RFC1857 日本語訳
1857 A Model for Common Operational Statistics. M. Lambert. October 1995. (Format: TXT=55314 bytes) (Obsoletes RFC1404) (Status: INFORMATIONAL)
プログラムでの自動翻訳です。
RFC一覧
英語原文
Network Working Group M. Lambert Request For Comments: 1857 Pittsburgh Supercomputing Center Obsoletes: 1404 October 1995 Category: Informational
コメントを求めるワーキンググループM.ランバート要求をネットワークでつないでください: 1857ピッツバーグスーパーコンピューティングセンターは以下を時代遅れにします。 1404 1995年10月のカテゴリ: 情報
A Model for Common Operational Statistics
一般的な操作上の統計のためのモデル
Status of this Memo
このMemoの状態
This memo provides information for the Internet community. This memo does not specify an Internet standard of any kind. Distribution of this memo is unlimited.
このメモはインターネットコミュニティのための情報を提供します。 このメモはどんな種類のインターネット標準も指定しません。 このメモの分配は無制限です。
Abstract
要約
This memo describes a model for operational statistics in the Internet. It gives recommendations for metrics, measurements, polling periods and presentation formats and defines a format for the exchange of operational statistics.
このメモはインターネットでの操作上の統計のためにモデルについて説明します。 それは、測定基準、測定値、世論調査の期間、およびプレゼンテーション形式のために推薦を与えて、操作上の統計の交換のために書式を定義します。
Acknowledgements
承認
The author would like to thank the members of the Operational Statistics Working Group of the IETF whose efforts made this memo possible, particularly Bernhard Stockman, author of RFC 1404, and Nevil Brownlee, who produced the revised BNF description of the model. Wherever possible, their text has been changed as little as feasible.
作者はモデルの改訂されたBNF記述を起こした取り組みがこのメモを可能にしたIETFのOperational Statistics作業部会、特にバーンハードStockman、RFC1404の作者、およびネヴィル・ブラウンリーのメンバーに感謝したがっています。 どこでも、可能であるところでは、それらのテキストが可能であるとして同じくらい少ししか変えられていません。
Table of Contents
目次
1. Introduction ............................................. 2 2. The Model ................................................ 5 2.1 Metrics and Polling Periods .............................. 5 2.2 Format for Storing Collected Data ........................ 6 2.3 Reports .................................................. 6 2.4 Security Issues .......................................... 6 3. Categorization of Metrics ................................ 7 3.1 Overview ................................................. 7 3.2 Categorization of Metrics Based on Measurement Areas ..... 7 3.2.1 Utilization Metrics ...................................... 7 3.2.2 Performance Metrics ...................................... 7 3.2.3 Availability Metrics ..................................... 8 3.2.4 Stability Metrics ........................................ 8 3.3 Categorization Based on Availability of Metrics .......... 8 3.3.1 Per Interface Variables Already in Standard MIB .......... 8 3.3.2 Per Interface Variables in Private Enterprise MIB ........ 9 3.3.3 Per interface Variables Needing High Resolution Polling .. 9
1. 序論… 2 2. モデル… 5 2.1の測定基準と世論調査の期間… 5 2.2 保存のための形式はデータを集めました… 6 2.3 報告します… 6 2.4 セキュリティ問題… 6 3. 測定基準の分類… 7 3.1概要… 7 測定基準の3.2分類は領域を測定に基礎づけました… 7 3.2 .1 利用測定基準… 7 3.2 .2 パフォーマンス測定基準… 7 3.2 .3 有用性測定基準… 8 3.2 .4 安定性測定基準… 8 3.3分類は測定基準の有用性を基礎づけました… 8 3.3 .1 既に標準のMIBのインタフェース変数単位で… 8 3.3 .2 私企業MIBのインタフェース変数単位で… 9 3.3 .3 High Resolution Pollingを必要とするインタフェースVariables単位で。 9
Lambert Informational [Page 1] RFC 1857 Operational Statistics October 1995
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3.3.4 Per Interface Variables not in any MIB ................... 9 3.3.5 Per Node Variables ....................................... 9 3.3.6 Metrics not being Retrievable with SNMP ................. 10 3.4 Recommended Metrics ..................................... 10 4. Polling Frequencies ..................................... 10 4.1 Variables Needing High Resolution Polling ............... 11 4.2 Variables not Needing High Resolution Polling ........... 11 5. Pre-Processing of Raw Statistical Data .................. 11 5.1 Optimizing and Concentrating Data to Resources .......... 11 5.2 Aggregation of Data ..................................... 12 6. Storing of Statistical Data ............................. 12 6.1 The Storage Format ...................................... 13 6.1.1 The Label Section ....................................... 14 6.1.2 The Device Section ...................................... 15 6.1.3 The Data Section ........................................ 17 6.2 Storage Requirement Estimations ......................... 17 7. Report Formats .......................................... 18 7.1 Report Types and Contents ............................... 18 7.2 Contents of the Reports ................................. 19 7.2.1 Offered Load by Link .................................... 19 7.2.2 Offered Load by Customer ................................ 19 7.2.3 Resource Utilization Reporting .......................... 20 7.2.3.1 Utilization as Maximum Peak Behavior .................... 20 7.2.3.2 Utilization as Frequency Distribution of Peaks .......... 20 8. Considerations for Future Development ................... 20 8.1 A Client/Server Based Statistical Exchange System ....... 21 8.2 Inclusion of Variables not in the Internet Standard MIB . 21 8.3 Detailed Resource Utilization Statistics ................ 21 Appendix A Some formulas for statistical aggregation ........... 22 Appendix B An example .......................................... 24 Security Considerations ......................................... 27 Author's Address ................................................ 27
3.3.4 どんなコネではなく、Interface VariablesあたりのMIBも… 9 3.3 .5 ノード変数単位で… 9 3.3 SNMPとRetrievableでない.6の測定基準… 10 3.4 測定基準を推薦します… 10 4. 世論調査頻度… 10 高画質世論調査を必要とする4.1の変数… 11 高画質世論調査を必要としない4.2の変数… 11 5. 生の統計データの前処理… 11 5.1 データをリソースに最適化して、集結します… 11 5.2 データの集合… 12 6. 統計データの保存… 12 6.1 ストレージ形式… 13 6.1 .1 ラベル部分… 14 6.1 .2 デバイス部分… 15 6.1 .3 資料課… 17 6.2 ストレージ要件見積り… 17 7. 書式を報告してください… 18 7.1 タイプとコンテンツを報告してください… 18 7.2 レポートのコンテンツ… 19 7.2 .1はリンクのそばで負荷を提供しました… 19 7.2 .2は顧客で負荷を提供しました… 19 7.2 .3 リソース利用報告… 20 7.2 .3 最大のピークの振舞いとしての.1利用… 20 7.2 .3 ピークの度数分布としての.2利用… 20 8. 今後の開発のための問題… 20 8.1 クライアント/サーバは統計的な交換システムを基礎づけました… 21 8.2 変数の包含はインターネット標準のMIB. 21 8.3でリソース利用統計を詳しく述べませんでした… 21 統計的な集合のための付録A Some定石… 22付録B Anの例… 24 セキュリティ問題… 27作者のアドレス… 27
1. Introduction
1. 序論
Many network administrations commonly collect and archive network management metrics that indicate network utilization, growth and reliability. The primary goals of this activity are to facilitate near-term problem isolation and longer-term network planning within the organization. There is also the broader goal of cooperative problem isolation and network planning among network administrations. This broader goal is likely to become increasingly important as the Internet continues to grow, particularly as the number of Internet service providers expands and the quality of service between providers becomes more of a concern.
