows. This may be applied alone in order to normalize split Flows, or in combination with other aggregation functions in order to obtain more accurate original Flow counts. 5.2. Spatial Aggregation of Flow Keys Key aggregation generates a new Flow Key for the Aggregated Flows from the original Flow Keys, non-Key fields in the original Flows, or from correlation of the original Flow information with some external source. There are two basic operations here. First, Aggregated Flow Keys may be derived directly from original Flow Keys through reduction, or the dropping of fields or precision in the original Flow Keys. Second, an Aggregated Flow Key may be derived through replacement, e.g. by removing one or more fields from the original Flow and replacing them with a fields derived from the removed fields. Replacement may refer to external information (e.g., IP to AS number mappings). Replacement need not replace only key fields. For example, consider an application which aggregates 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 field. Trammell, et al. Expires December 31, 2011 [Page 13] Internet-Draft IPFIX Aggregation June 2011 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 Section 5.2.2 and Section 5.2.1, 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. Key aggregation may aggregate original Flows with different sets of Flow Key fields; only the Flow Keys of the resulting Aggregated Flows of any given Key aggregation operation need contain the same set of fields. Original Flow Key +---------+---------+----------+----------+-------+-----+ | src ip4 | dst ip4 | src port | dst port | proto | tos | +---------+---------+----------+----------+-------+-----+ | | | | | | retain mask /24 X X X X V V +---------+-------------+ | src ip4 | dst ip4 /24 | +---------+-------------+ Aggregated Flow Key (by source address and destination class-C) Figure 5: Illustration of key aggregation by reduction Figure 5 illustrates an example reduction operation, aggregation by source address and destination class C network. Here, the port, protocol, and type-of-service information is removed from the Flow Key, the source address is retained, and the destination address is masked by dropping the low 8 bits. Original Flow Key +---------+---------+----------+----------+-------+-----+ | src ip4 | dst ip4 | src port | dst port | proto | tos | +---------+---------+----------+----------+-------+-----+ | | | | | | +-------------------+ X X X X | ASN lookup table | +-------------------+ V V +---------+---------+ | src asn | dst asn | +---------+---------+ Aggregated Flow Key (by source and dest ASN) Figure 6: Illustration of key aggregation by reduction and Trammell, et al. Expires December 31, 2011 [Page 14] Internet-Draft IPFIX Aggregation June 2011 replacement Figure 6 illustrates an example reduction and replacement operation, aggregation by source and destination ASN without ASN information available in the original Flow. Here, the port, protocol, and type- of-service information is removed from the Flow Key, while the source and destination addresses are run though an IP address to ASN lookup table, and the Aggregated Flow Key is made up of the resulting source and destination ASNs. 5.2.1. Counting Distinct Key Values One common case in aggregation is counting distinct key values that were reduced away during key aggregation. The most common use case for this is counting distinct hosts per Flow Key; for example, in host characterization or anomaly detection, distinct sources per destination or distinct destinations per source are common metrics. These new non-Ley 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. 