Framework for Performance Metric Development
draft-ietf-pmol-metrics-framework-03

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Abstract

This document describes a framework and a process for developing performance metrics for IP-based applications that operate over reliable or datagram transport protocols, and that can be used to characterize traffic on live networks and services. The framework refers to a Performance Metrics Entity, or PM Entity, which may in future be a working group or directorate or a combination of these two.

Requirements Language

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 RFC 2119 (Bradner, S., “Key words for use in RFCs to Indicate Requirement Levels,” March 1997.) [RFC2119].

Table of Contents

1.  TO DO
2.  Introduction
    2.1.  Background and Motivation
    2.2.  Organization of this document
3.  Terminology
    3.1.  Quality of Service
    3.2.  Application Performance Metric
    3.3.  Quality of Experience
4.  Purpose and Scope
5.  QoS versus Application Performance Metrics versus QoE
6.  Metrics Development
    6.1.  Identifying and Categorizing the Audience
    6.2.  Definitions of a Metric
    6.3.  Computed Metrics
        6.3.1.  Composed Metrics
        6.3.2.  Index (from compagg)
    6.4.  Metric Specification
        6.4.1.  Outline
        6.4.2.  Normative parts of metric definition
        6.4.3.  Informative parts of metric definition
        6.4.4.  Metric Definition Template
        6.4.5.  Example: Burst Packet Loss Frequency
    6.5.  Dependencies
        6.5.1.  Timing accuracy
        6.5.2.  Dependencies of metric definitions on related events or metrics
        6.5.3.  Relationship between application performance and lower layer metrics
        6.5.4.  Middlebox presence
    6.6.  Organization of Results
    6.7.  Parameters, the variables of a metric
7.  Performance Metric Development Process
    7.1.  New Proposals for Metrics
    7.2.  Reviewing Metrics
    7.3.  Proposal Approval
    7.4.  PM Entity Interaction with other WGs
    7.5.  Standards Track Performance Metrics
8.  IANA Considerations
9.  Security Considerations
10.  Acknowledgements
11.  References
    11.1.  Normative References
    11.2.  Informative References
§  Authors' Addresses

1.  TO DO

• Multiple EDITOR'S NOTES throughout the document
• Should we refer to ITU G1010 for application performance metrics?
• Do we want a definition for Performance Metric Entity?

2.  Introduction

Many networking technologies, applications, or services, are distributed in nature, and their performance may be impacted by IP impairments, server capacity, congestion and other factors. It is important to measure the performance of applications and services to ensure that quality objectives are being met and to support problem diagnosis. Standardized metrics help to ensure that performance measurement is implemented consistently and to facilitate interpretation and comparison.

There are at least three phases in the development of performance standards. They are:

1. Definition of a Performance Metric and its units of measure
2. Specification of a Method of Measurement
3. Specification of the Reporting Format

During the development of metrics, it is often useful to define performance objectives and expected value ranges. However, this is not defined as part of the metric specification.

This document refers to a Performance Metrics Entity, or PM Entity, which may in future be a working group or directorate or a combination of these two.

2.1.  Background and Motivation

Although the IETF has two active Working Groups dedicated to the development of performance metrics, they each have strict limitations in their charters:

- The Benchmarking Methodology Working Group has addressed a range of networking technologies and protocols in their long history (such as IEEE 802.3, ATM, Frame Relay, and Routing Protocols), but the charter strictly limits their performance characterizations to the laboratory environment.

- The IP Performance Metrics (IPPM) Working Group has developed a set of standard metrics that can be applied to the quality, performance, and reliability of Internet data delivery services. The IPPM metrics development is applicable to live IP networks, but it is specifically prohibited from developing metrics that characterize traffic at upper layers, such as a VoIP stream.

A BOF held at IETF-69 introduced the IETF community to the possibility of a generalized activity to define standardized performance metrics. The existence of a growing list of Internet-Drafts on performance metrics (with community interest in development, but in un-chartered areas) illustrates the need for additional performance work. The majority of people present at the BOF supported the proposition that IETF should be working in these areas, and no one objected to any of the proposals.