多くのネットワーク運営が、一般的にネットワーク利用、成長、および信頼性を示すネットワークマネージメント測定基準を、集めて、格納します。 この活動のプライマリ目標は組織の中で短期間問題分離と、より長い期間ネットワーク計画を容易にすることです。 また、ネットワーク運営の中に協力的な問題分離とネットワーク計画の、より広い目標があります。 インターネットが、成長し続けているのに応じて、このより広い目標はますます重要になりそうです、特に、インターネット接続サービス業者の数が広がって、プロバイダーの間のサービスの質が一層の関心になるように。
Lambert Informational [Page 2] RFC 1857 Operational Statistics October 1995
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There exist a variety of network management tools for the collection and presentation of network management metrics. However, different kinds of measurement and presentation techniques make it difficult to compare data among networks. In addition, there is not general agreement on what metrics should be regularly collected or how they should be displayed.
ネットワークマネージメント測定基準の収集とプレゼンテーションのためのさまざまなネットワークマネージメントツールが存在しています。 しかしながら、異種の測定とプレゼンテーションのテクニックで、ネットワークの中でデータを比較するのは難しくなります。 さらに、定期的にどんな測定基準を集めるべきであるか、そして、またはどうそれらを表示するべきであるかの一般協定がありません。
There needs to be an agreed-upon model for
同意しているモデルは、ある必要があります。
1) A minimal set of common network management metrics to satisfy
the goals stated above,
1) 上に述べられた目標を満たす1人の極小集合の一般的なネットワークマネージメント測定基準
2) Tools for collecting these metrics,
2) これらの測定基準を集めるためのツール
3) A common interchange format to facilitate the usage of these
data by common presentation tools and
3) そして一般的なプレゼンテーション・ツールでこれらのデータの用法を容易にする一般的な置き換え形式。
4) Common presentation formats.
4) 一般的なプレゼンテーション形式。
Under this Operational Statistics model, collection tools will collect and store data to be retrieved later in a given format by presentation tools displaying the data in a predefined way. (See figure below.)
このOperational Statisticsモデルの下では、収集ツールは、後で事前に定義された方法でデータを表示するプレゼンテーション・ツールによって与えられた形式で検索されるためにデータを集めて、保存するでしょう。 (以下の図を参照してください。)
Lambert Informational [Page 3] RFC 1857 Operational Statistics October 1995
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The Operational Statistics Model
操作上の統計モデル
(Collection of common metrics, by commonly available tools, stored in a common format, displayed in common formats by commonly available presentation tools.)
(一般的に利用可能なツールによる一般的な測定基準の一般的な形式で保存された収集は一般的に利用可能なプレゼンテーション・ツールによる一般的な形式で表示しました。)
!-----------------------!
! Network !
!---+---------------+---!
/ \
/ \
/ \
--------+------ ----+---------
! New ! ! Old !
! Collection ! ! Collection !
! Tool ! ! Tool !
!---------+---! !------+-----!
\ !
\ !-------+--------!
\ ! Post-Processor !
\ !--+-------------!
\ /
\ /
\ /
!--+-------+---!
! Common !
! Statistics !
! Database !
!-+--------+---!
/ \
/ \
/ \
/ !-+-------------!
/ ! Pre-Processor !
/ !-------+-------!
!-----------+--! !
! New ! !-------+-------!
! Presentation ! ! Old !
! Tool ! ! Presentation !
!---------+----! ! Tool !
\ !--+------------!
\ /
\ /
!-+---------------+-!
! Graphical Output !
! (e.g., to paper !
! or X Window) !
!-------------------!
!-----------------------! ! ネットワークでつないでください!---+---------------+---! / \ / \ / \ --------+------ ----+--------- ! 新しさ、老人!収集!収集!ツール!ツール!---------+---! !------+-----! \ ! \ !-------+--------! \!ポストプロセッサ!\!、--、+-------------! \ / \ / \ / !--+-------+---! ! 統計!データベース!コモン!-+--------+---! / \ / \ / \ / !-+-------------! /!プリプロセッサ/!-------+-------! !-----------+--新しく!-------+-------! ! プレゼンテーション!老人!ツール!プレゼンテーション!---------+----! ! ツール!\!、--、+------------! \ / \ / !-+---------------+! Graphical Output!、(紙!例えば、X windowへの!)-------------------!
Lambert Informational [Page 4] RFC 1857 Operational Statistics October 1995
操作上の[4ページ]RFC1857統計1995年10月の情報のランバート
This memo gives an overview of this model for common operational statistics. The model defines the gathering, storing and presentation of network operational statistics and classifies the types of information that should be available at each network operation center (NOC) conforming to this model.
