Internet Engineering Task Force (IETF) A. Morton
Request for Comments: 6049 AT&T Labs
Category: Standards Track E. Stephan
ISSN: 2070-1721 France Telecom Orange
January 2011
Internet Engineering Task Force (IETF) A. Morton
Request for Comments: 6049 AT&T Labs
Category: Standards Track E. Stephan
ISSN: 2070-1721 France Telecom Orange
January 2011
Spatial Composition of Metrics
度量的空间构成
Abstract
摘要
This memo utilizes IP performance metrics that are applicable to both complete paths and sub-paths, and it defines relationships to compose a complete path metric from the sub-path metrics with some accuracy with regard to the actual metrics. This is called "spatial composition" in RFC 2330. The memo refers to the framework for metric composition, and provides background and motivation for combining metrics to derive others. The descriptions of several composed metrics and statistics follow.
本备忘录利用了适用于完整路径和子路径的IP性能指标，并定义了关系，以从子路径指标构成完整的路径指标，并在实际指标方面具有一定的准确性。这在RFC2330中称为“空间构成”。备忘录提到了度量组合的框架，并提供了组合度量以派生其他度量的背景和动机。以下是对几个组合指标和统计数据的描述。
Status of This Memo
关于下段备忘
This is an Internet Standards Track document.
这是一份互联网标准跟踪文件。
This document is a product of the Internet Engineering Task Force (IETF). It represents the consensus of the IETF community. It has received public review and has been approved for publication by the Internet Engineering Steering Group (IESG). Further information on Internet Standards is available in Section 2 of RFC 5741.
本文件是互联网工程任务组（IETF）的产品。它代表了IETF社区的共识。它已经接受了公众审查，并已被互联网工程指导小组（IESG）批准出版。有关互联网标准的更多信息，请参见RFC 5741第2节。
Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at http://www.rfc-editor.org/info/rfc6049.
有关本文件当前状态、任何勘误表以及如何提供反馈的信息，请访问http://www.rfc-editor.org/info/rfc6049.
Copyright Notice
版权公告
Copyright (c) 2011 IETF Trust and the persons identified as the document authors. All rights reserved.
版权所有（c）2011 IETF信托基金和确定为文件作者的人员。版权所有。
This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (http://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License.
本文件受BCP 78和IETF信托有关IETF文件的法律规定的约束(http://trustee.ietf.org/license-info)自本文件出版之日起生效。请仔细阅读这些文件，因为它们描述了您对本文件的权利和限制。从本文件中提取的代码组件必须包括信托法律条款第4.e节中所述的简化BSD许可证文本，并提供简化BSD许可证中所述的无担保。
This document may contain material from IETF Documents or IETF Contributions published or made publicly available before November 10, 2008. The person(s) controlling the copyright in some of this material may not have granted the IETF Trust the right to allow modifications of such material outside the IETF Standards Process. Without obtaining an adequate license from the person(s) controlling the copyright in such materials, this document may not be modified outside the IETF Standards Process, and derivative works of it may not be created outside the IETF Standards Process, except to format it for publication as an RFC or to translate it into languages other than English.
本文件可能包含2008年11月10日之前发布或公开的IETF文件或IETF贡献中的材料。控制某些材料版权的人员可能未授予IETF信托允许在IETF标准流程之外修改此类材料的权利。在未从控制此类材料版权的人员处获得充分许可的情况下，不得在IETF标准流程之外修改本文件，也不得在IETF标准流程之外创建其衍生作品，除了将其格式化以RFC形式发布或将其翻译成英语以外的其他语言。
Table of Contents
目录
1. Introduction ....................................................4
1.1. Motivation .................................................6
1.2. Requirements Language ......................................6
2. Scope and Application ...........................................6
2.1. Scope of Work ..............................................6
2.2. Application ................................................7
2.3. Incomplete Information .....................................7
3. Common Specifications for Composed Metrics ......................8
3.1. Name: Type-P ...............................................8
3.1.1. Metric Parameters ...................................8
3.1.2. Definition and Metric Units .........................9
3.1.3. Discussion and Other Details ........................9
3.1.4. Statistic ...........................................9
3.1.5. Composition Function ................................9
3.1.6. Statement of Conjecture and Assumptions ............10
3.1.7. Justification of the Composition Function ..........10
3.1.8. Sources of Deviation from the Ground Truth .........10
3.1.9. Specific Cases where the Conjecture Might Fail .....11
3.1.10. Application of Measurement Methodology ............12
4. One-Way Delay Composed Metrics and Statistics ..................12
4.1. Name: Type-P-Finite-One-way-Delay-<Sample>-Stream .........12
4.1.1. Metric Parameters ..................................12
4.1.2. Definition and Metric Units ........................12
4.1.3. Discussion and Other Details .......................13
4.1.4. Statistic ..........................................13
4.2. Name: Type-P-Finite-Composite-One-way-Delay-Mean ..........13
4.2.1. Metric Parameters ..................................13
4.2.2. Definition and Metric Units of the Mean Statistic ..14
4.2.3. Discussion and Other Details .......................14
4.2.4. Statistic ..........................................14
4.2.5. Composition Function: Sum of Means .................14
4.2.6. Statement of Conjecture and Assumptions ............15
4.2.7. Justification of the Composition Function ..........15
4.2.8. Sources of Deviation from the Ground Truth .........15
4.2.9. Specific Cases where the Conjecture Might Fail .....15
4.2.10. Application of Measurement Methodology ............16
4.3. Name: Type-P-Finite-Composite-One-way-Delay-Minimum .......16
4.3.1. Metric Parameters ..................................16
4.3.2. Definition and Metric Units of the Minimum
Statistic ..........................................16
4.3.3. Discussion and Other Details .......................16
4.3.4. Statistic ..........................................16
4.3.5. Composition Function: Sum of Minima ................16
4.3.6. Statement of Conjecture and Assumptions ............17
4.3.7. Justification of the Composition Function ..........17
4.3.8. Sources of Deviation from the Ground Truth .........17
1. Introduction ....................................................4
1.1. Motivation .................................................6
1.2. Requirements Language ......................................6
2. Scope and Application ...........................................6
2.1. Scope of Work ..............................................6
2.2. Application ................................................7
2.3. Incomplete Information .....................................7
3. Common Specifications for Composed Metrics ......................8
3.1. Name: Type-P ...............................................8
3.1.1. Metric Parameters ...................................8
3.1.2. Definition and Metric Units .........................9
3.1.3. Discussion and Other Details ........................9
3.1.4. Statistic ...........................................9
3.1.5. Composition Function ................................9
3.1.6. Statement of Conjecture and Assumptions ............10
3.1.7. Justification of the Composition Function ..........10
3.1.8. Sources of Deviation from the Ground Truth .........10
3.1.9. Specific Cases where the Conjecture Might Fail .....11
3.1.10. Application of Measurement Methodology ............12
4. One-Way Delay Composed Metrics and Statistics ..................12
4.1. Name: Type-P-Finite-One-way-Delay-<Sample>-Stream .........12
4.1.1. Metric Parameters ..................................12
4.1.2. Definition and Metric Units ........................12
4.1.3. Discussion and Other Details .......................13
4.1.4. Statistic ..........................................13
4.2. Name: Type-P-Finite-Composite-One-way-Delay-Mean ..........13
4.2.1. Metric Parameters ..................................13
4.2.2. Definition and Metric Units of the Mean Statistic ..14
4.2.3. Discussion and Other Details .......................14
4.2.4. Statistic ..........................................14
4.2.5. Composition Function: Sum of Means .................14
4.2.6. Statement of Conjecture and Assumptions ............15
4.2.7. Justification of the Composition Function ..........15
4.2.8. Sources of Deviation from the Ground Truth .........15
4.2.9. Specific Cases where the Conjecture Might Fail .....15
4.2.10. Application of Measurement Methodology ............16
4.3. Name: Type-P-Finite-Composite-One-way-Delay-Minimum .......16
4.3.1. Metric Parameters ..................................16
4.3.2. Definition and Metric Units of the Minimum
Statistic ..........................................16
4.3.3. Discussion and Other Details .......................16
4.3.4. Statistic ..........................................16
4.3.5. Composition Function: Sum of Minima ................16
4.3.6. Statement of Conjecture and Assumptions ............17
4.3.7. Justification of the Composition Function ..........17
4.3.8. Sources of Deviation from the Ground Truth .........17
4.3.9. Specific Cases where the Conjecture Might Fail .....17
4.3.10. Application of Measurement Methodology ............17
5. Loss Metrics and Statistics ....................................18
5.1. Type-P-Composite-One-way-Packet-Loss-Empirical-Probability 18
5.1.1. Metric Parameters ..................................18
5.1.2. Definition and Metric Units ........................18
5.1.3. Discussion and Other Details .......................18
5.1.4. Statistic:
Type-P-One-way-Packet-Loss-Empirical-Probability ...18
5.1.5. Composition Function: Composition of
Empirical Probabilities ............................18