The IETF does have current and completed activities related to the reporting of application performance metrics: for example the Real-time Application Quality-of-Service Monitoring (RAQMON) Framework RFC 4710 (Siddiqui, A., Romascanu, D., and E. Golovinsky, “Real-time Application Quality-of-Service Monitoring (RAQMON) Framework,” October 2006.) [RFC4710], which extends the remote network monitoring (RMON) family of specifications to allow real-time quality-of-service (QoS) monitoring of various applications that run on devices such as IP phones, pagers, Instant Messaging clients, mobile phones, and various other handheld computing devices.

The IETF is also actively involved in the development of reliable transport protocols which would affect the relationship between IP performance and application performance.

EDITOR'S NOTE: I'm not sure what the previous sentence refers to?

Thus there is a gap in the currently chartered coverage of IETF WGs: development of performance metrics for non-IP-layer protocols that can be used to characterize performance on live networks.

EDITOR'S NOTE: must extend on the "non-IP-layer". Could be above L4 such as voice specific metrics, but also L2 such as (G)MPLS

2.2.  Organization of this document

This document is divided in two major sections beyond the Purpose and Scope section. The first is a definition and description of a performance metric and its key aspects. The second defines a process to develop these metrics that is applicable to the IETF environment.

3.  Terminology

3.1.  Quality of Service

The Quality of Service (QoS) is defined similarly to the ITU "QoS experienced/perceived by customer/user (QoSE)" E800 (, “ITU-T Recommendation E.800. SERIES E: OVERALL NETWORK OPERATION, TELEPHONE SERVICE, SERVICE OPERATION AND HUMAN FACTORS,” .) [E800], i.e.: Totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service.

EDITOR'S NOTE: currently searching for a QoS definition in the IETF

3.2.  Application Performance Metric

EDITOR'S NOTE: to be filled in

3.3.  Quality of Experience

The Quality of Experience (QoE) is defined similarly to the ITU "QoS experienced/perceived by customer/user (QoSE)" E800 (, “ITU-T Recommendation E.800. SERIES E: OVERALL NETWORK OPERATION, TELEPHONE SERVICE, SERVICE OPERATION AND HUMAN FACTORS,” .) [E800], i.e.: a statement expressing the level of quality that customers/users believe they have experienced.

NOTE 1 – The level of QoS experienced and/or perceived by the customer/user may be expressed by an opinion rating.

NOTE 2 – QoSE has two main man components: quantitative and qualitative. The quantitative component can be influenced by the complete end-to-end system effects (network infrastructure).

NOTE 3 – The qualitative component can be influenced by user expectations, ambient conditions, psychological factors, application context, etc.

NOTE 4 – QoSE may also be considered as QoSD received and interpreted by a user with the pertinent qualitative factors influencing his/her perception of the service.

4.  Purpose and Scope

The purpose of this document is to define a framework and a process for developing performance metrics for IP-based applications that operate over reliable or datagram transport protocols, and that can be used to characterize traffic on live networks and services. As such, this document will not define any performance metrics.

The scope of this document includes the support of metric definition for any protocol developed by the IETF. However this document is not intended to supercede existing working methods within Working Groups that have existing chartered work in this area.

This process is not intended to govern performance metric development in existing IETF WG that are focused on metrics development, such as IPPM and BMWG. However, the framework and guidelines may be useful in these activities, and MAY be applied where appropriate. A typical example is the development of performance metrics to be exported with the IPFIX protocol RFC 5101 (Claise, B., “Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of IP Traffic Flow Information,” January 2008.) [RFC5101], with specific IPFIX information elements RFC 5102 (Quittek, J., Bryant, S., Claise, B., Aitken, P., and J. Meyer, “Information Model for IP Flow Information Export,” January 2008.) [RFC5102], which would benefit from the framework in this document.