このメモは一般的な操作上の統計のためにこのモデルの概要を与えます。 モデルは、ネットワークの操作上の統計の集会、保存、およびプレゼンテーションを定義して、このモデルに従いながらそれぞれのネットワークオペレーション・センター(NOC)で利用可能であるべき情報のタイプを分類します。
The model defines a minimal set of metrics and discusses how these metrics should be gathered and stored. It gives recommendations for the content and layout of statistical reports which make possible the easy comparison of network statistics among NOCs.
モデルは、1人の極小集合に関する測定基準を定義して、これらの測定基準がどう集められて、保存されるべきであるかについて議論します。 それはNOCsの中でネットワーク統計の簡単な比較を可能にする統計報告の内容とレイアウトのための推薦を与えます。
The primary purpose of this model is to define mechanisms by which NOCs could share most effectively their operational statistics. One intent of this model is to specify a baseline capability that NOCs conforming to the model may support with minimal development effort and minimal ongoing effort.
このモデルのプライマリ目的はNOCsが最も効果的に彼らの操作上の統計を共有できたメカニズムを定義することです。 このモデルの1つの意図はNOCsがモデルに従う場合最小量で開発努力と最小量の進行中の取り組みをサポートするかもしれない基線能力を指定することです。
2. The Model
2. モデル
The model defines three areas of interest on which all underlying concepts are based:
モデルはすべての基本的概念が基づいている興味がある3つの領域を定義します:
1) The definition of a minimal set of metrics to be gathered,
1) 1人の極小集合の集められるべき測定基準の定義
2) The definition of a format for storing collected statistical
data and
2) そして集まっている統計データを保存するための形式の定義。
3) The definition of methods and formats for generating reports.
3) 生成するメソッドと形式の定義は報告します。
The model indicates that old tools currently in use could be retrofitted into the new paradigm. This could be done by providing conversion filters between old and new tools. In this sense this model intends to advocate the development of freely redistributable software for use by participating NOCs.
モデルは、現在使用中の古いツールを新しい発想に改装できたのを示します。 古くて新しいツールの間に色温度変換フィルタを提供することによって、これができるでしょう。 この意味で、このモデルは参加しているNOCsによる使用のために自由に再配付可能なソフトウェアの開発を支持するつもりです。
One basic idea of the model is that statistical data stored at one place could be retrieved and displayed at some other place.
モデルの1つの基本的な考え方はある他の場所に1つの場所に保存された統計データを検索して、表示できたということです。
2.1. Metrics and Polling Periods
2.1. 測定基準と世論調査の期間
Here the value is 0.
ここで、値は0です。
The intent here is to define a minimal set of metrics that could be gathered easily using standard SNMP-based network management tools. Thus, these metrics should be available as variables in the Internet Standard MIB.
ここの意図は標準のSNMPベースのネットワークマネージメントツールを使用することで容易に集めることができた1人の極小集合に関する測定基準を定義することです。 したがって、これらの測定基準はインターネットStandard MIBの変数として利用可能であるべきです。
Lambert Informational [Page 5] RFC 1857 Operational Statistics October 1995
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If the Internet Standard MIB were changed, this minimal set of metrics should be reconsidered, as there are many metrics regarded as important, but not currently defined in the standard MIB. Some metrics which are highly desirable to collect are probably not retrievable using SNMP. Therefore, tools and methods for gathering such metrics should be defined explicitly if such metrics are to be considered. This is, however, outside of the scope of this memo.
インターネットStandard MIBを変えるなら、この極小集合に関する測定基準を再考するでしょうに、重要であると見なされますが、現在標準のMIBでは定義されていない多くの測定基準があるとき。 いくつかの集めるのにおいて非常に望ましい測定基準は、たぶんSNMPを使用することで回収可能ではありません。 したがって、そのような測定基準を集めるためのツールとメソッドはそのような測定基準が考えられることであるなら明らかに定義されるべきです。 しかしながら、これはこのメモの範囲の外にあります。
2.2. Format for Storing Collected Data
2.2. 集まっているデータを保存するための形式
A format for storing data is defined. The intent is to minimize redundant information by using a single header structure wherein all information relevant to a certain set of statistical data is stored. This header section will give information about when and where the corresponding statistical data were collected.
データを保存するための書式は定義されます。 意図はある統計データに関連しているすべての情報が保存されるただ一つのヘッダー構造を使用することによって余分な情報を最小にすることです。 このヘッダー部分は対応する統計データがいつ、どこであったかに関して集まった状態で知らせるでしょう。
2.3. Reports
2.3. レポート
Some basic classes of reports are suggested, addressing different views of network behavior. Reports of total octets and packets over some time period are regarded as essential to give an overall view of the traffic flow in a network. Differentiation between applications and protocols is regarded as needed to give ideas on which type of traffic is dominant. Reports on resource utilization are recommended.
ネットワークの振舞いの異なった視点を扱って、数人の基本的なクラスのレポートは示されます。 いつかの期間にわたる総八重奏とパケットのレポートはネットワークの交通の流れの全体図を与えるために不可欠であると見なされます。 アプリケーションとプロトコルの間の分化は、どのタイプのトラフィックが優位であるかに関する考えを与えるために必要に応じて見なされます。 リソース利用に関するレポートはお勧めです。
The time period which a report spans may vary depending on its intent. In engineering and operations daily or weekly reports may be sufficient, whereas for capacity planning there may be a need for longer-term reports.
意図によって、レポートがわたる期間は異なるかもしれません。 工学と毎日の操作か週報には、より長い期間レポートの必要は、十分であるかもしれませんが、キャパシティプランニングのためにあるかもしれません。
2.4. Security Issues
2.4. 安全保障問題
There are legal, ethical and political concerns about data sharing. People, in particular Network Service Providers, are concerned about showing data that may make one of their networks look bad.