5.1.6. Statement of Conjecture and Assumptions ............19
5.1.7. Justification of the Composition Function ..........19
5.1.8. Sources of Deviation from the Ground Truth .........19
5.1.9. Specific Cases where the Conjecture Might Fail .....19
5.1.10. Application of Measurement Methodology ............19
6. Delay Variation Metrics and Statistics .........................20
6.1. Name: Type-P-One-way-pdv-refmin-<Sample>-Stream ...........20
6.1.1. Metric Parameters ..................................20
6.1.2. Definition and Metric Units ........................20
6.1.3. Discussion and Other Details .......................21
6.1.4. Statistics: Mean, Variance, Skewness, Quantile .....21
6.1.5. Composition Functions ..............................22
6.1.6. Statement of Conjecture and Assumptions ............23
6.1.7. Justification of the Composition Function ..........23
6.1.8. Sources of Deviation from the Ground Truth .........23
6.1.9. Specific Cases where the Conjecture Might Fail .....24
6.1.10. Application of Measurement Methodology ............24
7. Security Considerations ........................................24
7.1. Denial-of-Service Attacks .................................24
7.2. User Data Confidentiality .................................24
7.3. Interference with the Metrics .............................24
8. IANA Considerations ............................................25
9. Contributors and Acknowledgements ..............................27
10. References ....................................................28
10.1. Normative References .....................................28
10.2. Informative References ...................................28
4.3.9. Specific Cases where the Conjecture Might Fail .....17
4.3.10. Application of Measurement Methodology ............17
5. Loss Metrics and Statistics ....................................18
5.1. Type-P-Composite-One-way-Packet-Loss-Empirical-Probability 18
5.1.1. Metric Parameters ..................................18
5.1.2. Definition and Metric Units ........................18
5.1.3. Discussion and Other Details .......................18
5.1.4. Statistic:
Type-P-One-way-Packet-Loss-Empirical-Probability ...18
5.1.5. Composition Function: Composition of
Empirical Probabilities ............................18
5.1.6. Statement of Conjecture and Assumptions ............19
5.1.7. Justification of the Composition Function ..........19
5.1.8. Sources of Deviation from the Ground Truth .........19
5.1.9. Specific Cases where the Conjecture Might Fail .....19
5.1.10. Application of Measurement Methodology ............19
6. Delay Variation Metrics and Statistics .........................20
6.1. Name: Type-P-One-way-pdv-refmin-<Sample>-Stream ...........20
6.1.1. Metric Parameters ..................................20
6.1.2. Definition and Metric Units ........................20
6.1.3. Discussion and Other Details .......................21
6.1.4. Statistics: Mean, Variance, Skewness, Quantile .....21
6.1.5. Composition Functions ..............................22
6.1.6. Statement of Conjecture and Assumptions ............23
6.1.7. Justification of the Composition Function ..........23
6.1.8. Sources of Deviation from the Ground Truth .........23
6.1.9. Specific Cases where the Conjecture Might Fail .....24
6.1.10. Application of Measurement Methodology ............24
7. Security Considerations ........................................24
7.1. Denial-of-Service Attacks .................................24
7.2. User Data Confidentiality .................................24
7.3. Interference with the Metrics .............................24
8. IANA Considerations ............................................25
9. Contributors and Acknowledgements ..............................27
10. References ....................................................28
10.1. Normative References .....................................28
10.2. Informative References ...................................28
The IP Performance Metrics (IPPM) framework [RFC2330] describes two forms of metric composition: spatial and temporal. The composition framework [RFC5835] expands and further qualifies these original forms into three categories. This memo describes spatial composition, one of the categories of metrics under the umbrella of the composition framework.
IP性能度量（IPPM）框架[RFC2330]描述了度量组合的两种形式：空间和时间。组合框架[RFC5835]将这些原始形式扩展并进一步限定为三类。本备忘录描述了空间组合，这是组合框架下的一类度量标准。
Spatial composition encompasses the definition of performance metrics that are applicable to a complete path, based on metrics collected on various sub-paths.
空间组合包括基于在不同子路径上收集的度量，适用于完整路径的性能度量的定义。
The main purpose of this memo is to define the deterministic functions that yield the complete path metrics using metrics of the sub-paths. The effectiveness of such metrics is dependent on their usefulness in analysis and applicability with practical measurement methods.
本备忘录的主要目的是使用子路径的度量定义产生完整路径度量的确定性函数。这些指标的有效性取决于它们在分析中的有用性以及对实际测量方法的适用性。
The relationships may involve conjecture, and [RFC2330] lists four points that the metric definitions should include:
这些关系可能涉及猜测，[RFC2330]列出了度量定义应包括的四点：
o the specific conjecture applied to the metric and assumptions of the statistical model of the process being measured (if any; see [RFC2330], Section 12),
o 应用于被测量过程统计模型的度量和假设的具体推测（如有；见[RFC2330]，第12节），
o a justification of the practical utility of the composition in terms of making accurate measurements of the metric on the path,
o 在对路径上的度量进行精确测量方面，证明组合的实际效用，
o a justification of the usefulness of the composition in terms of making analysis of the path using A-frame concepts more effective, and
o 在使使用a框架概念的路径分析更有效方面，证明组合的有用性，以及
o an analysis of how the conjecture could be incorrect.
o 对这个猜想如何可能不正确的分析。
Also, [RFC2330] gives an example using the conjecture that the delay of a path is very nearly the sum of the delays of the exchanges and clouds of the corresponding path digest. This example is particularly relevant to those who wish to assess the performance of an inter-domain path without direct measurement, and the performance estimate of the complete path is related to the measured results for various sub-paths instead.
此外，[RFC2330]给出了一个示例，使用了一个假设，即路径的延迟非常接近于相应路径摘要的交换和云的延迟之和。该示例尤其适用于那些希望在不进行直接测量的情况下评估域间路径的性能的人，并且完整路径的性能估计与各种子路径的测量结果相关。
Approximate functions between the sub-path and complete path metrics are useful, with knowledge of the circumstances where the relationships are/are not applicable. For example, we would not expect that delay singletons from each sub-path would sum to produce an accurate estimate of a delay singleton for the complete path (unless all the delays were essentially constant -- very unlikely). However, other delay statistics (based on a reasonable sample size) may have a sufficiently large set of circumstances where they are applicable.
子路径和完整路径度量之间的近似函数是有用的，了解关系适用/不适用的情况。例如，我们不希望来自每个子路径的延迟单例之和能够产生完整路径的延迟单例的准确估计（除非所有延迟基本上是恒定的——这是不可能的）。然而，其他延迟统计数据（基于合理的样本量）可能有一组足够大的适用情况。
One-way metrics defined in other RFCs (such as [RFC2679] and [RFC2680]) all assume that the measurement can be practically carried out between the source and the destination of interest. Sometimes there are reasons that the measurement cannot be executed from the source to the destination. For instance, the measurement path may cross several independent domains that have conflicting policies, measurement tools and methods, and measurement time assignment. The solution then may be the composition of several sub-path measurements. This means each domain performs the one-way measurement on a sub-path between two nodes that are involved in the complete path, following its own policy, using its own measurement tools and methods, and using its own measurement timing. Under the appropriate conditions, one can combine the sub-path one-way metric results to estimate the complete path one-way measurement metric with some degree of accuracy.
其他RFC（如[RFC2679]和[RFC2680]）中定义的单向度量都假设测量实际上可以在感兴趣的源和目标之间进行。有时，由于某些原因，无法从源到目标执行测量。例如，度量路径可能跨越多个独立的域，这些域具有冲突的策略、度量工具和方法以及度量时间分配。然后，解决方案可以是多个子路径测量的组合。这意味着每个域在完整路径中涉及的两个节点之间的子路径上执行单向测量，遵循其自己的策略，使用其自己的测量工具和方法，并使用其自己的测量定时。在适当的条件下，可以结合子路径单向度量结果来估计具有一定精度的完整路径单向度量。
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 [RFC2119].
本文件中的关键词“必须”、“不得”、“要求”、“应”、“不应”、“应”、“不应”、“建议”、“可”和“可选”应按照RFC 2119[RFC2119]中所述进行解释。
In this memo, the characters "<=" should be read as "less than or equal to" and ">=" as "greater than or equal to".
在此备忘录中，字符“<=”应理解为“小于或等于”，而“>=”应理解为“大于或等于”。
For the primary IP Performance Metrics RFCs for loss [RFC2680], delay [RFC2679], and delay variation [RFC3393], this memo gives a set of metrics that can be composed from the same or similar sub-path metrics. This means that the composition function may utilize:
对于丢失[RFC2680]、延迟[RFC2679]和延迟变化[RFC3393]的主要IP性能指标RFC，本备忘录提供了一组可由相同或类似子路径指标组成的指标。这意味着合成功能可以利用：
o the same metric for each sub-path;
o 每个子路径的相同度量；
o multiple metrics for each sub-path (possibly one that is the same as the complete path metric);
o 每个子路径的多个度量（可能与完整路径度量相同）；
o a single sub-path metric that is different from the complete path metric;
o 与完整路径度量不同的单个子路径度量；
o different measurement techniques like active [RFC2330], [RFC3432] and passive [RFC5474].
o 不同的测量技术，如有源[RFC2330]、[RFC3432]和无源[RFC5474]。
We note a possibility: using a complete path metric and all but one sub-path metric to infer the performance of the missing sub-path, especially when the "last" sub-path metric is missing. However, such de-composition calculations, and the corresponding set of issues they raise, are beyond the scope of this memo.