The framework in this document applies to performance metrics derived from both active and passive measurements.

5.  QoS versus Application Performance Metrics versus QoE

QoS deals with the network and protocol, while QoE deals with the notion of a user in a context of a task or a service. As a consequence, QoE leads to the notion of Application Performance Metrics. For example, QoS performance metrics contain the one-way delay and the delay variation RFC 5481 (Morton, A. and B. Claise, “Packet Delay Variation Applicability Statement,” March 2009.) [RFC5481] and the Mean Opinion Score (MOS) P.800 (, “ITU-T Recommendation P.800. : Methods for subjective determination of transmission quality,” .) [P.800] can be modelled and calculated as an Application Performance Metric for multimedia applications. However, the MOS for a particular user in the specific context such as a conference call, an IPTV or an emergency call are different QoE's. Finally, QoS and Application Performance Metrics are quantitative, while QoE is qualitative.

EDITOR'S NOTE: not too happy about the MOS example, as it's debatable whether MOS is QoE or Applicatoin Performance Metric? If there is a better example...

6.  Metrics Development

This section provides key definitions and qualifications of performance metrics.

6.1.  Identifying and Categorizing the Audience

Many of the aspects of metric definition and reporting, even the selection or determination of the essential metrics, depend on who will use the results, and for what purpose: in order to properly maintain service quality? or to identify and quantify problems? The question, "How will the results be used?" usually yields important factors to consider when developing performance metrics.

All documents defining performance metrics SHOULD identify the primary audience and its associated requirements. The audience can influence both the definition of metrics and the methods of measurement.

The key areas of variation between different metric users include:

• Suitability of passive measurements of live traffic, or active measurements using dedicated traffic
• Measurement in laboratory environment, or on a network of deployed devices
• Suitability of passive measurements of live traffic, or active measurements using dedicated traffic
• Measurement in laboratory environment, or on a network of deployed devices
• Accuracy of the results
• Access to measurement points and configuration information
• Measurement topology (point-to-point, point-to-multipoint)
• Scale of the measurement system
• Measurements conducted on-demand, or continuously
• Required reporting formats

6.2.  Definitions of a Metric

A metric is a measure of an observable behavior of an networking technology, an application, or a service. Most of the time, the metric can be directly measured. However, sometimes, the metric definition is computed: it assumes some implicit or explicit underlying statistical process. In such case, the metric is an estimate of a parameter of this process, assuming that that the statistical process closely models the behavior of the system.

A metric should serve some defined purpose. This may include the measurement of capacity, quantifying how bad some problem is, measurement of service level, problem diagnosis or location and other such uses. A metric may also be an input to some other process, for example the computation of a composite metric or a model or simulation of a system. Tests of the "usefulness" of a metric include:

(i) the degree to which its absence would cause significant loss of information on the behavior or performance of the application or system being measured

(ii) the correlation between the performance metric, the QoS G1000 (, “ITU-T Recommendation G.1000. Communications Quality of Service: A framework and definitions,” .) [G1000] and QoE delivered to the user (person or other application)

(iii) the degree to which the metric is able to support the identification and location of problems affecting service quality.

(iv) the requirement to develop policies (Service Level Agreement, and potentially Service Level Contract) based on the metric.

For example, consider a distributed application operating over a network connection that is subject to packet loss. A Packet Loss Rate (PLR) metric is defined as the mean packet loss rate over some time period. If the application performs poorly over network connections with high packet loss rate and always performs well when the packet loss rate is zero then the PLR metric is useful to some degree. Some applications are sensitive to short periods of high loss (bursty loss) and are relatively insensitive to isolated packet loss events; for this type of application there would be very weak correlation between PLR and application performance. A "better" metric would consider both the packet loss rate and the distribution of loss events. If application performance is degraded when the PLR exceeds some rate then a useful metric may be a measure of the duration and frequency of periods during which the PLR exceeds that rate.