データ共有に関する法的で、倫理的で政治上の心配があります。 人々(特にNetwork Service Providers)はそれらのネットワークに加わるかもしれないデータが悪く見えるのを示すことに関して心配しています。
For this reason there is a need to insure integrity, conformity and confidentiality of the shared data. To be useful, the same data should be collected from all involved sites and it should be collected at the same interval.
この理由で、共有データの保全、一致、および秘密性を保障する必要があります。 役に立って、同じデータであることはすべてのかかわったサイトから集まるべきです、そして、同じ間隔を置いて、それは集められるべきです。
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操作上の[6ページ]RFC1857統計1995年10月の情報のランバート
3. Categorization of Metrics
3. 測定基準の分類
3.1. Overview
3.1. 概要
This section gives a classification of metrics with regard to scope and ease of retrieval. A recommendation of a minimal set of metrics is given. This section also gives some hints on metrics to be considered for future inclusion when available in the network management environment. Finally some thoughts on storage requirements are presented.
このセクションは範囲と検索の容易さに関して測定基準の分類を与えます。 1人の極小集合に関する測定基準の推薦を与えます。 また、このセクションは、ネットワークマネージメント環境で利用可能であるときに、将来の包含のために考えられるために測定基準でいくつかのヒントを与えます。 ストレージ要件に関する最終的にいくつかの考えが提示されます。
3.2. Categorization of Metrics Based on Measurement Areas
3.2. 測定領域に基づく測定基準の分類
The metrics used in evaluating network traffic could be classified into (at least) four major categories:
ネットワークトラフィックを評価する際に使用される測定基準は(少なくとも)の4つの大範疇に分類できました:
o Utilization metrics
o Performance metrics
o Availability metrics
o Stability metrics
o 利用測定基準oパフォーマンス測定基準o Availability測定基準o Stability測定基準
3.2.1. Utilization Metrics
3.2.1. 利用測定基準
This category describes different aspects of the total traffic being forwarded through the network. Possible metrics include:
このカテゴリはネットワークを通して進められる総トラフィックの異なった局面について説明します。 可能な測定基準は:
o Total input and output packets and octets
o Various peak metrics
o Per protocol and per application metrics
o ピーク測定基準o Perプロトコルとアプリケーション測定基準あたりの総投入、出力パケット、および八重奏o Various
3.2.2. Performance Metrics
3.2.2. パフォーマンス測定基準
These metrics relate to quality of service issues such as delays and congestion situations. Possible metrics include:
これらの測定基準は遅れや混雑状況などのサービスの質問題に関連します。 可能な測定基準は:
o RTT metrics on different protocol layers
o Number of collisions on a bus network
o Number of ICMP Source Quench messages
o Number of packets dropped
o パケットのICMP Source Quenchメッセージo NumberのNumberが下げたバスネットワークoにおける衝突の異なったプロトコル層o NumberにおけるRTT測定基準
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3.2.3. Availability Metrics
3.2.3. 有用性測定基準
These metrics could be viewed as gauging long term accessibility on different protocol layers. Possible metrics include:
異なったプロトコル層で長期のアクセシビリティを測るとこれらの測定基準を見なすことができました。 可能な測定基準は:
o Line availability as percentage uptime
o Route availability
o Application availability
o 割合動作可能時間のo Route有用性o Applicationの有用性としての線の有用性
3.2.4. Stability Metrics
3.2.4. 安定性測定基準
These metrics describe short-term fluctuations in the network which degrade the service level. Changes in traffic patterns also could be recognized using these metrics. Possible metrics include:
これらの測定基準はネットワークのサービスレベルを下げる一時的な変動について説明します。 これらの測定基準を使用することでトラフィック・パターンにおける変化も認識できるでしょう。 可能な測定基準は:
o Number of fast line status transitions
o Number of fast route changes (also known as route flapping)
o Number of routes per interface in the tables
o Next hop count stability
o Short term ICMP behavior
o 速いルートのNumberがNextが飛び越すテーブルoでルートの1インタフェースあたりのo Numberに変える(また、ルートのばたつくとして、知られています)ファスト・ライン状態変遷oの数は安定性o Short用語ICMPの振舞いを数えます。
3.3. Categorization Based on Availability of Metrics
3.3. 測定基準の有用性に基づく分類
To be able to retrieve metrics, the corresponding variables must be accessible at every network object which is part of the management domain for which statistics are being collected.
測定基準、対応する変数を検索できるのは統計が集められている管理ドメインの一部であるあらゆるネットワークオブジェクトでアクセスしやすいに違いありません。
Some metrics are easily retrievable because they are defined as variables in the Internet Standard MIB. Other metrics may be retrievable because they are part of some vendor's private enterprise MIB subtree. Finally, some metrics are considered irretrievable, either because they are not possible to include in the SNMP concept or because their measurement would require extensive polling (loading the network with management traffic).
それらがインターネットStandard MIBの変数と定義されるので、いくつかの測定基準が容易に回収可能です。 それらが、あるベンダーの私企業MIB下位木の一部であるので、他の測定基準は回収可能であるかもしれません。 最終的に、いくつかの測定基準が取り戻すことができないと考えられます、それらがSNMP概念に含むのにおいて可能でないか、または彼らの測定は大規模な世論調査を必要とするでしょう(管理トラフィックをネットワークに積んで)、したがって。
The metrics categorized below could each be judged as important in evaluating network behavior. This list may serve as a basis for revisiting the decisions on which metrics are to be regarded as reasonable and desirable to collect. If the availability of the metrics listed below changes, these decisions may change.