5.2.2. Counting Original Flows When aggregating multiple original Flows into an Aggregated Flow, it is often useful to know how many original Flows are present in the Aggregated Flow. This document introduces four new information elements in Section 7.2 to export these counters. There are two possible ways to count original Flows, which we call here conservative and non-conservative. Conservative flow counting has the property that each original Flow contributes exactly one to the total flow count within a set of aggregated Flows. In other words, conservative flow counters are distributed just as any other counter during interval distribution, except each original Flow is assumed to have a flow count of one. When a count for an original Flow must be distributed across a set of Aggregated Flows, and a distribution method is used which does not account for that original Flow completely within a single Aggregated Flow, conservative flow counting requires a fractional representation. By contrast, non-conservative flow counting is used to count how many contributing Flows are represented in an Aggregated Flow. Flow counters are not distributed in this case. An original Flow which is present within N Aggregated Flows would add N to the sum of non- conservative flow counts, one to each Aggregated Flow. In other Trammell, et al. Expires December 31, 2011 [Page 15] Internet-Draft IPFIX Aggregation June 2011 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 4. 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, all the original Exporters producing original Flows to be aggregated SHOULD be aggregated using caches configured identically or similarly. Original Exporters using the IPFIX Configuration Model SHOULD be configured to export Flows with equal or similar activeTimeout and inactiveTimeout configuration values, and the same cacheMode, as defined in section 4.3 of [I-D.ietf-ipfix-configuration-model]. 5.3. Spatial Aggregation of Non-Key Fields Aggregation operations may also lead to the addition of value fields demoted from key fields, or derived from other value fields in the original Flows. Specific cases of this are treated in the subsections below. 5.3.1. Counter Statistics Some applications of aggregation may benefit from computing different statistics than those native to each non-key field (i.e., union for flags, sum for counters). For example, minimum and maximum packet counts per Flow, mean bytes per packet per aggregated Flow, and so on. Certain Information Elements for these applications are already provided in the IANA IPFIX Information Elements registry (http://www.iana.org/assignments/ipfix/ipfix.html (e.g. minimumIpTotalLength). A complete specification of additional aggregate counter statistics Trammell, et al. Expires December 31, 2011 [Page 16] Internet-Draft IPFIX Aggregation June 2011 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. 5.4. Aggregation Combination Interval distribution and key aggregation together may generate multiple partially aggregated Flows covering the same time interval with the same Flow Key. The process of combining these partially aggregated Flows into a single Aggregated Flow is called aggregation combination. In general, non-Key values from multiple contributing Flows are combined using the same operation by which values are combined from packets to form Flows for each Information Element. Counters are summed, averages are averaged, flags are unioned, and so on. 6. Additional Considerations and Special Cases in Flow Aggregation 6.1. Exact versus Approximate Counting during Aggregation In certain circumstances, particularly involving aggregation by devices with limited resources, and in situations where exact aggregated counts are less important than relative magnitudes (e.g. driving graphical displays), counter distribution during key aggregation may be performed by approximate counting means (e.g. Bloom filters). The choice to use approximate counting is implementation- and application-dependent. 6.2. Considerations for Aggregation of Sampled Flows The accuracy of Aggregated Flows may also be affected by sampling of the original Flows, or sampling of packets making up the original Flows. The effect of sampling on flow aggregation is still an open research question. However, to maximize the comparability of Aggregated Flows, aggregation of sampled Flows SHOULD only use original Flows sampled using the same sampling rate and sampling algorithm, or Flows created from packets sampled using the same sampling rate and sampling algorithm. For more on packet sampling within IPFIX, see [RFC5476]. For more on Flow sampling within the IPFIX Mediator Framework, see [I-D.ietf-ipfix-flow-selection-tech]. 