6.3.  Computed Metrics

6.3.1.  Composed Metrics

Some metrics may not be measured directly, but may be composed from base metrics that have been measured. A composed metric is derived from other metrics by applying a deterministic process or function (e.g., a composition function). The process may use metrics that are identical to the metric being composed, or metrics that are dissimilar, or some combination of both types.Usually the base metrics have a limited scope in time or space, and they can be combined to estimate the performance of some larger entities.

Some examples of composed metrics and composed metric definitions are:

Spatial Composition is defined as the composition of metrics of the same type with differing spatial domains [I‑D.ietf‑ippm‑framework‑compagg] (Morton, A., “Framework for Metric Composition,” December 2009.) [I‑D.ietf‑ippm‑spatial‑composition] (Morton, A. and E. Stephan, “Spatial Composition of Metrics,” April 2010.). For spatially composed metrics to be meaningful, the spatial domains should be non- overlapping and contiguous, and the composition operation should be mathematically appropriate for the type of metric.

Temporal Composition is defined as the composition of sets of metrics of the same type with differing time spans [I‑D.ietf‑ippm‑framework‑compagg] (Morton, A., “Framework for Metric Composition,” December 2009.). For temporally composed metrics to be meaningful, the time spans should be non-overlapping and contiguous, and the composition operation should be mathematically appropriate for the type of metric.

Temporal Aggregation is a summarization of metrics into a smaller number of metrics that relate to the total time span covered by the original metrics. An example would be to compute the minimum, maximum and average values of a series of time sampled values of a metric.

EDITOR'S NOTE: review draft-ietf-ippm-framework-compagg-08.txt and determine is something should be added in this section

EDITOR'S NOTE: should we mention the IPFIX Mediators drafts that explains about aggregation? http://www.ietf.org/id/draft-ietf-ipfix-mediators-problem-statement-06.txt http://www.ietf.org/id/draft-ietf-ipfix-mediators-framework-04.txt

6.3.2.  Index (from compagg)

An Index is a metric for which the output value range has been selected for convenience or clarity, and the behavior of which is selected to support ease of understanding (e.g. G.107 R Factor). The deterministic function for an index is often developed after the index range and behavior have been determined.

EDITOR'S NOTE: the section title was "Index (from compagg)". I guess it refers to http://www.ietf.org/id/draft-ietf-ippm-framework-compagg-08.txt section 3.5 "composed metrics" now. Do we want to keep a separate sub section, or do we combine this with the previous section?

6.4.  Metric Specification

6.4.1.  Outline

A metric definition MUST have a normative part that defines what the metric is and how it is measured or computed and SHOULD have an informative part that describes the metric and its application.

6.4.2.  Normative parts of metric definition

The normative part of a metric definition MUST define at least the following:

(i) Metric Name

Metric names MUST be unique within the set of metrics being defined and MAY be descriptive.

(ii) Metric Description

The description MUST explain what the metric is, what is being measured and how this relates to the performance of the system being measured.

(iii) Collection Method

EDITOR'S NOTE: remove "measurement" in "measurement method" as this this method can be measured, estimated or computed". Looking for a generic term -> collection method? Do we want to change from measurement to collection all over?

This MUST define what is being measured, estimated or computed and the specific algorithm to be used. Terms such as "average" should be qualified (e.g. running average or average over some interval). Exception cases SHOULD also be defined with the appropriate handling method. For example, there are a number of commonly used metrics related to packet loss; these often don't define the criteria by which a packet is determined to be lost (vs very delayed) or how duplicate packets are handled. For example, if the average packet loss rate during a time interval is reported, and a packet's arrival is delayed from one interval to the next then was it "lost" during the interval during which it should have arrived or should it be counted as received?

Some parameters linked to the method MAY also be reported, in order to fully interpret the performance metric. For example, the time interval, the load, the minimum packet loss, etc...

(iv) Units of measurement

The units of measurement MUST be clearly stated.