ネットワークの振舞いを評価するのにおいて重要であるとしてそれぞれ以下で分類された測定基準は判断できました。 このリストは集まる妥当で望ましいと見なされる測定基準がことである決定を再訪させる基礎として機能するかもしれません。 測定基準の有用性が変化の下にリストアップされたなら、これらの決定は変化するかもしれません。
3.3.1. Per Interface Variables Already in Internet Standard MIB (thus
easy to retrieve)
3.3.1. 既にインターネットの標準のMIBのインタフェース変数単位で(その結果、検索する簡単)です。
ifInUcastPkts (unicast packets in)
ifOutUcastPkts (unicast packets out)
ifInNUcastPkts (non-unicast packets in
ifOutNUcastPkts (non-unicast packets out)
ifInUcastPkts(中にユニキャストパケットがある状態で)ifOutUcastPkts(ユニキャストパケットが外にある状態で)ifInNUcastPkts、(ifOutNUcastPktsの非ユニキャストパケット(非ユニキャストパケットが外にある状態で)
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ifInOctets (octets in)
ifOutOctets (octets out)
ifOperStatus (line status)
ifInOctets(中に八重奏がある状態で)ifOutOctets(八重奏が外にある状態で)ifOperStatus(系列状態)
3.3.2. Per Interface Variables in Internet Private Enterprise MIB (thus
could sometimes be retrievable)
3.3.2. インターネット私企業MIBのインタフェース変数単位で(その結果、時々回収可能であるかもしれません)
discarded packets in
discarded packets out
congestion events in
congestion events out
aggregate errors
interface resets
集合誤りからの混雑イベントにおける捨てられたパケットアウト混雑イベントにおける捨てられたパケットはリセットを連結します。
3.3.3. Per Interface Variables Needing High Resolution Polling (which
is hard due to resulting network load)
3.3.3. 高画質世論調査を必要とするインタフェース変数単位で(結果として起こるネットワーク負荷のために困難です)
interface queue length
seconds missing stats
interface unavailable
route changes
interface next hop count
入手できないルートが変える統計インタフェースを逃すインタフェース待ち行列長さの秒が次のホップカウントを連結します。
3.3.4. Per Interface Variables not in any Known MIB (thus impossible
to retrieve using SNMP but possible to include in a MIB)
3.3.4. どんなKnown MIBでないところのInterface Variables単位でも(その結果、SNMPを使用することで検索する不可能ですが、MIBに含むのにおいて可能)です。
link layer packets in
link layer packets out
link layer octets in
link layer octets out
packet interarrival times
packet size distribution
パケットinterarrival回のパケットサイズ分布からのリンクレイヤ八重奏におけるリンクレイヤパケットアウトリンクレイヤ八重奏におけるリンクレイヤパケット
3.3.5. Per Node Variables (not categorized here)
3.3.5. ノード変数単位で(ここで、分類されません)
per-protocol packets in
per-protocol packets out
per-protocol octets in
per-protocol octets out
packets discarded in
packets discarded out
packet size distribution
system uptime
poll delta time
reboot count
パケットサイズ分布システム・アップタイムの投票デルタ時間リブートから捨てられたパケットで捨てられたパケットからの1プロトコルあたりの八重奏における1プロトコルあたりの八重奏からの1プロトコルあたりのパケットの1プロトコルあたりのパケットは数えられます。
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3.3.6. Metrics not Retrievable with SNMP
3.3.6. SNMPと共に回収可能でない測定基準
delays (RTTs) on different protocol layers
application layer availabilities
peak behavior metrics
異なったプロトコル層応用層の有用性に関する遅れ(RTTs)は振舞い測定基準に最大限にします。
3.4. Recommended Metrics
3.4. お勧めの測定基準
A large number of metrics could be considered for collection in the process of doing network statistics. To facilitate general consensus for this model, there is a need to define a minimal set of metrics that are both essential and retrievable in a majority of today's network objects. General retrievability is equated with presence in the Internet Standard MIB.
しているネットワーク統計の途中に収集のために多くの測定基準を考えることができました。 このモデルに関する全体的な合意を容易にするために、1人の極小集合の不可欠の、そして、かつ今日のネットワークオブジェクトの大部分で回収可能な測定基準を定義する必要があります。 一般再取り出し可能性はインターネットStandard MIBで存在と同一視されます。
The following metrics from the Internet Standard MIB were chosen as being desirable and reasonable:
インターネットStandard MIBからの以下の測定基準は望ましくて、妥当であるとして選ばれました:
For each interface:
それぞれに関しては、連結してください:
ifInOctets (octets in)
ifOutOctets (octets out)
ifInUcastPkts (unicast packets in)
ifOutUcastPkts (unicast packets out)
ifInNUcastPkts (non-unicast packets in)
ifOutNUcastPkts (non-unicast packets out)
ifInDiscards (in discards)
ifOutDiscards (out discards)
ifOperStatus (line status)
ifInOctets(中に八重奏がある状態で)ifOutOctets(八重奏が外にある状態で)ifInUcastPkts(中にユニキャストパケットがある状態で)ifOutUcastPkts(ユニキャストパケットが外にある状態で)ifInNUcastPkts(中に非ユニキャストパケットがある状態で)ifOutNUcastPkts(非ユニキャストパケットが外にある状態で)ifInDiscards(破棄における)ifOutDiscards(出ている破棄)ifOperStatus(系列状態)
For each node:
各ノードのために:
ipForwDatagrams (IP forwards)
ipInDiscards (IP in discards)
sysUpTime (system uptime)
ipForwDatagrams(IPフォワード)ipInDiscards(破棄におけるIP)sysUpTime(システム・アップタイム)
4. Polling Frequencies
4. 世論調査頻度
The purpose of polling at specified intervals is to gather statistics to serve as a basis for trend and capacity planning. From the operational data it should be possible to derive engineering and management data. It should be noted that all polling and retention values given below are recommendations and are not mandatory.
指定された間隔で、世論調査の目的は傾向とキャパシティプランニングの基礎として機能するように統計を集めることです。 操作上のデータから、工学と管理データを引き出すのは可能であるべきです。 すべての世論調査と以下に与えられた保持値が推薦であり、義務的でないことに注意されるべきです。
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4.1. Variables Needing High Resolution Polling
4.1. 高画質世論調査を必要とする変数
To be able to detect peak behavior, it is recommended that a period of 1 minute (60 seconds) at a maximum be used in gathering traffic data. The metrics to be collected at this frequency are:
ピークの振舞いを検出できるように、最大における1分(60秒)の期間が集会トラフィックデータで費やされるのは、お勧めです。 この頻度で集められるべき測定基準は以下の通りです。
for each interface
各インタフェースに
ifInOctets (octets in)
ifOutOctets (octets out)
ifInUcastPkts (unicast packets in)
ifOutUcastPkts (unicast packets out)
ifInOctets(中に八重奏がある状態で)ifOutOctets(八重奏が外にある状態で)ifInUcastPkts(中にユニキャストパケットがある状態で)ifOutUcastPkts(ユニキャストパケットが外にある状態で)
If it is not possible to gather data at this high polling frequency, it is recommended that an exact multiple of 60 seconds be used. The initial polling frequency value will be part of the stored statistical data as described in section 6.1.2 below.