7. Export of Aggregated IP Flows using IPFIX In general, Aggregated Flows are exported in IPFIX as any normal Flow. However, certain aspects of Aggregated Flow export benefit from additional guidelines, or new Information Elements to represent Trammell, et al. Expires December 31, 2011 [Page 17] Internet-Draft IPFIX Aggregation June 2011 aggregation metadata or information generated during aggregation. These are detailed in the following subsections. 7.1. Time Interval Export Since an Aggregated Flow is simply a Flow, the existing timestamp Information Elements in the IPFIX Information Model (e.g., flowStartMilliseconds, flowEndNanoseconds) are sufficient to specify the time interval for aggregation. Therefore, this document specifies no new aggregation-specific Information Elements for exporting time interval information. Each Aggregated Flow SHOULD contain both an interval start and interval end timestamp. If an exporter of Aggregated Flows omits the interval end timestamp from each Aggregated Flow, the time interval for Aggregated Flows within an Observation Domain and Transport Session MUST be regular and constant. However, note that this approach might lead to interoperability problems when exporting Aggregated Flows to non-aggregation-aware Collecting Processes and downstream analysis tasks; therefore, an Exporting Process capable of exporting only interval start timestamps MUST provide a configuration option to export interval end timestamps as well. 7.2. Flow Count Export The following four Information Elements are defined to count original Flows as discussed in Section 5.2.2. 7.2.1. originalFlowsPresent Description: The non-conservative count of original Flows contributing to this Aggregated Flow. Non-conservative counts need not sum to the original count on re-aggregation. Abstract Data Type: unsigned64 ElementId: TBD1 Status: Current 7.2.2. originalFlowsInitiated Description: The conservative count of original Flows whose first packet is represented within this Aggregated Flow. Conservative counts must some to the original count on re-aggregation. Trammell, et al. Expires December 31, 2011 [Page 18] Internet-Draft IPFIX Aggregation June 2011 Abstract Data Type: unsigned64 ElementId: TBD2 Status: Current 7.2.3. originalFlowsCompleted Description: The conservative count of original Flows whose last packet is represented within this Aggregated Flow. Conservative counts must some to the original count on re-aggregation. Abstract Data Type: unsigned64 ElementId: TBD3 Status: Current 7.2.4. originalFlows Description: The conservative count of original Flows contributing to this Aggregated Flow; may be distributed via any of the methods described in Section 5.1.1. Abstract Data Type: float64 ElementId: 3 Status: Current 7.3. Distinct Host Export The following four Information Elements represent the distinct counts of source and destination addresses for IPv4 and IPv6, used to exporting distinct host counts reduced away during key aggregation. 7.3.1. distinctCountOfSourceIPv4Address Description: The count of distinct source IPv4 address values for original Flows contributing to this Aggregated Flow. Abstract Data Type: unsigned32 ElementId: TBD6 Trammell, et al. Expires December 31, 2011 [Page 19] Internet-Draft IPFIX Aggregation June 2011 Status: Current 7.3.2. distinctCountOfDestinationIPv4Address Description: The count of distinct destination IPv4 address values for original Flows contributing to this Aggregated Flow. Abstract Data Type: unsigned32 ElementId: TBD7 Status: Current 7.3.3. distinctCountOfSourceIPv6Address Description: The count of distinct source IPv6 address values for original Flows contributing to this Aggregated Flow. Abstract Data Type: unsigned64 ElementId: TBD8 Status: Current 7.3.4. distinctCountOfDestinationIPv6Address Description: The count of distinct destination IPv6 address values for original Flows contributing to this Aggregated Flow. Abstract Data Type: unsigned64 ElementId: TBD9 Status: Current 7.4. Aggregate Counter Distribution Export When exporting counters distributed among Aggregated Flows, as described in Section 5.1.1, the Exporting Process MAY export an Aggregate Counter Distribution Record for each Template describing Aggregated Flow records; this Options Template is described below. It uses the valueDistributionMethod Information Element, also defined below. Since in many cases distribution is simple, accounting the counters from contributing Flows to the first Interval to which