(v) Measurement Point(s)

If the measurement is specific to a measurement point this SHOULD be defined. The measurement domain MAY also be defined. Specifically, if measurement points are spread across domains, the measurement domain (intra-, inter-) is another factor to consider.

EDITOR'S NOTE: discuss that the collection is not necessarily scoped to a single observation point.

(vi) Measurement timing

The acceptable range of timing intervals or sampling intervals for a measurement and the timing accuracy required for such intervals MUST be specified. Short sampling intervals or frequent samples provide a rich source of information that can help to assess application performance but may lead to excessive measurement data. Long measurement or sampling intervals reduce the amount of reported and collected data such that it may be insufficient to understand application performance or service quality insofar as the measured quantity may vary significantly with time.

EDITOR'S NOTE: explain that, in case of multiple measurement points, synchronized clocks might be required. See RFC5481

6.4.3.  Informative parts of metric definition

The informative part of a metric specification is intended to support the implementation and use of the metric. This part SHOULD provide the following data:

(i) Implementation

The implementation description MAY be in the form of text, algorithm or example software. The objective of this part of the metric definition is to assist implementers to achieve a consistent result.

(ii) Verification

The metric definition SHOULD provide guidance on verification testing. This may be in the form of test vectors, a formal verification test method or informal advice.

(iii) Use and Applications

The Use and Applications description is intended to assist the "user" to understand how, when and where the metric can be applied, and what significance the value range for the metric may have. This MAY include a definition of the "typical" and "abnormal" range of the metric, if this was not apparent from the nature of the metric.

For example:

(a) it is fairly intuitive that a lower packet loss rate would equate to better performance. However the user may not know the significance of some given packet loss rate,

(b) the speech level of a telephone signal is commonly expressed in dBm0. If the user is presented with:

Speech level = -7 dBm0

this is not intuitively understandable, unless the user is a telephony expert. If the metric definition explains that the typical range is -18 to -28 dBm0, a value higher than -18 means the signal may be too high (loud) and less than -28 means that the signal may be too low (quiet), it is much easier to interpret the metric.

(iv) Reporting Model

The Reporting Model definition is intended to make any relationship between the metric and the reporting model clear. There are often implied relationships between the method of reporting metrics and the metric itself, however these are often not made apparent to the implementor. For example, if the metric is a short term running average packet delay variation (e.g. PPDV as defined in RFC3550) and this value is reported at intervals of 6-10 seconds, the resulting measurement may have limited accuracy when packet delay variation is non-stationary.

6.4.4.  Metric Definition Template

Normative

• Metric Name
• Metric Description
• Method
• Units of measurement
• Measurement Timing

Informative

• Implementation Guidelines
• Verification
• Use and Applications
• Reporting Model

6.4.5.  Example: Burst Packet Loss Frequency

The burst packet loss frequency can be observed at different layers. The following example is specific to RTP RFC 3550 (Schulzrinne, H., Casner, S., Frederick, R., and V. Jacobson, “RTP: A Transport Protocol for Real-Time Applications,” July 2003.) [RFC3550].

Metric Name: BurstPacketLossFrequency

Metric Description: A burst of packet loss is defined as a longest period starting and ending with lost packets during which no more than Gmin consecutive packets are received. The BurstPacketLossFrequency is defined as the number of bursts of packet loss occurring during a specified time interval (e.g. per minute, per hour, per day). If Gmin is set to 0 then a burst of packet loss would comprise only consecutive lost packets, whereas a Gmin of 16 would define bursts as periods of both lost and received packets (sparse bursts) having a loss rate of greater than 5.9%.

Method: Bursts may be detected using the Markov Model algorithm defined in RFC3611. The BurstPacketLossFrequency is calculated by counting the number of burst events within the defined measurement interval. A burst that spans the boundary between two time intervals shall be counted within the later of the two intervals.

Units of Measurement: Bursts per time interval (e.g. per second, per hour, per day)

Measurement Timing: This metric can be used over a wide range of time intervals. Using time intervals of longer than one hour may prevent the detection of variations in the value of this metric due to time- of-day changes in network load. Timing intervals should not vary in duration by more than +/- 2%.