この高い世論調査頻度で資料を取り集めるのが可能でないなら、60秒の正確な倍数が使用されるのは、お勧めです。 初期の世論調査頻度価値はセクション6.1.2未満で説明されるように保存された統計データの一部になるでしょう。
4.2. Variables not Needing High Resolution Polling
4.2. 高画質世論調査を必要としない変数
The remainder of the recommended variables to be gathered, i.e.,
すなわち集められるべきお勧めの変数の残り
For each interface:
それぞれに関しては、連結してください:
ifInNUcastPkts (non-unicast packets in)
ifOutNUcastPkts (non-unicast packets out)
ifInDiscards (in discards)
ifOutDiscards (out discards)
ifOperStatus (line status)
ifInNUcastPkts(中に非ユニキャストパケットがある状態で)ifOutNUcastPkts(非ユニキャストパケットが外にある状態で)ifInDiscards(破棄における)ifOutDiscards(出ている破棄)ifOperStatus(系列状態)
and for each node:
そして、各ノードのために:
ipForwDatagrams (IP forwards)
ipInDiscards (IP in discards)
sysUpTime (system uptime)
ipForwDatagrams(IPフォワード)ipInDiscards(破棄におけるIP)sysUpTime(システム・アップタイム)
could be collected at a lower polling rate. No polling rate is specified, but it is recommended that the period chosen be an exact multiple of 60 seconds.
低い世論調査率で集めることができました。 世論調査率は全く指定されませんが、選ばれた期間が60秒の正確な倍数であることはお勧めです。
5. Pre-Processing of Raw Statistical Data
5. 生の統計データの前処理
5.1. Optimizing and Concentrating Data to Resources
5.1. データをリソースに最適化して、集結します。
To avoid storing redundant data in what might be a shared file system, it is desirable to preprocess the raw data. For example, if a link is down there is no need to continuously store a counter which is not changing. The use of the variables sysUpTime and ifOperStatus
共有ファイルシステムであるかもしれないことにおける冗長データを保存するのを避けるために、生データを前処理するのは望ましいです。 例えば、リンクが下がっているなら、絶え間なく変化しないカウンタを保存する必要は全くありません。 変数のsysUpTimeとifOperStatusの使用
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makes it possible not to have to continuously store data collected from links and nodes where no traffic has been transmitted for some period of time.
絶え間なくトラフィックが全くいつかの期間の間に伝えられていないリンクとノードから集められたデータを保存している必要はないのを可能にします。
Another aspect of processing is to decouple the data from the raw interface being polled. The intent should be to convert such data into the resource of interest as, for example, the traffic on a given link. Changes of interface in a gateway for a given link should not be visible in the resulting data.
処理のもう一つの側面は投票される生のインタフェースからデータの衝撃を吸収することです。 意図は与えられたリンクの上に興味があるリソースへの例えば、トラフィックのようなデータを変換することであるべきです。 与えられたリンクへのゲートウェイのインタフェースの変化は結果として起こるデータで目に見えるべきではありません。
5.2. Aggregation of Data
5.2. データの集合
At many sites, the volume of data generated by a polling period of 1 minute will make aggregation of the stored data desirable if not necessary.
多くのサイトでは、1分の世論調査の期間までに生成されたデータ量で、記憶されたデータの集合は望ましいか必要になるでしょう。
Aggregation here refers to the replacement of data values on a number of time intervals by some function of the values over the union of the intervals. Either raw data or shorter-term aggregates may be aggregated. Note that aggregation reduces the amount of data, but also reduces the available information.
ここの集合は間隔の組合の上の値の何らかの関数で多くの時間間隔のデータ値の交換品について言及します。 未加工データか、より短い期間集合のどちらかが集められるかもしれません。 集合がデータ量を減少させますが、入手可能な情報をまた減らすことに注意してください。
In this model, the function used for the aggregation is either the arithmetic mean or the maximum, depending on whether it is desired to track the average or peak value of a variable.
このモデルでは、集合に使用される機能は、算術平均か最大のどちらかです、変数の平均かピーク値を追跡するのが必要であるかどうかよって。
Details of the layout of the aggregated entries in the data file are given in section 6.1.3.
データファイルにおける、集められたエントリーのレイアウトの詳細はセクション6.1.3で明らかにされます。
Suggestions for aggregation periods:
集合の期間の提案:
Over a
aの上で
24 hour period aggregate to 15 minutes,
1 month period aggregate to 1 hour,
1 year period aggregate to 1 day
24時間の15分までの期間の集合、1カ月の1時間までの期間の集合、1年間の1日までの期間の集合
6. Storing of Statistical Data
6. 統計データの保存
This section describes a format for the storage of statistical data. The goal is to facilitate a common set of tools for the gathering, storage and analysis of statistical data. The format is defined with the intent of minimizing redundant information and thus minimizing storage requirements. If a client server based model for retrieving remote statistical data were later developed, the specified storage format could be used as the transmission protocol.
このセクションは統計データのストレージのために形式について説明します。 目標は統計データの集会、ストレージ、および分析のために一般的なセットのツールを容易にすることです。 書式は余分な情報を最小にして、その結果、ストレージ要件を最小にする意図をもって定義されます。 リモート統計データを検索するためのクライアントサーバに基づいているモデルが後で開発されるなら、トランスミッションプロトコルとして指定されたストレージ形式を使用できるでしょうに。
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This model is intended to define an interchange file format, which would not necessarily be used for actual data storage. That means its goal is to provide complete, self-contained, portable files, rather than to describe a full database for storing them.