我们注意到一种可能性：使用完整的路径度量和除一个子路径度量之外的所有子路径度量来推断缺失子路径的性能，特别是当“最后”子路径度量缺失时。然而，此类分解计算及其引发的相应问题超出了本备忘录的范围。
The composition framework [RFC5835] requires the specification of the applicable circumstances for each metric. In particular, each section addresses whether the metric:
组合框架[RFC5835]要求对每个指标的适用情况进行说明。特别是，每个部分都说明了度量是否：
o Requires the same test packets to traverse all sub-paths or may use similar packets sent and collected separately in each sub-path.
o 要求相同的测试数据包遍历所有子路径，或者可以使用在每个子路径中单独发送和收集的类似数据包。
o Requires homogeneity of measurement methodologies or can allow a degree of flexibility (e.g., active, active spatial division [RFC5644], or passive methods produce the "same" metric). Also, the applicable sending streams will be specified, such as Poisson, Periodic, or both.
o 要求测量方法的同质性或允许一定程度的灵活性（例如，主动、主动空间分割[RFC5644]，或被动方法产生“相同”度量）。此外，将指定适用的发送流，例如泊松流、周期流或两者。
o Needs information or access that will only be available within an operator's domain, or is applicable to inter-domain composition.
o 需要仅在运营商的域内可用的信息或访问权限，或适用于域间组合的信息或访问权限。
o Requires synchronized measurement start and stop times in all sub-paths or largely overlapping measurement intervals, or no timing requirements.
o 要求在所有子路径中同步测量开始和停止时间，或测量间隔基本重叠，或无计时要求。
o Requires the assumption of sub-path independence with regard to the metric being defined/composed or other assumptions.
o 要求假设子路径独立于定义/组成的度量或其他假设。
o Has known sources of inaccuracy/error and identifies the sources.
o 已知不准确/错误的来源，并确定来源。
In practice, when measurements cannot be initiated on a sub-path (and perhaps the measurement system gives up during the test interval), then there will not be a value for the sub-path reported, and the entire test result SHOULD be recorded as "undefined". This case should be distinguished from the case where the measurement system continued to send packets throughout the test interval, but all were declared lost.
实际上，当无法在子路径上启动测量时（可能测量系统在测试间隔期间放弃），则不会报告子路径的值，整个测试结果应记录为“未定义”。这种情况应与测量系统在整个测试间隔内继续发送数据包，但所有数据包均被宣布丢失的情况区分开来。
When a composed metric requires measurements from sub-paths A, B, and C, and one or more of the sub-path results are undefined, then the composed metric SHOULD also be recorded as undefined.
当合成度量要求从子路径a、B和C进行测量，并且一个或多个子路径结果未定义时，则合成度量也应记录为未定义。
To reduce the redundant information presented in the detailed metrics sections that follow, this section presents the specifications that are common to two or more metrics. The section is organized using the same subsections as the individual metrics, to simplify comparisons.
为了减少后面的详细度量部分中显示的冗余信息，本部分介绍了两个或多个度量的通用规范。本节使用与单个指标相同的小节来组织，以简化比较。
Also, the index variables are represented as follows:
此外，索引变量表示如下：
o m = index for packets sent.
o m=已发送数据包的索引。
o n = index for packets received.
o n=接收到的数据包的索引。
o s = index for involved sub-paths.
o s=相关子路径的索引。
All metrics use the "Type-P" convention as described in [RFC2330]. The rest of the name is unique to each metric.
所有指标均使用[RFC2330]中所述的“P型”约定。名称的其余部分对于每个度量都是唯一的。
o Src, the IP address of a host.
o Src，主机的IP地址。
o Dst, the IP address of a host.
o Dst，主机的IP地址。
o T, a time (start of test interval).
o T、 时间（测试间隔的开始）。
o Tf, a time (end of test interval).
o Tf，一个时间（测试间隔结束）。
o lambda, a rate in reciprocal seconds (for Poisson Streams).
o lambda，以倒数秒为单位的速率（对于泊松流）。
o incT, the nominal duration of inter-packet interval, first bit to first bit (for Periodic Streams).
o incT，数据包间隔的标称持续时间，从第一位到第一位（对于周期性流）。
o dT, the duration of the allowed interval for Periodic Stream sample start times.
o dT，周期性流采样开始时间的允许间隔的持续时间。
o T0, a time that MUST be selected at random from the interval [T, T + dT] to start generating packets and taking measurements (for Periodic Streams).
o T0，必须从间隔[T，T+dT]中随机选择的时间，以开始生成分组并进行测量（对于周期流）。
o TstampSrc, the wire time of the packet as measured at MP(Src) (measurement point at the source).
o TstampSrc，在MP（Src）（源处的测量点）处测量的数据包的连线时间。
o TstampDst, the wire time of the packet as measured at MP(Dst), assigned to packets that arrive within a "reasonable" time.
o TstampDst，在MP（Dst）处测量的数据包的连线时间，分配给在“合理”时间内到达的数据包。
o Tmax, a maximum waiting time for packets at the destination, set sufficiently long to disambiguate packets with long delays from packets that are discarded (lost); thus, the distribution of delay is not truncated.
o Tmax，目的地数据包的最大等待时间，设置为足够长，以消除丢弃（丢失）数据包的长延迟数据包的歧义；因此，延迟的分布不会被截断。
o M, the total number of packets sent between T0 and Tf.
o M、 T0和Tf之间发送的数据包总数。
o N, the total number of packets received at Dst (sent between T0 and Tf).
o N、 在Dst接收的数据包总数（在T0和Tf之间发送）。
o S, the number of sub-paths involved in the complete Src-Dst path.
o S、 完整Src Dst路径中涉及的子路径数。
o Type-P, as defined in [RFC2330], which includes any field that may affect a packet's treatment as it traverses the network.
o 类型-P，如[RFC2330]中所定义，包括在数据包通过网络时可能影响其处理的任何字段。
In metric names, the term "<Sample>" is intended to be replaced by the name of the method used to define a sample of values of parameter TstampSrc. This can be done in several ways, including:
在度量名称中，术语“<Sample>”将替换为用于定义参数TstampSrc的值样本的方法名称。这可以通过几种方式实现，包括：
1. Poisson: a pseudo-random Poisson process of rate lambda, whose values fall between T and Tf. The time interval between successive values of TstampSrc will then average 1/lambda, as per [RFC2330].
1. 泊松：速率λ的伪随机泊松过程，其值介于T和Tf之间。根据[RFC2330]，TstampSrc连续值之间的时间间隔将平均为1/lambda。
2. Periodic: a Periodic stream process with pseudo-random start time T0 between T and dT, and nominal inter-packet interval incT, as per [RFC3432].
2. 周期性：根据[RFC3432]，周期性流过程的伪随机开始时间T0介于T和dT之间，且标称数据包间隔incT。
This section is unique for every metric.
此部分对于每个度量都是唯一的。
This section is unique for every metric.
此部分对于每个度量都是唯一的。
This section is unique for every metric.
此部分对于每个度量都是唯一的。
This section is unique for every metric.
此部分对于每个度量都是唯一的。
This section is unique for each metric. The term "ground truth" is frequently used in these sections and is defined in Section 4.7 of [RFC5835].
此部分对于每个度量都是唯一的。这些章节中经常使用术语“基本事实”，并在[RFC5835]的第4.7节中进行了定义。
It is sometimes impractical to conduct active measurements between every Src-Dst pair. Since the full mesh of N measurement points grows as N x N, the scope of measurement may be limited by testing resources.
在每个Src Dst对之间进行主动测量有时是不切实际的。由于N个测量点的完整网格随着N x N的增加而增加，因此测量范围可能会受到测试资源的限制。
There may be varying limitations on active testing in different parts of the network. For example, it may not be possible to collect the desired sample size in each test interval when access link speed is limited, because of the potential for measurement traffic to degrade the user traffic performance. The conditions on a low-speed access link may be understood well enough to permit use of a small sample size/rate, while a larger sample size/rate may be used on other sub-paths.
网络不同部分的主动测试可能有不同的限制。例如，当接入链路速度受到限制时，可能不可能在每个测试间隔中收集期望的样本大小，因为测量流量可能会降低用户流量性能。可以充分理解低速接入链路上的条件，以允许使用小样本大小/速率，而在其他子路径上可以使用较大的样本大小/速率。
Also, since measurement operations have a real monetary cost, there is value in re-using measurements where they are applicable, rather than launching new measurements for every possible source-destination pair.