Implementation Guidelines: See RFC3611.

Verification Testing: See Appendix for C code to generate test vectors.

Use and Applications: This metric is useful to detect IP network transients that affect the performance of applications such as Voice over IP or IP Video. The value of Gmin may be selected to ensure that bursts correspond to a packet loss rate that would degrade the performance of the application of interest (e.g. 16 for VoIP).

Reporting Model: This metric needs to be associated with a defined time interval, which could be defined by fixed intervals or by a sliding window.

6.5.  Dependencies

6.5.1.  Timing accuracy

The accuracy of the timing of a measurement may affect the accuracy of the metric. This may not materially affect a sampled value metric however would affect an interval based metric. Some metrics, for example the number of events per time interval, would be directly affected; for example a 10% variation in time interval would lead directly to a 10% variation in the measured value. Other metrics, such as the average packet loss rate during some time interval, would be affected to a lesser extent.

If it is necessary to correlate sampled values or intervals then it is essential that the accuracy of sampling time and interval start/ stop times is sufficient for the application (for example +/- 2%).

6.5.2.  Dependencies of metric definitions on related events or metrics

Metric definitions may explicitly or implicitly rely on factors that may not be obvious. For example, the recognition of a packet as being "lost" relies on having some method to know the packet was actually lost (e.g. RTP sequence number), and some time threshold after which a non-received packet is declared as lost. It is important that any such dependencies are recognized and incorporated into the metric definition.

6.5.3.  Relationship between application performance and lower layer metrics

Lower layer metrics may be used to compute or infer the performance of higher layer applications, potentially using an application performance model. The accuracy of this will depend on many factors including:

(i) The completeness of the set of metrics - i.e. are there metrics for all the input values to the application performance model?

(ii) Correlation between input variables (being measured) and application performance

(iii) Variability in the measured metrics and how this variability affects application performance

6.5.4.  Middlebox presence

Presence of a middlebox RFC 3303 (Srisuresh, P., Kuthan, J., Rosenberg, J., Molitor, A., and A. Rayhan, “Middlebox communication architecture and framework,” August 2002.) [RFC3303], e.g., proxy, NAT, redirect server, session border controller (SBC), and application layer gateway (ALG) may add variability to or restrict the scope of measurements of a metric. For example, an SBC that does not process RTP loopback packets may block or locally terminate this traffic rather then pass it through to its target.

6.6.  Organization of Results

The IPPM Framework [RFC2330] organizes the results of metrics into three related notions:

• singleton, an elementary instance, or "atomic" value.
• sample, a set of singletons with some common properties and some varying properties.
• statistic, a value derived from a sample through deterministic calculation, such as the mean.

Many metrics can use this organization for the results, with or without the term names used by IPPM working group. Section 11 of RFC 2330 (Paxson, V., Almes, G., Mahdavi, J., and M. Mathis, “Framework for IP Performance Metrics,” May 1998.) [RFC2330] should consulted for further details.

6.7.  Parameters, the variables of a metric

Metrics are completely defined when all options and input variables have been identified and considered. These variables are sometimes left unspecified in a metric definition, and their general name indicates that the user must set them and report them with the results. Such variables are called "parameters" in the IPPM metric template. The scope of the metric, the time at which it was conducted, the settings for timers and the thresholds for counters are all examples of parameters.

All documents defining performance metrics SHOULD identify ALL key parameters for each metric.

7.  Performance Metric Development Process

7.1.  New Proposals for Metrics

The following entry criteria will be considered for each proposal.

Proposals SHOULD be prepared as Internet Drafts, describing the metrics and conforming to the qualifications above as much as possible.

Proposals SHOULD be vetted by the corresponding protocol development Working Group prior to discussion by the PM Entity. This aspect of the process includes an assessment of the need for the metrics proposed and assessment of the support for their development in IETF.