このモデルは置き換えファイル形式を定義するつもりです。(ファイル形式は実際のデータ保存に必ず使用されるというわけではないでしょう)。 それは、それらを保存するための完全なデータベースについて説明するより目標がむしろ完全で、自己充足的で、携帯用のファイルを提供することであることを意味します。
6.1. The Storage Format
6.1. The Storage Format
All white space (including tabs, line feeds and carriage returns) within a file is ignored. In addition all text from a # symbol to the following end of line (inclusive) is also ignored.
All white space (including tabs, line feeds and carriage returns) within a file is ignored. In addition all text from a # symbol to the following end of line (inclusive) is also ignored.
stat-data ::= <stat-section> [ <FS> <stat-section> ] stat-section ::= <device-section> | <label-section> | <data-section>
stat-data ::= <stat-section> [ <FS> <stat-section> ] stat-section ::= <device-section> | <label-section> | <data-section>
A data file must contain at least one device section and at least one label section. At least one data section must be associated with each label section. A device section must precede any data section which uses tags defined within it.
A data file must contain at least one device section and at least one label section. At least one data section must be associated with each label section. A device section must precede any data section which uses tags defined within it.
A data section may appear in the file (in which case it is called an internal data section and is preceded by a label section) or in another file (in which case it is called an external data section and is specified in an external label section). Such an external file may contain one and only one data section.
A data section may appear in the file (in which case it is called an internal data section and is preceded by a label section) or in another file (in which case it is called an external data section and is specified in an external label section). Such an external file may contain one and only one data section.
A label section indicates the start and finish times for its associated data section or sections, and a list of the names of the tags they contain. Within a data file there is an ordering of label sections. This depends only upon their relative position in the file. All internal data sections associated with the first label record must precede those associated with the second label record, and so on.
A label section indicates the start and finish times for its associated data section or sections, and a list of the names of the tags they contain. Within a data file there is an ordering of label sections. This depends only upon their relative position in the file. All internal data sections associated with the first label record must precede those associated with the second label record, and so on.
Here are some examples of valid data files:
Here are some examples of valid data files:
<label-s> <device-s> <data-s> <data-s>
<label-s> <device-s> <data-s> <data-s>
<label-s> <device-s> <data-s> <device-s> <data-s> <data-s>
<label-s> <device-s> <data-s> <device-s> <data-s> <data-s>
Both these files start with a label section giving the times and tag-name lists for the device and data sections which follow.
Both these files start with a label section giving the times and tag-name lists for the device and data sections which follow.
<dev-s> <label-s> <label-s> <label-s>
<dev-s> <label-s> <label-s> <label-s>
This file begins with a device section (which specifies tags used in its data sections) then has three 'external' label sections, each of which points to a separate data section. The data sections need not use all the tags defined in the device section; this is indicated by
This file begins with a device section (which specifies tags used in its data sections) then has three 'external' label sections, each of which points to a separate data section. The data sections need not use all the tags defined in the device section; this is indicated by
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the tag-name lists in their label sections.
the tag-name lists in their label sections.
<default-dev> <dev-1> <label-1> <dev-2> <label-2> ..
<default-dev> <dev-1> <label-1> <dev-2> <label-2> ..
In this example default-dev is a full device section, including a complete tag-table, with initial polling and aggregation periods specified for each variable in each variable-field. There is no label or data for default-dev--it is there purely to provide default tag-list information. Dev-1, dev-2, ... are device sections for a series of different devices. They each have their description fields (network-name, router-name, etc), but no tag-table. Instead they rely on using the tag-table from default-device. A default-dev record, if present, must be the first item in the data file. Label-1, label-2, etc. are label sections which point to files containing data sections for each device.
In this example default-dev is a full device section, including a complete tag-table, with initial polling and aggregation periods specified for each variable in each variable-field. There is no label or data for default-dev--it is there purely to provide default tag-list information. Dev-1, dev-2, ... are device sections for a series of different devices. They each have their description fields (network-name, router-name, etc), but no tag-table. Instead they rely on using the tag-table from default-device. A default-dev record, if present, must be the first item in the data file. Label-1, label-2, etc. are label sections which point to files containing data sections for each device.
6.1.1. The Label Section
6.1.1. The Label Section
label-section ::= BEGIN_LABEL <FS> <data-location> <FS>
<tag-name-list> <FS>
<start-time> <FS> <stop-time> <FS> END_LABEL
data-location ::= <data-file-name> | <empty>
label-section ::= BEGIN_LABEL <FS> <data-location> <FS> <tag-name-list> <FS> <start-time> <FS> <stop-time> <FS> END_LABEL data-location ::= <data-file-name> | <empty>
tag-name-list ::= <LEFT> <tag> [ <FS> <tag> ] <RIGHT>
tag-name-list ::= <LEFT> <tag> [ <FS> <tag> ] <RIGHT>
The label section gives the start and stop times for its corresponding data section (or sections) and a list of the tags it uses. If a data location is given it specifies the name of a file containing its data section; otherwise the data section follows in this file.
The label section gives the start and stop times for its corresponding data section (or sections) and a list of the tags it uses. If a data location is given it specifies the name of a file containing its data section; otherwise the data section follows in this file.
start-time ::= <time-string> stop-time ::= <time-string> data-file-name ::= <ASCII-string>
start-time ::= <time-string> stop-time ::= <time-string> data-file-name ::= <ASCII-string>
time-string ::= <year><month><day><hour><minute><second>
time-string ::= <year><month><day><hour><minute><second>
year ::= <digit><digit><digit><digit> month ::= 01..12 day ::= 01..31 hour ::= 00..23 minute ::= 00..59 second ::= <float>
year ::= <digit><digit><digit><digit> month ::= 01..12 day ::= 01..31 hour ::= 00..23 minute ::= 00..59 second ::= <float>
The start-time and stop-time are specified in UTC.
The start-time and stop-time are specified in UTC.
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A maximum of 60.0 is specified for 'seconds' so as to allow for leap seconds, as is done (for example) by ntp. If a time-zone changes during a data file--e.g. because daylight savings time has ended--this should be recorded by ending the current data section, writing a device section with the new time-zone and starting a new data section.