they contribute, this is default situation, for which no Aggregate Counter Distribution Record is necessary; Aggregate Counter Distribution Records are only applicable in more exotic situations, such as using an Aggregation Interval smaller than the durations of original Flows. Trammell, et al. Expires December 31, 2011 [Page 20] Internet-Draft IPFIX Aggregation June 2011 7.4.1. Aggregate Counter Distribution Options Template This Options Template defines the Aggregate Counter Distribution Record, which allows the binding of a value distribution method to a Template ID. This is used to signal to the Collecting Process how the counters were distributed. The fields are as below: +-------------------------+-----------------------------------------+ | IE | Description | +-------------------------+-----------------------------------------+ | templateId [scope] | The Template ID of the Template | | | defining the Aggregated Flows to which | | | this distribution option applies. This | | | Information Element MUST be defined as | | | a Scope Field. | | valueDistributionMethod | The method used to distribute the | | | counters for the Aggregated Flows | | | defined by the associated Template. | +-------------------------+-----------------------------------------+ 7.4.2. valueDistributionMethod Information Element Description: A description of the method used to distribute the counters from contributing Flows into the Aggregated Flow records described by an associated Template. The method is deemed to apply to all the non-key Information Elements in the referenced Template for which value distribution is a valid operation; if the originalFlowsInitiated and/or originalFlowsCompleted Information Elements appear in the Template, they are not subject to this distribution method, as they each infer their own distribution method. The distribution methods are taken from Section 5.1.1 and encoded as follows: +-------+-----------------------------------------------------------+ | Value | Description | +-------+-----------------------------------------------------------+ | 1 | Start Interval: The counters for an original Flow are | | | added to the counters of the appropriate Aggregated Flow | | | containing the start time of the original Flow. This | | | should be assumed the default if value distribution | | | information is not available at a Collecting Process for | | | an Aggregated Flow. | | 2 | End Interval: The counters for an original Flow are added | | | to the counters of the appropriate Aggregated Flow | | | containing the end time of the original Flow. | Trammell, et al. Expires December 31, 2011 [Page 21] Internet-Draft IPFIX Aggregation June 2011 | 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 Aggregation Process uses | | | based upon properties of Flows presumed to be like the | | | original Flow. This is essentially an assertion that the | | | Aggregation Process has no direct packet timing | | | information but is nevertheless not using one of the | | | other simpler distribution methods. The Aggregation | | | Process specifically makes no assertion as to the | | | correctness of the simulation. | | 7 | Direct: The Aggregation Process has access to the | | | original packet timings from the packets making up the | | | original Flow, and uses these to distribute or | | | recalculate the counters. | +-------+-----------------------------------------------------------+ Abstract Data Type: unsigned8 ElementId: TBD5 Status: Current 8. Examples In these examples, the same data, described by the same template, will be aggregated multiple different ways; this illustrates the various different functions which could be implemented by Trammell, et al. Expires December 31, 2011 [Page 22] Internet-Draft IPFIX Aggregation June 2011 Intermediate Aggregation Processes. Templates are shown in iespec format as introduced in [I-D.trammell-ipfix-ie-doctors]. The source data format is a simplified flow: timestamps, traditional 5-tuple, and octet count. The template is shown in Figure 7. flowStartMilliseconds flowEndMilliseconds sourceIPv4Address destinationIPv4Address sourceTransportPort destinationTransportPort protocolIdentifier octetDeltaCount Figure 7: Input template for examples The data records given as input to the examples in this section are shown below, in the format "flowStartMilliseconds-flowEndMilliseconds sourceIPv4Address:sourceTransportPort -> destinationIPv4Address: destinationTransportPort (protocolIdentifier) octetDeltaCount"; timestamps are given in H:MM:SS.sss format. 