此外，由于测量操作有实际的货币成本，因此在适用的地方重新使用测量值是有价值的，而不是为每个可能的源-目的地对启动新的测量值。
The measurement packets, each having source and destination addresses intended for collection at edges of the sub-path, may take a different specific path through the network equipment and links when compared to packets with the source and destination addresses of the complete path. Example sources of parallel paths include Equal Cost Multi-Path and parallel (or bundled) links. Therefore, the performance estimated from the composition of sub-path measurements may differ from the performance experienced by packets on the complete path. Multiple measurements employing sufficient sub-path address pairs might produce bounds on the extent of this error.
当与具有完整路径的源地址和目的地址的分组相比时，每个具有打算在子路径的边缘处收集的源地址和目的地址的测量分组可以通过网络设备和链路采用不同的特定路径。并行路径的示例来源包括等成本多路径和并行（或捆绑）链路。因此，从子路径测量的组合估计的性能可能不同于完整路径上的分组所经历的性能。使用足够的子路径地址对的多次测量可能会对此错误的范围产生限制。
We also note the possibility of re-routing during a measurement interval, as it may affect the correspondence between packets traversing the complete path and the sub-paths that were "involved" prior to the re-route.
我们还注意到在测量间隔期间重新路由的可能性，因为它可能会影响穿过完整路径的数据包与重新路由之前“涉及”的子路径之间的对应关系。
Related to the case of an alternate path described above is the case where elements in the measured path are unique to measurement system connectivity. For example, a measurement system may use a dedicated link to a LAN switch, and packets on the complete path do not traverse that link. The performance of such a dedicated link would be measured continuously, and its contribution to the sub-path metrics SHOULD be minimized as a source of error.
与上述备用路径的情况相关的是测量路径中的元件对于测量系统连接是唯一的情况。例如，测量系统可能使用到LAN交换机的专用链路，完整路径上的数据包不会穿过该链路。这种专用链路的性能将被持续测量，其对子路径度量的贡献应作为误差源最小化。
Measurements of sub-path performance may not cover all the network elements on the complete path. For example, the network exchange points might be excluded unless a cooperative measurement is conducted. In this example, test packets on the previous sub-path are received just before the exchange point, and test packets on the next sub-path are injected just after the same exchange point. Clearly, the set of sub-path measurements SHOULD cover all critical network elements in the complete path.
子路径性能的测量可能不包括完整路径上的所有网络元素。例如，除非进行合作测量，否则可以排除网络交换点。在此示例中，前一子路径上的测试数据包在交换点之前接收，下一子路径上的测试数据包在同一交换点之后注入。显然，子路径测量集应该覆盖完整路径中的所有关键网络元素。
At a specific point in time, no viable route exists between the complete path source and destination. The routes selected for one or more sub-paths therefore differ from the complete path. Consequently, spatial composition may produce finite estimation of a ground truth metric (see Section 4.7 of [RFC5835]) between a source and a destination, even when the route between them is undefined.
在特定时间点，完整路径源和目标之间不存在可行的路由。因此，为一个或多个子路径选择的路由与完整路径不同。因此，即使源和目的地之间的路线未定义，空间合成也可能产生源和目的地之间的地面真值度量的有限估计（见[RFC5835]第4.7节）。
This section is unique for most metrics (see the metric-specific sections).
此部分对于大多数度量是唯一的（请参阅特定于度量的部分）。
For delay-related metrics, one-way delay always depends on packet size and link capacity, since it is measured in [RFC2679] from first bit to last bit. If the size of an IP packet changes on its route (due to encapsulation), this can influence delay performance. However, the main error source may be the additional processing associated with encapsulation and encryption/decryption if not experienced or accounted for in sub-path measurements.
对于延迟相关指标，单向延迟始终取决于数据包大小和链路容量，因为它是在[RFC2679]中从第一位到最后一位测量的。如果IP数据包的大小在其路由上发生变化（由于封装），这可能会影响延迟性能。然而，如果在子路径测量中未经历或未考虑，则主要错误源可能是与封装和加密/解密相关的附加处理。
Fragmentation is a major issue for composition accuracy, since all metrics require all fragments to arrive before proceeding, and fragmented complete path performance is likely to be different from performance with non-fragmented packets and composed metrics based on non-fragmented sub-path measurements.
碎片化是合成准确性的一个主要问题，因为所有度量都要求所有碎片在继续之前到达，碎片化完整路径性能可能不同于非碎片化数据包和基于非碎片化子路径度量的合成度量的性能。
Highly manipulated routing can cause measurement error if not expected and compensated for. For example, policy-based MPLS routing could modify the class of service for the sub-paths and complete path.
如果没有预料到并得到补偿，高度操纵的路由可能会导致测量误差。例如，基于策略的MPLS路由可以修改子路径和完整路径的服务类别。
o The methodology SHOULD use similar packets sent and collected separately in each sub-path, where "similar" in this case means that Type-P contains as many equal attributes as possible, while recognizing that there will be differences. Note that Type-P includes stream characteristics (e.g., Poisson, Periodic).
o 该方法应使用在每个子路径中分别发送和收集的相似数据包，在这种情况下，“相似”意味着Type-P包含尽可能多的相同属性，同时认识到将存在差异。请注意，P型包括流特征（例如，泊松、周期性）。
o The methodology allows a degree of flexibility regarding test stream generation (e.g., active or passive methods can produce an equivalent result, but the lack of control over the source, timing, and correlation of passive measurements is much more challenging).
o 该方法允许在测试流生成方面具有一定程度的灵活性（例如，主动或被动方法可以产生等效的结果，但对被动测量的源、定时和相关性缺乏控制更具挑战性）。
o Poisson and/or Periodic streams are RECOMMENDED.
o 建议使用泊松流和/或周期流。
o The methodology applies to both inter-domain and intra-domain composition.
o 该方法适用于域间和域内合成。
o The methodology SHOULD have synchronized measurement time intervals in all sub-paths, but largely overlapping intervals MAY suffice.
o 该方法应在所有子路径中具有同步的测量时间间隔，但大量重叠的间隔可能就足够了。
o Assumption of sub-path independence with regard to the metric being defined/composed is REQUIRED.
o 需要对定义/组成的度量进行子路径独立性假设。
This metric is a necessary element of delay composition metrics, and its definition does not formally exist elsewhere in IPPM literature.
该度量是延迟合成度量的一个必要元素，其定义在IPPM文献的其他地方并不正式存在。
See the common parameters section (Section 3.1.1).
见通用参数部分（第3.1.1节）。
Using the parameters above, we obtain the value of the Type-P-One-way-Delay singleton as per [RFC2679].
使用上述参数，我们根据[RFC2679]获得了P型单向延迟单例的值。
For each packet "[i]" that has a finite one-way delay (in other words, excluding packets that have undefined one-way delay):
对于具有有限单向延迟的每个数据包“[i]”（换句话说，不包括具有未定义单向延迟的数据包）：
Type-P-Finite-One-way-Delay-<Sample>-Stream[i] =
Type-P-Finite-One-way-Delay-<Sample>-Stream[i] =
FiniteDelay[i] = TstampDst - TstampSrc
有限延时[i]=TstampDst-TstampSrc
This metric is measured in units of time in seconds, expressed in sufficiently low resolution to convey meaningful quantitative information. For example, resolution of microseconds is usually sufficient.
该度量以秒为时间单位，以足够低的分辨率表示，以传达有意义的定量信息。例如，微秒的分辨率通常就足够了。
The "Type-P-Finite-One-way-Delay" metric permits calculation of the sample mean statistic. This resolves the problem of including lost packets in the sample (whose delay is undefined) and the issue with the informal assignment of infinite delay to lost packets (practical systems can only assign some very large value).
“P型有限单向延迟”度量允许计算样本平均统计。这解决了在样本中包含丢失数据包（其延迟未定义）的问题，以及对丢失数据包非正式分配无限延迟的问题（实际系统只能分配一些非常大的值）。
The Finite-One-way-Delay approach handles the problem of lost packets by reducing the event space. We consider conditional statistics, and estimate the mean one-way delay conditioned on the event that all packets in the sample arrive at the destination (within the specified waiting time, Tmax). This offers a way to make some valid statements about one-way delay, at the same time avoiding events with undefined outcomes. This approach is derived from the treatment of lost packets in [RFC3393], and is similar to [Y.1540].
有限单向延迟方法通过减少事件空间来处理丢失数据包的问题。我们考虑条件统计，并且估计在样本中的所有分组到达目的地（在指定的等待时间，Tmax）的情况下条件的平均单向延迟。这提供了一种方法，可以对单向延迟做出一些有效的陈述，同时避免发生结果未定义的事件。该方法源自[RFC3393]中对丢失数据包的处理，类似于[Y.1540]。
All statistics defined in [RFC2679] are applicable to the finite one-way delay, and additional metrics are possible, such as the mean (see below).