Proposals SHOULD include an assessment of interaction and/or overlap with work in other Standards Development Organizations.

Proposals SHOULD specify the intended audience and users of the metrics. The development process encourages participation by members of the intended audience.

Proposals SHOULD survey the existing standards work in the area and identify additional expertise that might be consulted, or possible overlap with other standards development orgs.

Proposals SHOULD identify any security and IANA requirements. Security issues could potentially involve revealing of user identifying data or the potential misuse of active test tools. IANA considerations may involve the need for a metrics registry.

7.2.  Reviewing Metrics

Each metric SHOULD be assessed according to the following list of qualifications:

• Unambiguously defined?
• Units of Measure Specified?
• Measurement Interval Specified?
• Measurement Errors Identified?
• Repeatable?
• Implementable?
• Assumptions concerning underlying process?
• Use cases?
• Correlation with application performance/ user experience?

7.3.  Proposal Approval

New work item proposals SHALL be approved using the existing IETF process.

The process depends on the form that the PM Entity ultimately takes, Directorate or working group.

In all cases, the proposal will need to achieve consensus, in the corresponding protocol development working group (or alternatively, an "Area" working group with broad charter), that there is interest and a need for the work.

IF the PM Entity is a Directorate,

THEN Approval SHOULD include the following steps

• consultation with the PM Directorate, using this framework document
• consultation with Area Director(s)
• and possibly IESG approval of a new or revised charter for the working group

IF the PM Entity is a Working Group, and the protocol development working group decides to take up the work under its charter,

THEN the approval is the same as the PM Directorate steps above, with the possible additional assignment of a PM Advisor for the work item.

IF the PM Entity is a Working Group, and the protocol development working group decides it does not have sufficient expertise to take-up the work, or the proposal falls outside the current charter,

THEN

Approval SHOULD include the following steps

• identification of protocol expertise to support metric development
• consensus in the PM working group that there is interest and a need for the work, and that a document conforming to this framework can be successfully developed
• consultation with Area Director(s)
• IESG approval of a revised charter for the PM working group

7.4.  PM Entity Interaction with other WGs

The PM Entity SHALL work in partnership with the related protocol development WG when considering an Internet Draft that specifies performance metrics for a protocol. A sufficient number of individuals with expertise must be willing to consult on the draft. If the related WG has concluded, comments on the proposal should still be sought from key RFC authors and former chairs, or from the WG mailing list if it was not closed.

Existing mailing lists SHOULD be used however a dedicated mailing list MAY be initiated if necessary to facilitate work on a draft.

In some cases, it will be appropriate to have the IETF session discussion during the related protocol WG session, to maximize visibility of the effort to that WG and expand the review.

7.5.  Standards Track Performance Metrics

The PM Entity will manage the progression of PM RFCs along the Standards Track. See [I‑D.bradner‑metricstest] (Bradner, S. and V. Paxson, “Advancement of metrics specifications on the IETF Standards Track,” August 2007.). This may include the preparation of test plans to examine different implementations of the metrics to ensure that the metric definitions are clear and unambiguous (depending on the final form of the draft above).

8.  IANA Considerations

This document makes no request of IANA.

Note to RFC Editor: this section may be removed on publication as an RFC.

9.  Security Considerations

In general, the existence of framework for performance metric development does not constitute a security issue for the Internet. Metric definitions may introduce security issues and this framework recommends that those defining metrics should identify any such risk factors.

The security considerations that apply to any active measurement of live networks are relevant here as well. See [RFC4656] (Shalunov, S., Teitelbaum, B., Karp, A., Boote, J., and M. Zekauskas, “A One-way Active Measurement Protocol (OWAMP),” September 2006.).

EDITOR'S NOTE: do we want to mention something about specific to passive? For example, anonymization.

10.  Acknowledgements

The authors would like to thank Al Morton, Dan Romascanu, Daryl Malas and Loki Jorgenson for their comments and contributions.

11.  References

11.1. Normative References

11.2. Informative References

Authors' Addresses