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 9:06:56.813-9:06:59.821 203.0.113.3:52572 -> 198.51.100.2:443 (6) 1637 9:06:30.565-9:07:00.261 203.0.113.3:49914 -> 197.51.100.133:80 (6) 561 9:06:55.160-9:07:05.208 192.0.2.2:50824 -> 198.51.100.2:443 (6) 1899 9:06:49.322-9:07:05.322 192.0.2.3:34597 -> 198.51.100.3:80 (6) 1284 9:07:05.849-9:07:09.625 203.0.113.3:58907 -> 198.51.100.4:80 (6) 2670 9:10:45.161-9:10:45.161 192.0.2.4:22478 -> 192.0.2.131:53 (17) 75 9:10:45.209-9:11:01.465 192.0.2.4:49513 -> 198.51.100.68:80 (6) 3374 9:10:57.094-9:11:00.614 192.0.2.4:64832 -> 198.51.100.67:80 (6) 138 9:10:59.770-9:11:02.842 192.0.2.3:60833 -> 198.51.100.69:443 (6) 2325 9:13:53.933-9:14:06.605 192.0.2.2:19638 -> 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 8: Input data for examples Trammell, et al. Expires December 31, 2011 [Page 23] Internet-Draft IPFIX Aggregation June 2011 8.1. Traffic Time-Series per Source Aggregating flows by source IP address in time series (i.e., with a regular interval) can be used in subsequent heavy-hitter analysis and as a source parameter for statistical anomaly detection techniques. Here, the Intermediate Aggregation Process imposes an interval, aggregates the key to remove all key fields other than the source IP address, then combines the result into a stream of Aggregated Flows. For simplicity, the imposed interval of 30 minutes is defined to be larger than the maximum active timeout of the original Flows; counter distribution will be added to this example below in Section 8.4. 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 9. flowStartMilliseconds flowEndMilliseconds sourceIPv4Address octetDeltaCount Figure 9: Output template for time series per source Assume the goal is to get 5-minute time series of octet counts per source IP address. The aggregation operations would then be arranged as in Figure 10. Trammell, et al. Expires December 31, 2011 [Page 24] Internet-Draft IPFIX Aggregation June 2011 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 partially aggregated Flows Figure 10: Aggregation operations for time series per source After the interval distribution step, only the time intervals have changed; the partially aggregated Flows are shown in Figure 11. Trammell, et al. Expires December 31, 2011 [Page 25] Internet-Draft IPFIX Aggregation June 2011 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 9:05:00.000-9:10:00.000 203.0.113.3:52572 -> 198.51.100.2:443 (6) 1637 9:05:00.000-9:10:00.000 203.0.113.3:49914 -> 197.51.100.133:80 (6) 561 9:05:00.000-9:10:00.000 192.0.2.2:50824 -> 198.51.100.2:443 (6) 1899 9:05:00.000-9:10:00.000 192.0.2.3:34597 -> 198.51.100.3:80 (6) 1284 9:05:00.000-9:10:00.000 203.0.113.3:58907 -> 198.51.100.4:80 (6) 2670 9:10:00.000-9:15:00.000 192.0.2.4:22478 -> 192.0.2.131:53 (17) 75 9:10:00.000-9:15:00.000 192.0.2.4:49513 -> 198.51.100.68:80 (6) 3374 9:10:00.000-9:15:00.000 192.0.2.4:64832 -> 198.51.100.67:80 (6) 138 9:10:00.000-9:15:00.000 192.0.2.3:60833 -> 198.51.100.69:443 (6) 2325 9:10:00.000-9:15:00.000 192.0.2.2:19638 -> 198.51.100.3:80 (6) 2869 9:10:00.000-9:15:00.000 192.0.2.3:40429 -> 198.51.100.4:80 (6) 18289 Figure 11: Partially aggregated Flows: intervals imposed After the key aggregation step, all the parts of the flow key except the source IP address have been discarded, as shown in Figure 12. This leaves duplicate partially aggregated Flows to be combined the final operation. Trammell, et al. Expires December 31, 2011 [Page 26] Internet-Draft IPFIX Aggregation June 2011 9:00:00.000-9:05:00.000 192.0.2.2 119 9:00:00.000-9:05:00.000 192.0.2.2 83 9:00:00.000-9:05:00.000 192.0.2.2 1637 9:00:00.000-9:05:00.000 192.0.2.3 111 9:00:00.000-9:05:00.000 192.0.2.3 16838 9:00:00.000-9:05:00.000 192.0.2.2 11538 9:00:00.000-9:05:00.000 192.0.2.3 119 9:00:00.000-9:05:00.000 192.0.2.3 2973 9:00:00.000-9:05:00.000 192.0.2.4 8350 9:00:00.000-9:05:00.000 203.0.113.3 778 9:00:00.000-9:05:00.000 203.0.113.3 883 9:05:00.000-9:10:00.000 203.0.113.3 1637 9:05:00.000-9:10:00.000 203.0.113.3 561 9:05:00.000-9:10:00.000 192.0.2.2 1899 9:05:00.000-9:10:00.000 192.0.2.3 1284 9:05:00.000-9:10:00.000 203.0.113.3 2670 9:10:00.000-9:15:00.000 192.0.2.4 75 9:10:00.000-9:15:00.000 192.0.2.4 3374 9:10:00.000-9:15:00.000 192.0.2.4 138 9:10:00.000-9:15:00.000 192.0.2.3 2325 9:10:00.000-9:15:00.000 192.0.2.2 2869 9:10:00.000-9:15:00.000 192.0.2.3 18289 Figure 12: Partially aggregated Flows: key aggregation Aggregate combination sums the counters per key and interval; the summations of the first two keys and intervals are shown in detail in Figure 13. 