[RFC2679]中定义的所有统计数据均适用于有限单向延迟，并且可以使用其他指标，如平均值（见下文）。
This section describes a statistic based on the Type-P-Finite-One-way-Delay-<Sample>-Stream metric.
本节描述基于Type-P-Finite-One-way-Delay-<Sample>流度量的统计信息。
See the common parameters section (Section 3.1.1).
见通用参数部分（第3.1.1节）。
We define
我们定义
Type-P-Finite-One-way-Delay-Mean =
P型有限单向延迟平均=
N
---
1 \
MeanDelay = - * > (FiniteDelay [n])
N /
---
n = 1
N
---
1 \
MeanDelay = - * > (FiniteDelay [n])
N /
---
n = 1
where all packets n = 1 through N have finite singleton delays.
其中，所有数据包n=1到n具有有限的单态延迟。
This metric is measured in units of time in seconds, expressed in sufficiently fine resolution to convey meaningful quantitative information. For example, resolution of microseconds is usually sufficient.
该度量以秒为时间单位，以足够精细的分辨率表示，以传达有意义的定量信息。例如，微秒的分辨率通常就足够了。
The Type-P-Finite-One-way-Delay-Mean metric requires the conditional delay distribution described in Section 4.1.3.
P型有限单向延迟平均度量要求第4.1.3节中描述的条件延迟分布。
This metric, a mean, does not require additional statistics.
这个指标是一个平均值，不需要额外的统计数据。
The Type-P-Finite-Composite-One-way-Delay-Mean, or CompMeanDelay, for the complete source to destination path can be calculated from the sum of the mean delays of all of its S constituent sub-paths.
完整源到目的地路径的P型有限复合单向延迟平均值，或CompMeanDelay，可以从其所有组成子路径的平均延迟之和计算得出。
Then the
然后
Type-P-Finite-Composite-One-way-Delay-Mean =
P型有限复合单向延迟平均=
S
---
\
CompMeanDelay = > (MeanDelay [s])
/
---
s = 1
S
---
\
CompMeanDelay = > (MeanDelay [s])
/
---
s = 1
where sub-paths s = 1 to S are involved in the complete path.
其中子路径s=1到s涉及完整路径。
The mean of a sufficiently large stream of packets measured on each sub-path during the interval [T, Tf] will be representative of the ground truth mean of the delay distribution (and the distributions themselves are sufficiently independent), such that the means may be added to produce an estimate of the complete path mean delay.
在间隔[T，Tf]期间在每个子路径上测量的足够大的分组流的平均值将代表延迟分布的基本真值平均值（并且分布本身足够独立），使得可以添加该平均值以产生完整路径平均延迟的估计。
It is assumed that the one-way delay distributions of the sub-paths and the complete path are continuous. The mean of multi-modal distributions has the unfortunate property that such a value may never occur.
假设子路径和完整路径的单向延迟分布是连续的。多模分布的平均值具有一个不幸的特性，即这样的值可能永远不会出现。
See the common section (Section 3).
见通用部分（第3节）。
See the common section (Section 3).
见通用部分（第3节）。
If any of the sub-path distributions are multi-modal, then the measured means may not be stable, and in this case the mean will not be a particularly useful statistic when describing the delay distribution of the complete path.
如果任何子路径分布是多模态的，则测量的平均值可能不稳定，并且在这种情况下，当描述完整路径的延迟分布时，平均值将不是特别有用的统计量。
The mean may not be a sufficiently robust statistic to produce a reliable estimate, or to be useful even if it can be measured.
平均值可能不是一个足够稳健的统计数据，无法产生可靠的估计值，或者即使可以测量，也不可能有用。
If a link contributing non-negligible delay is erroneously included or excluded, the composition will be in error.
如果造成不可忽略延迟的链路被错误地包括或排除，则合成将是错误的。
The requirements of the common section (Section 3) apply here as well.
通用部分（第3节）的要求也适用于此处。
This section describes a statistic based on the Type-P-Finite-One-way-Delay-<Sample>-Stream metric, and the composed metric based on that statistic.
本节描述了基于Type-P-Finite-One-way-Delay-<Sample>流度量的统计信息，以及基于该统计信息的合成度量信息。
See the common parameters section (Section 3.1.1).
见通用参数部分（第3.1.1节）。
We define
我们定义
Type-P-Finite-One-way-Delay-Minimum =
P型有限单向延迟最小值=
MinDelay = (FiniteDelay [j])
MinDelay = (FiniteDelay [j])
such that for some index, j, where 1 <= j <= N
FiniteDelay[j] <= FiniteDelay[n] for all n
such that for some index, j, where 1 <= j <= N
FiniteDelay[j] <= FiniteDelay[n] for all n
where all packets n = 1 through N have finite singleton delays.
其中，所有数据包n=1到n具有有限的单态延迟。
This metric is measured in units of time in seconds, expressed in sufficiently fine resolution to convey meaningful quantitative information. For example, resolution of microseconds is usually sufficient.
该度量以秒为时间单位，以足够精细的分辨率表示，以传达有意义的定量信息。例如，微秒的分辨率通常就足够了。
The Type-P-Finite-One-way-Delay-Minimum metric requires the conditional delay distribution described in Section 4.1.3.
P型有限单向延迟最小度量要求第4.1.3节中描述的条件延迟分布。
This metric, a minimum, does not require additional statistics.
该指标至少不需要额外的统计数据。
The Type-P-Finite-Composite-One-way-Delay-Minimum, or CompMinDelay, for the complete source to destination path can be calculated from the sum of the minimum delays of all of its S constituent sub-paths.
完整源到目标路径的P型有限复合单向延迟最小值，或CompMinDelay，可以从其所有组成子路径的最小延迟之和计算得出。
Then the
然后
Type-P-Finite-Composite-One-way-Delay-Minimum =
P型有限复合单向延迟最小值=
S
---
\
CompMinDelay = > (MinDelay [s])
/
---
s = 1
S
---
\
CompMinDelay = > (MinDelay [s])
/
---
s = 1
The minimum of a sufficiently large stream of packets measured on each sub-path during the interval [T, Tf] will be representative of the ground truth minimum of the delay distribution (and the distributions themselves are sufficiently independent), such that the minima may be added to produce an estimate of the complete path minimum delay.
在间隔[T，Tf]期间在每个子路径上测量的足够大的分组流的最小值将代表延迟分布的基本真值最小值（并且分布本身足够独立），使得可以添加最小值以产生完整路径最小延迟的估计。
It is assumed that the one-way delay distributions of the sub-paths and the complete path are continuous.
假设子路径和完整路径的单向延迟分布是连续的。
See the common section (Section 3).
见通用部分（第3节）。
See the common section (Section 3).
见通用部分（第3节）。
If the routing on any of the sub-paths is not stable, then the measured minimum may not be stable. In this case the composite minimum would tend to produce an estimate for the complete path that may be too low for the current path.
如果任何子路径上的路由不稳定，则测量的最小值可能不稳定。在这种情况下，复合最小值将倾向于产生对完整路径的估计，该估计对于当前路径可能太低。
The requirements of the common section (Section 3) apply here as well.
通用部分（第3节）的要求也适用于此处。
See the common parameters section (Section 3.1.1).
见通用参数部分（第3.1.1节）。
Using the parameters above, we obtain the value of the Type-P-One-way-Packet-Loss singleton and stream as per [RFC2680].
使用上述参数，我们根据[RFC2680]获得类型-P-单向丢包单例和流的值。
We obtain a sequence of pairs with elements as follows:
我们获得了一系列元素对，如下所示：
o TstampSrc, as above.
o TstampSrc，如上所述。
o L, either zero or one, where L = 1 indicates loss and L = 0 indicates arrival at the destination within TstampSrc + Tmax.
o 五十、 零或一，其中L=1表示丢失，L=0表示到达TstampSrc+Tmax内的目的地。
None.
没有一个
Given the stream parameter M, the number of packets sent, we can define the Empirical Probability of Loss Statistic (Ep), consistent with average loss in [RFC2680], as follows:
给定流参数M，即发送的数据包数量，我们可以定义与[RFC2680]中的平均丢失一致的丢失统计经验概率（Ep），如下所示：
Type-P-One-way-Packet-Loss-Empirical-Probability =
P型单向丢包经验概率=
M
---
1 \
Ep = - * > (L[m])
M /
---
m = 1
M
---
1 \
Ep = - * > (L[m])
M /
---
m = 1
where all packets m = 1 through M have a value for L.