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 13377 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 13: Summation during aggregate combination Applying this to each set of partially aggregated Flows to produce the final Aggregated Flows shown in Figure 14m to be exported by the template in Figure 9. Trammell, et al. Expires December 31, 2011 [Page 27] Internet-Draft IPFIX Aggregation June 2011 9:00:00.000-9:05:00.000 192.0.2.2 13377 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 1661 9:05:00.000-9:10:00.000 192.0.2.2 1899 9:05:00.000-9:10:00.000 192.0.2.3 1284 9:05:00.000-9:10:00.000 203.0.113.3 4868 9:10:00.000-9:15:00.000 192.0.2.2 2869 9:10:00.000-9:15:00.000 192.0.2.3 20594 9:10:00.000-9:15:00.000 192.0.2.4 3587 Figure 14: Aggregated Flows 8.2. Core Traffic Matrix Aggregating flows by source and destination autonomous system number in time series is used to generate core traffic matrices. The core traffic matrix provides a view of the state of the routes within a network, and can be used for long-term planning of changes to network design based on traffic demand. Here, imposed time intervals are generally much longer than active flow timeouts. The traffic matrix is reported in terms of octets, packets, and flows, as each of these values may have a subtly different effect on capacity planning. This example demonstrates key aggregation using derived keys and Original Flow counting. While some original Flows may be generated by Exporting Processes on forwarding devices, and therefore contain the bgpSourceAsNumber and bgpDestinationAsNumber Information Elements, original Flows from Exporting Processes on dedicated measurement devices will contain only a destinationIPv[46]Address. For these flows, the Mediator must look up a next hop AS from a IP to AS table, replacing source and destination addresses with AS numbers. [TODO: complete example. show AS map, output templates, and processing in IAP.] 8.3. Distinct Source Count per Destination Endpoint Aggregating flows by destination address and port, and counting distinct sources aggregated away, can be used as part of passive service inventory and host characterization approaches. This example shows aggregation as an analysis technique, performed on source data stored in an IPFIX File. As the Transport Session in this File is bounded, removal of all timestamp information allows summarization of the entire time interval contained within the interval. Removal of timing information during interval imposition is equivalent to an infinitely long imposed time interval. This demonstrates both how infinite intervals work, and how unique counters work. Trammell, et al. Expires December 31, 2011 [Page 28] Internet-Draft IPFIX Aggregation June 2011 [TODO: complete example. show output templates and processing in IAP.] 8.4. Traffic Time-Series per Source with Counter Distribution Returning to the example in Section 8.1, consider a case where aggregation by the maximum active timeout, here 30 minutes, is incompatible with the processing interval, here defined to be 5 minutes. For this case, flows longer than 5 minutes must have their counters distributed. This example demonstrates counter distribution metadata export. [TODO: complete example. show output metadata and processing in IAP.] 9. Security Considerations [TODO] 10. IANA Considerations This document specifies the creation of twelve new IPFIX Information Elements in the IPFIX Information Element registry located at http://www.iana.org/assignments/ipfix, as defined in Section 7 above. IANA has assigned Information Element numbers to these Information Elements, and entered them into the registry. [NOTE for IANA: The text TBDn should be replaced with the respective assigned Information Element numbers where they appear in this document. Note that the originalFlows Information Element has been assigned the number 3, as it is compatible with the corresponding existing (reserved) NetFlow v9 Information Element. Other Information Element numbers should be assigned outside the NetFlow V9 compatibility range, as these Information Elements are not supported by NetFlow V9.] 11. Acknowledgments This work is materially supported by the European Union Seventh Framework Programme under grant agreement 257315 (DEMONS). 12. References Trammell, et al. Expires December 31, 2011 [Page 29] Internet-Draft IPFIX Aggregation June 2011 12.1. Normative References [RFC5101] Claise, B., "Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of IP Traffic Flow Information", RFC 5101, January 2008. [RFC5102] Quittek, J., Bryant, S., Claise, B., Aitken, P., and J. Meyer, "Information Model for IP Flow Information Export", RFC 5102, January 2008. 12.2. Informative References [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, March 1997. [RFC3917] Quittek, J., Zseby, T., Claise, B., and S. Zander, "Requirements for IP Flow Information Export (IPFIX)", RFC 3917, October 2004. [RFC5103] Trammell, B. and E. Boschi, "Bidirectional Flow Export Using IP Flow Information Export (IPFIX)", RFC 5103, January 2008. [RFC5153] Boschi, E., Mark, L., Quittek, J., Stiemerling, M., and P. Aitken, "IP Flow Information Export (IPFIX) Implementation Guidelines", RFC 5153, April 2008. [RFC5470] Sadasivan, G., Brownlee, N., Claise, B., and J. Quittek, "Architecture for IP Flow Information Export", RFC 5470, March 2009. [RFC5472] Zseby, T., Boschi, E., Brownlee, N., and B. Claise, "IP Flow Information Export (IPFIX) Applicability", RFC 5472, March 2009. [RFC5476] Claise, B., Johnson, A., and J. Quittek, "Packet Sampling (PSAMP) Protocol Specifications", RFC 5476, March 2009. [RFC5610] Boschi, E., Trammell, B., Mark, L., and T. Zseby, "Exporting Type Information for IP Flow Information Export (IPFIX) Information Elements", RFC 5610, July 2009. [RFC5655] Trammell, B., Boschi, E., Mark, L., Zseby, T., and A. Wagner, "Specification of the IP Flow Information Export (IPFIX) File Format", RFC 5655, October 2009. [RFC5835] Morton, A. and S. Van den Berghe, "Framework for Metric Composition", RFC 5835, April 2010. Trammell, et al. Expires December 31, 2011 [Page 30] Internet-Draft IPFIX Aggregation June 2011 [RFC5982] Kobayashi, A. and B. Claise, "IP Flow Information Export (IPFIX) Mediation: Problem Statement", RFC 5982, August 2010. [I-D.ietf-ipfix-anon] Boschi, E. and B. Trammell, "IP Flow Anonymization Support", draft-ietf-ipfix-anon-06 (work in progress), January 2011. [I-D.ietf-ipfix-mediators-framework] Kobayashi, A., Claise, B., Muenz, G., and K. Ishibashi, "IPFIX Mediation: Framework", draft-ietf-ipfix-mediators-framework-09 (work in progress), October 2010. [I-D.claise-ipfix-mediation-protocol] Claise, B., "Specification of the Protocol for IPFIX Mediations", draft-claise-ipfix-mediation-protocol-03 (work in progress), February 2011. [I-D.trammell-ipfix-ie-doctors] Trammell, B. and B. Claise, "Guidelines for Authors and Reviewers of IPFIX Information Elements", draft-trammell-ipfix-ie-doctors-02 (work in progress), June 2011. [I-D.ietf-ipfix-configuration-model] Muenz, G., Claise, B., and P. Aitken, "Configuration Data Model for IPFIX and PSAMP", draft-ietf-ipfix-configuration-model-09 (work in progress), March 2011. [I-D.ietf-ipfix-flow-selection-tech] D'Antonio, S., Zseby, T., Henke, C., and L. Peluso, "Flow Selection Techniques", draft-ietf-ipfix-flow-selection-tech-06 (work in progress), May 2011. Trammell, et al. Expires December 31, 2011 [Page 31] Internet-Draft IPFIX Aggregation June 2011 Authors' Addresses Brian Trammell Swiss Federal Institute of Technology Zurich Gloriastrasse 35 8092 Zurich Switzerland Phone: +41 44 632 70 13 Email: trammell@tik.ee.ethz.ch Elisa Boschi Swiss Federal Institute of Technology Zurich Gloriastrasse 35 8092 Zurich Switzerland Email: boschie@tik.ee.ethz.ch Arno Wagner Consecom AG Bleicherweg 64a 8002 Zurich Switzerland Email: arno@wagner.name Benoit Claise Cisco Systems, Inc. De Kleetlaan 6a b1 1831 Diagem Belgium Phone: +32 2 704 5622 Email: bclaise@cisco.com Trammell, et al. Expires December 31, 2011 [Page 32]