其中，所有数据包m=1到m都有一个L值。
The Type-P-One-way-Composite-Packet-Loss-Empirical-Probability, or CompEp, for the complete source to destination path can be calculated by combining the Ep of all of its constituent sub-paths (Ep1, Ep2, Ep3, ... Epn) as
完整源到目的地路径的类型P-单向-复合-分组丢失-经验-概率，或CompEp，可通过组合其所有组成子路径（Ep1、Ep2、Ep3、…Epn）的Ep来计算，如下所示：
Type-P-Composite-One-way-Packet-Loss-Empirical-Probability =
P型复合单向丢包经验概率=
CompEp = 1 - {(1 - Ep1) x (1 - Ep2) x (1 - Ep3) x ... x (1 - EpS)}
CompEp = 1 - {(1 - Ep1) x (1 - Ep2) x (1 - Ep3) x ... x (1 - EpS)}
If any Eps is undefined in a particular measurement interval, possibly because a measurement system failed to report a value, then any CompEp that uses sub-path s for that measurement interval is undefined.
如果在特定测量间隔内未定义任何Eps，可能是因为测量系统未能报告值，则使用该测量间隔的子路径s的任何COMEP均未定义。
The empirical probability of loss calculated on a sufficiently large stream of packets measured on each sub-path during the interval [T, Tf] will be representative of the ground truth empirical loss probability (and the probabilities themselves are sufficiently independent), such that the sub-path probabilities may be combined to produce an estimate of the complete path empirical loss probability.
在间隔[T，Tf]期间在每个子路径上测量的足够大的分组流上计算的丢失的经验概率将代表地面真实经验丢失概率（并且概率本身充分独立），这样子路径概率可被组合以产生完整路径经验损失概率的估计。
See the common section (Section 3).
见通用部分（第3节）。
See the common section (Section 3).
见通用部分（第3节）。
A concern for loss measurements combined in this way is that root causes may be correlated to some degree.
以这种方式组合的损失测量的一个问题是，根本原因可能在某种程度上相互关联。
For example, if the links of different networks follow the same physical route, then a single catastrophic event like a fire in a tunnel could cause an outage or congestion on remaining paths in multiple networks. Here it is important to ensure that measurements before the event and after the event are not combined to estimate the composite performance.
例如，如果不同网络的链路遵循相同的物理路径，那么像隧道火灾这样的单一灾难性事件可能会导致多个网络中剩余路径的中断或拥塞。在这里，重要的是确保事件前和事件后的测量值不被合并以估计复合性能。
Or, when traffic volumes rise due to the rapid spread of an email-borne worm, loss due to queue overflow in one network may help another network to carry its traffic without loss.
或者，当由于电子邮件传播的蠕虫病毒的快速传播而导致流量增加时，一个网络中由于队列溢出而造成的损失可能有助于另一个网络在不损失流量的情况下承载其流量。
See the common section (Section 3).
见通用部分（第3节）。
This packet delay variation (PDV) metric is a necessary element of Composed Delay Variation metrics, and its definition does not formally exist elsewhere in IPPM literature (with the exception of [RFC5481]).
该数据包延迟变化（PDV）度量是合成延迟变化度量的必要元素，其定义在IPPM文献的其他地方没有正式存在（RFC5481除外）。
In addition to the parameters of Section 3.1.1:
除第3.1.1节的参数外：
o TstampSrc[i], the wire time of packet[i] as measured at MP(Src) (measurement point at the source).
o TstampSrc[i]，在MP（Src）（源处的测量点）处测量的数据包[i]的连线时间。
o TstampDst[i], the wire time of packet[i] as measured at MP(Dst), assigned to packets that arrive within a "reasonable" time.
o TstampDst[i]，在MP（Dst）处测量的数据包[i]的连线时间，分配给在“合理”时间内到达的数据包。
o B, a packet length in bits.
o B、 以位为单位的数据包长度。
o F, a selection function unambiguously defining the packets from the stream that are selected for the packet-pair computation of this metric. F(current packet), the first packet of the pair, MUST have a valid Type-P-Finite-One-way-Delay less than Tmax (in other words, excluding packets that have undefined one-way delay) and MUST have been transmitted during the interval [T, Tf]. The second packet in the pair, F(min_delay packet) MUST be the packet with the minimum valid value of Type-P-Finite-One-way-Delay for the stream, in addition to the criteria for F(current packet). If multiple packets have equal minimum Type-P-Finite-One-way-Delay values, then the value for the earliest arriving packet SHOULD be used.
o F、 选择函数明确定义流中为该度量的数据包对计算而选择的数据包。F（当前数据包），该对中的第一个数据包，必须具有小于Tmax的有效类型-P-有限单向延迟（换句话说，不包括具有未定义单向延迟的数据包），并且必须在间隔[T，Tf]期间传输。这对数据包中的第二个数据包F（minu delay packet）必须是流的最小有效值为Type-P-Finite-One-way-delay的数据包，以及F（当前数据包）的标准。如果多个数据包的最小Type-P-Finite-One-way-Delay值相等，则应使用最早到达的数据包的值。
o MinDelay, the Type-P-Finite-One-way-Delay value for F(min_delay packet) given above.
o MinDelay，上面给出的F（最小延迟包）的P型有限单向延迟值。
o N, the number of packets received at the destination that meet the F(current packet) criteria.
o N、 在目的地接收的满足F（当前数据包）标准的数据包数。
Using the definition above in Section 5.1.2, we obtain the value of Type-P-Finite-One-way-Delay-<Sample>-Stream[n], the singleton for each packet[i] in the stream (a.k.a. FiniteDelay[i]).
使用上述第5.1.2节中的定义，我们获得Type-P-Finite-One-way-Delay-<Sample>-Stream[n]的值，即流中每个包[i]的单态（也称为FiniteDelay[i]）。
For each packet[n] that meets the F(first packet) criteria given
above: Type-P-One-way-pdv-refmin-<Sample>-Stream[n] =
For each packet[n] that meets the F(first packet) criteria given
above: Type-P-One-way-pdv-refmin-<Sample>-Stream[n] =
PDV[n] = FiniteDelay[n] - MinDelay
PDV[n]=有限时间[n]-思维时间
where PDV[i] is in units of time in seconds, expressed in sufficiently fine resolution to convey meaningful quantitative information. For example, resolution of microseconds is usually sufficient.
其中，PDV[i]以秒为时间单位，以足够精细的分辨率表示，以传达有意义的定量信息。例如，微秒的分辨率通常就足够了。
This metric produces a sample of delay variation normalized to the minimum delay of the sample. The resulting delay variation distribution is independent of the sending sequence (although specific FiniteDelay values within the distribution may be correlated, depending on various stream parameters such as packet spacing). This metric is equivalent to the IP Packet Delay Variation parameter defined in [Y.1540].
该度量生成一个延迟变化样本，该样本标准化为样本的最小延迟。所得到的延迟变化分布独立于发送序列（尽管分布中的特定有限延迟值可能是相关的，这取决于诸如分组间隔之类的各种流参数）。该指标相当于[Y.1540]中定义的IP数据包延迟变化参数。
We define the mean PDV as follows (where all packets n = 1 through N have a value for PDV[n]):
我们将平均PDV定义如下（其中所有数据包n=1到n都有一个PDV[n]的值）：
Type-P-One-way-pdv-refmin-Mean = MeanPDV =
类型-P-单向-pdv-参考最小值-平均值=平均值pdv=
N
---
1 \
- * > (PDV[n])
N /
---
n = 1
N
---
1 \
- * > (PDV[n])
N /
---
n = 1
We define the variance of PDV as follows:
我们将PDV的方差定义如下：
Type-P-One-way-pdv-refmin-Variance = VarPDV =
Type-P-One-way-pdv-refmin-Variance=VarPDV=
N
---
1 \ 2
------- > (PDV[n] - MeanPDV)
(N - 1) /
---
n = 1
N
---
1 \ 2
------- > (PDV[n] - MeanPDV)
(N - 1) /
---
n = 1
We define the skewness of PDV as follows:
我们将PDV的偏度定义如下：
Type-P-One-way-pdv-refmin-Skewness = SkewPDV =
Type-P-One-way-pdv-refmin-Skewness=SkewPDV=
N
--- 3
\ / \
> | PDV[n] - MeanPDV |
/ \ /
---
n = 1
-----------------------------------
/ \
| ( 3/2 ) |
\ (N - 1) * VarPDV /
N
--- 3
\ / \
> | PDV[n] - MeanPDV |
/ \ /
---
n = 1
-----------------------------------
/ \
| ( 3/2 ) |
\ (N - 1) * VarPDV /
(See Appendix X of [Y.1541] for additional background information.)
（更多背景信息，请参见[Y.1541]的附录X。）
We define the quantile of the PDV sample as the value where the specified fraction of singletons is less than the given value.
我们将PDV样本的分位数定义为指定的单态分数小于给定值的值。
This section gives two alternative composition functions. The objective is to estimate a quantile of the complete path delay variation distribution. The composed quantile will be estimated using information from the sub-path delay variation distributions.
本节给出了两种可选的合成函数。目标是估计完整路径延迟变化分布的分位数。将使用来自子路径延迟变化分布的信息来估计合成分位数。
The Type-P-Finite-One-way-Delay-<Sample>-Stream samples from each sub-path are summarized as a histogram with 1-ms bins representing the one-way delay distribution.
来自每个子路径的P型有限单向延迟流样本汇总为直方图，其中1-ms单元表示单向延迟分布。
From [STATS], the distribution of the sum of independent random variables can be derived using the relation:
根据[STATS]，可以使用以下关系式得出独立随机变量之和的分布：
Type-P-Composite-One-way-pdv-refmin-quantile-a =
类型-P-复合-单向-pdv-refmin-分位数-a=
. .
/ /
P(X + Y + Z <= a) = | | P(X <= a - y - z) * P(Y = y) * P(Z = z) dy dz
/ /
` `
z y
. .
/ /
P(X + Y + Z <= a) = | | P(X <= a - y - z) * P(Y = y) * P(Z = z) dy dz
/ /
` `
z y
Note that dy and dz indicate partial integration above, and that y and z are the integration variables. Also, the probability of an outcome is indicated by the symbol P(outcome), where X, Y, and Z are random variables representing the delay variation distributions of the sub-paths of the complete path (in this case, there are three sub-paths), and "a" is the quantile of interest.
注意，dy和dz表示上面的部分积分，y和z是积分变量。此外，结果的概率由符号P（结果）表示，其中X、Y和Z是表示完整路径的子路径的延迟变化分布的随机变量（在这种情况下，有三个子路径），“a”是感兴趣的分位数。
This relation can be used to compose a quantile of interest for the complete path from the sub-path delay distributions. The histograms with 1-ms bins are discrete approximations of the delay distributions.
该关系可用于从子路径延迟分布组成完整路径的感兴趣分位数。带有1-ms箱的直方图是延迟分布的离散近似值。
Type-P-One-way-Composite-pdv-refmin-NPA for the complete source to destination path can be calculated by combining the statistics of all the constituent sub-paths in the process described in [Y.1541], Clause 8 and Appendix X.
可通过组合[Y.1541]、第8条和附录X中所述过程中所有组成子路径的统计信息，计算完整源到目标路径的类型-P-单向-Composite-pdv-refmin-NPA。
The delay distribution of a sufficiently large stream of packets measured on each sub-path during the interval [T, Tf] will be sufficiently stationary, and the sub-path distributions themselves are sufficiently independent, so that summary information describing the sub-path distributions can be combined to estimate the delay distribution of the complete path.
在间隔[T，Tf]期间在每个子路径上测量的足够大的分组流的延迟分布将足够稳定，并且子路径分布本身足够独立，因此，可以组合描述子路径分布的摘要信息来估计完整路径的延迟分布。
It is assumed that the one-way delay distributions of the sub-paths and the complete path are continuous.
假设子路径和完整路径的单向延迟分布是连续的。
See the common section (Section 3).
见通用部分（第3节）。
In addition to the common deviations, a few additional sources exist here. For one, very tight distributions with ranges on the order of a few milliseconds are not accurately represented by a histogram with 1-ms bins. This size was chosen assuming an implicit requirement on accuracy: errors of a few milliseconds are acceptable when assessing a composed distribution quantile.
除了常见的偏差外，这里还有一些其他来源。首先，范围为几毫秒的非常紧密的分布不能用带有1-ms箱的直方图精确表示。选择该尺寸是假设对精度的隐含要求：在评估合成分布分位数时，几毫秒的误差是可以接受的。
Also, summary statistics cannot describe the subtleties of an empirical distribution exactly, especially when the distribution is very different from a classical form. Any procedure that uses these statistics alone may incur error.
此外，汇总统计无法准确描述经验分布的微妙之处，特别是当分布与经典形式非常不同时。任何单独使用这些统计信息的过程都可能会出错。
If the delay distributions of the sub-paths are somehow correlated, then neither of these composition functions will be reliable estimators of the complete path distribution.
如果子路径的延迟分布以某种方式相关，那么这些组合函数都不是完整路径分布的可靠估计量。
In practice, sub-path delay distributions with extreme outliers have increased the error of the composed metric estimate.
在实践中，具有极端异常值的子路径延迟分布增加了合成度量估计的误差。
See the common section (Section 3).
见通用部分（第3节）。
This metric requires a stream of packets sent from one host (source) to another host (destination) through intervening networks. This method could be abused for denial-of-service attacks directed at the destination and/or the intervening network(s).
此度量要求通过中间网络从一个主机（源）发送到另一个主机（目的地）的数据包流。此方法可能被滥用，用于针对目标和/或介入网络的拒绝服务攻击。
Administrators of source, destination, and intervening networks should establish bilateral or multilateral agreements regarding the timing, size, and frequency of collection of sample metrics. Use of this method in excess of the terms agreed upon between the participants may be cause for immediate rejection or discarding of packets, or other escalation procedures defined between the affected parties.
来源、目的地和干预网络的管理员应就样本指标收集的时间、规模和频率建立双边或多边协议。使用此方法超过参与者之间商定的条款可能会导致立即拒绝或丢弃数据包，或受影响方之间定义的其他升级程序。
Active use of this method generates packets for a sample, rather than taking samples based on user data, and does not threaten user data confidentiality. Passive measurement MUST restrict attention to the headers of interest. Since user payloads may be temporarily stored for length analysis, suitable precautions MUST be taken to keep this information safe and confidential. In most cases, a hashing function will produce a value suitable for payload comparisons.
主动使用此方法会为样本生成数据包，而不是基于用户数据采集样本，并且不会威胁用户数据的机密性。被动测量必须将注意力限制在感兴趣的标题上。由于用户有效载荷可能会临时存储以进行长度分析，因此必须采取适当的预防措施以确保该信息的安全和保密。在大多数情况下，哈希函数将生成适合于负载比较的值。
It may be possible to identify that a certain packet or stream of packets is part of a sample. With that knowledge at the destination and/or the intervening networks, it is possible to change the
可以识别特定分组或分组流是样本的一部分。在目的地和/或介入网络处有了这些知识，就有可能改变网络
processing of the packets (e.g., increasing or decreasing delay), which may distort the measured performance. It may also be possible to generate additional packets that appear to be part of the sample metric. These additional packets are likely to perturb the results of the sample measurement.
数据包的处理（例如，增加或减少延迟），这可能会扭曲测量的性能。还可以生成似乎是样本度量的一部分的附加数据包。这些额外的数据包可能会干扰样本测量的结果。
To discourage the kind of interference mentioned above, packet interference checks, such as cryptographic hash, may be used.
为了阻止上述类型的干扰，可以使用分组干扰检查，例如加密散列。
Metrics defined in the IETF are typically registered in the IANA IPPM Metrics Registry as described in the initial version of the registry [RFC4148].
IETF中定义的度量通常在IANA IPPM度量注册表中注册，如注册表初始版本[RFC4148]中所述。
IANA has registered the following metrics in the IANA-IPPM-METRICS-REGISTRY-MIB:
IANA已在IANA-IPPM-metrics-REGISTRY-MIB中注册了以下指标：
ietfFiniteOneWayDelayStream OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-One-way-Delay-Stream"
REFERENCE "RFC 6049, Section 4.1."
::= { ianaIppmMetrics 71 }
ietfFiniteOneWayDelayStream OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-One-way-Delay-Stream"
REFERENCE "RFC 6049, Section 4.1."
::= { ianaIppmMetrics 71 }
ietfFiniteOneWayDelayMean OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-One-way-Delay-Mean"
REFERENCE "RFC 6049, Section 4.2."
::= { ianaIppmMetrics 72 }
ietfFiniteOneWayDelayMean OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-One-way-Delay-Mean"
REFERENCE "RFC 6049, Section 4.2."
::= { ianaIppmMetrics 72 }
ietfCompositeOneWayDelayMean OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-Composite-One-way-Delay-Mean"
REFERENCE "RFC 6049, Section 4.2.5."
::= { ianaIppmMetrics 73 }
ietfCompositeOneWayDelayMean OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-Composite-One-way-Delay-Mean"
REFERENCE "RFC 6049, Section 4.2.5."
::= { ianaIppmMetrics 73 }
ietfFiniteOneWayDelayMinimum OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-One-way-Delay-Minimum"
REFERENCE "RFC 6049, Section 4.3.2."
::= { ianaIppmMetrics 74 }
ietfFiniteOneWayDelayMinimum OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-One-way-Delay-Minimum"
REFERENCE "RFC 6049, Section 4.3.2."
::= { ianaIppmMetrics 74 }
ietfCompositeOneWayDelayMinimum OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-Composite-One-way-Delay-Minimum"
REFERENCE "RFC 6049, Section 4.3."
::= { ianaIppmMetrics 75 }
ietfCompositeOneWayDelayMinimum OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Finite-Composite-One-way-Delay-Minimum"
REFERENCE "RFC 6049, Section 4.3."
::= { ianaIppmMetrics 75 }
ietfOneWayPktLossEmpiricProb OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-Packet-Loss-Empirical-Probability"
REFERENCE "RFC 6049, Section 5.1.4"
::= { ianaIppmMetrics 76 }
ietfOneWayPktLossEmpiricProb OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-Packet-Loss-Empirical-Probability"
REFERENCE "RFC 6049, Section 5.1.4"
::= { ianaIppmMetrics 76 }
ietfCompositeOneWayPktLossEmpiricProb OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Composite-One-way-Packet-Loss-Empirical-Probability"
REFERENCE "RFC 6049, Section 5.1."
::= { ianaIppmMetrics 77 }
ietfCompositeOneWayPktLossEmpiricProb OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Composite-One-way-Packet-Loss-Empirical-Probability"
REFERENCE "RFC 6049, Section 5.1."
::= { ianaIppmMetrics 77 }
ietfOneWayPdvRefminStream OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Stream"
REFERENCE "RFC 6049, Section 6.1."
::= { ianaIppmMetrics 78 }
ietfOneWayPdvRefminStream OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Stream"
REFERENCE "RFC 6049, Section 6.1."
::= { ianaIppmMetrics 78 }
ietfOneWayPdvRefminMean OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Mean"
REFERENCE "RFC 6049, Section 6.1.4."
::= { ianaIppmMetrics 79 }
ietfOneWayPdvRefminMean OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Mean"
REFERENCE "RFC 6049, Section 6.1.4."
::= { ianaIppmMetrics 79 }
ietfOneWayPdvRefminVariance OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Variance"
REFERENCE "RFC 6049, Section 6.1.4."
::= { ianaIppmMetrics 80 }
ietfOneWayPdvRefminVariance OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Variance"
REFERENCE "RFC 6049, Section 6.1.4."
::= { ianaIppmMetrics 80 }
ietfOneWayPdvRefminSkewness OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Skewness"
REFERENCE "RFC 6049, Section 6.1.4."
::= { ianaIppmMetrics 81 }
ietfOneWayPdvRefminSkewness OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-pdv-refmin-Skewness"
REFERENCE "RFC 6049, Section 6.1.4."
::= { ianaIppmMetrics 81 }
ietfCompositeOneWayPdvRefminQtil OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Composite-One-way-pdv-refmin-quantile-a"
REFERENCE "RFC 6049, Section 6.1.5.1."
::= { ianaIppmMetrics 82 }
ietfCompositeOneWayPdvRefminQtil OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-Composite-One-way-pdv-refmin-quantile-a"
REFERENCE "RFC 6049, Section 6.1.5.1."
::= { ianaIppmMetrics 82 }
ietfCompositeOneWayPdvRefminNPA OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-Composite-pdv-refmin-NPA"
REFERENCE "RFC 6049, Section 6.1.5.2."
::= { ianaIppmMetrics 83 }
ietfCompositeOneWayPdvRefminNPA OBJECT-IDENTITY
STATUS current
DESCRIPTION
"Type-P-One-way-Composite-pdv-refmin-NPA"
REFERENCE "RFC 6049, Section 6.1.5.2."
::= { ianaIppmMetrics 83 }
The following people have contributed useful ideas, suggestions, or the text of sections that have been incorporated into this memo:
以下人员提供了有用的想法、建议或纳入本备忘录的章节文本：
- Phil Chimento <vze275m9@verizon.net>
- 菲尔·奇门托<vze275m9@verizon.net>
- Reza Fardid <RFardid@cariden.com>
- 雷扎·法尔多<RFardid@cariden.com>
- Roman Krzanowski <roman.krzanowski@verizon.com>
- 罗曼·克扎诺夫斯基<罗曼。krzanowski@verizon.com>
- Maurizio Molina <maurizio.molina@dante.org.uk>
- 毛里齐奥·莫利纳<毛里齐奥。molina@dante.org.uk>
- Lei Liang <L.Liang@surrey.ac.uk>
- 雷亮。Liang@surrey.ac.uk>
- Dave Hoeflin <dhoeflin@att.com>
- 戴夫·霍夫林<dhoeflin@att.com>
A long time ago, in a galaxy far, far away (Minneapolis), Will Leland suggested the simple and elegant Type-P-Finite-One-way-Delay concept. Thanks Will.
很久以前，在一个遥远的星系（明尼阿波利斯），威尔·利兰提出了简单而优雅的P型有限单向延迟概念。谢谢你，威尔。
Yaakov Stein and Donald McLachlan also provided useful comments along the way.
Yaakov Stein和Donald McLachlan也提供了有用的意见。
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, March 1997.
[RFC2119]Bradner，S.，“RFC中用于表示需求水平的关键词”，BCP 14，RFC 2119，1997年3月。
[RFC2330] Paxson, V., Almes, G., Mahdavi, J., and M. Mathis, "Framework for IP Performance Metrics", RFC 2330, May 1998.
[RFC2330]Paxson，V.，Almes，G.，Mahdavi，J.，和M.Mathis，“IP性能度量框架”，RFC 2330，1998年5月。
[RFC2679] Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way Delay Metric for IPPM", RFC 2679, September 1999.
[RFC2679]Almes，G.，Kalidini，S.，和M.Zekauskas，“IPPM的单向延迟度量”，RFC 2679，1999年9月。
[RFC2680] Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way Packet Loss Metric for IPPM", RFC 2680, September 1999.
[RFC2680]Almes，G.，Kalidini，S.，和M.Zekauskas，“IPPM的单向数据包丢失度量”，RFC 2680，1999年9月。
[RFC3393] Demichelis, C. and P. Chimento, "IP Packet Delay Variation Metric for IP Performance Metrics (IPPM)", RFC 3393, November 2002.
[RFC3393]Demichelis，C.和P.Chimento，“IP性能度量的IP数据包延迟变化度量（IPPM）”，RFC 3393，2002年11月。
[RFC3432] Raisanen, V., Grotefeld, G., and A. Morton, "Network performance measurement with periodic streams", RFC 3432, November 2002.
[RFC3432]Raisanen，V.，Grotefeld，G.，和A.Morton，“周期流的网络性能测量”，RFC 3432，2002年11月。
[RFC4148] Stephan, E., "IP Performance Metrics (IPPM) Metrics Registry", BCP 108, RFC 4148, August 2005.
[RFC4148]Stephan，E.“IP性能度量（IPPM）度量注册表”，BCP 108，RFC 4148，2005年8月。
[RFC5835] Morton, A. and S. Van den Berghe, "Framework for Metric Composition", RFC 5835, April 2010.
[RFC5835]Morton，A.和S.Van den Berghe，“公制组合框架”，RFC 58352010年4月。
[RFC5474] Duffield, N., Chiou, D., Claise, B., Greenberg, A., Grossglauser, M., and J. Rexford, "A Framework for Packet Selection and Reporting", RFC 5474, March 2009.
[RFC5474]N.Duffield、Chiou、D.Claise、B.Greenberg、A.Grossglauser、M.和J.Rexford，“数据包选择和报告框架”，RFC 54742009年3月。
[RFC5481] Morton, A. and B. Claise, "Packet Delay Variation Applicability Statement", RFC 5481, March 2009.
[RFC5481]Morton，A.和B.Claise，“数据包延迟变化适用性声明”，RFC 54812009年3月。
[RFC5644] Stephan, E., Liang, L., and A. Morton, "IP Performance Metrics (IPPM): Spatial and Multicast", RFC 5644, October 2009.
[RFC5644]Stephan，E.，Liang，L.，和A.Morton，“IP性能度量（IPPM）：空间和多播”，RFC 56442009年10月。
[STATS] Mood, A., Graybill, F., and D. Boes, "Introduction to the Theory of Statistics, 3rd Edition", McGraw-Hill, New York, NY, 1974.
[统计]Mood，A.，Graybill，F.，和D.Boes，“统计理论导论，第三版”，McGraw Hill，纽约，纽约，1974年。
[Y.1540] ITU-T Recommendation Y.1540, "Internet protocol data communication service - IP packet transfer and availability performance parameters", November 2007.
[Y.1540]ITU-T建议Y.1540，“互联网协议数据通信服务-IP数据包传输和可用性性能参数”，2007年11月。
[Y.1541] ITU-T Recommendation Y.1541, "Network Performance Objectives for IP-based Services", February 2006.
[Y.1541]ITU-T建议Y.1541，“基于IP的服务的网络性能目标”，2006年2月。
Authors' Addresses
作者地址
Al Morton AT&T Labs 200 Laurel Avenue South Middletown, NJ 07748 USA
美国新泽西州劳雷尔大道南米德尔顿200号阿尔莫顿AT&T实验室，邮编：07748
Phone: +1 732 420 1571
Fax: +1 732 368 1192
EMail: acmorton@att.com
URI: http://home.comcast.net/~acmacm/
Phone: +1 732 420 1571
Fax: +1 732 368 1192
EMail: acmorton@att.com
URI: http://home.comcast.net/~acmacm/
Stephan Emile France Telecom Orange 2 avenue Pierre Marzin Lannion, F-22307 France
斯蒂芬·埃米尔法国电信橙色2大道皮埃尔·马津·拉尼翁，F-22307法国
EMail: emile.stephan@orange-ftgroup.com
EMail: emile.stephan@orange-ftgroup.com