A maximum of 60.0 is specified for 'seconds' so as to allow for leap seconds, as is done (for example) by ntp. If a time-zone changes during a data file--e.g. because daylight savings time has ended--this should be recorded by ending the current data section, writing a device section with the new time-zone and starting a new data section.
6.1.2. The Device Section
6.1.2. The Device Section
device-section ::= BEGIN_DEVICE <FS> <device-field> <FS> END_DEVICE
device-field ::= <network-name><FS><router-name><FS><link-name<FS>
<bw-value><FS><proto-type><FS><proto-addr><FS>
<time-zone> <optional-tag-table>
optional-tag-table ::= <FS> <tag-table> | <empty>
device-section ::= BEGIN_DEVICE <FS> <device-field> <FS> END_DEVICE device-field ::= <network-name><FS><router-name><FS><link-name<FS> <bw-value><FS><proto-type><FS><proto-addr><FS> <time-zone> <optional-tag-table> optional-tag-table ::= <FS> <tag-table> | <empty>
network-name ::= <ASCII-string> router-name ::= <ASCII-string> link-name ::= <ASCII-string> bw-value ::= <float> proto-type ::= IP | DECNET | X.25 | CLNS | IPX | AppleTalk proto-addr ::= <ASCII-string> time-zone ::= [+|-] [00..13] [00..59]
network-name ::= <ASCII-string> router-name ::= <ASCII-string> link-name ::= <ASCII-string> bw-value ::= <float> proto-type ::= IP | DECNET | X.25 | CLNS | IPX | AppleTalk proto-addr ::= <ASCII-string> time-zone ::= [+|-] [00..13] [00..59]
tag-table ::= <LEFT> <tag-desc> [ <FS> <tag-desc> ] <RIGHT> tag-desc ::= <tag> <FS> <tag-class> <FS> <variable-field-list>
tag-table ::= <LEFT> <tag-desc> [ <FS> <tag-desc> ] <RIGHT> tag-desc ::= <tag> <FS> <tag-class> <FS> <variable-field-list>
tag ::= <ASCII-string> tag-class ::= total | peak
tag ::= <ASCII-string> tag-class ::= total | peak
variable-field-list ::= <LEFT> <variable-field>
[ <FS> <variable-field> ] <RIGHT>
variable-field ::= <variable-name><FS><initial-polling-period>
<FS> <aggregation-period>
variable-field-list ::= <LEFT> <variable-field> [ <FS> <variable-field> ] <RIGHT> variable-field ::= <variable-name><FS><initial-polling-period> <FS> <aggregation-period>
variable-name ::= <ASCII-string> initial-polling-period ::= <integer> aggregation-period ::= <integer>
variable-name ::= <ASCII-string> initial-polling-period ::= <integer> aggregation-period ::= <integer>
The network-name is a human readable string indicating to which network the logged data belong.
The network-name is a human readable string indicating to which network the logged data belong.
The router-name is given as an ASCII string, allowing for styles other than IP domain names (which are names of interfaces, not routers).
The router-name is given as an ASCII string, allowing for styles other than IP domain names (which are names of interfaces, not routers).
The link-name is a human readable string indicating the connectivity of the link where from the logged data is gathered.
The link-name is a human readable string indicating the connectivity of the link where from the logged data is gathered.
Lambert Informational [Page 15] RFC 1857 Operational Statistics October 1995
Lambert Informational [Page 15] RFC 1857 Operational Statistics October 1995
The units for bandwidth (bw-value) are bits per second, and are given as a floating-point number, e.g. 1536000 or 1.536e6. A zero value indicates that the actual bandwidth is unknown; one instance of this would be a Frame Relay link with Committed Information Rate different from Burst Rate.
The units for bandwidth (bw-value) are bits per second, and are given as a floating-point number, e.g. 1536000 or 1.536e6. A zero value indicates that the actual bandwidth is unknown; one instance of this would be a Frame Relay link with Committed Information Rate different from Burst Rate.
The proto-type field describes to which network architecture the interface being logged is connected. Valid types are IP, DECNET, X.25, CLNS, IPX and AppleTalk.
The proto-type field describes to which network architecture the interface being logged is connected. Valid types are IP, DECNET, X.25, CLNS, IPX and AppleTalk.
The network address (proto-addr) is the unique numeric address of the interface being logged. The actual form of this address is dependent on the protocol type as indicated in the proto-type field. For Internet connected interfaces the dotted-quad notation should be used.
The network address (proto-addr) is the unique numeric address of the interface being logged. The actual form of this address is dependent on the protocol type as indicated in the proto-type field. For Internet connected interfaces the dotted-quad notation should be used.
The time-zone indicates the time difference that should be added to the time-stamp in the data-section to give the local time for the logged interface. Note that the range for time-zone is sufficient to allow for all possibilities, not just those which fall on 30-minute multiples.
The time-zone indicates the time difference that should be added to the time-stamp in the data-section to give the local time for the logged interface. Note that the range for time-zone is sufficient to allow for all possibilities, not just those which fall on 30-minute multiples.
The tag-table lists all variables being polled. Variable names are the fully qualified Internet MIB names. The table may contain multiple tags. Each tag must be associated with only one polling and aggregation period. If variables are being polled or aggregated at different periods, a separate tag in the table must be used for each period.
The tag-table lists all variables being polled. Variable names are the fully qualified Internet MIB names. The table may contain multiple tags. Each tag must be associated with only one polling and aggregation period. If variables are being polled or aggregated at different periods, a separate tag in the table must be used for each period.
As variables may be polled with different polling periods within the same set of logged data, there is a need to explicitly associate a polling period with each variable. After processing, the actual period covered may have changed compared to the initial polling period and this should be noted in the aggregation period field. The initial polling period and aggregation period are given in seconds.
As variables may be polled with different polling periods within the same set of logged data, there is a need to explicitly associate a polling period with each variable. After processing, the actual period covered may have changed compared to the initial polling period and this should be noted in the aggregation period field. The initial polling period and aggregation period are given in seconds.
Original data values, and data values which have been aggregated by adding them together, will have a tag-class of 'total.' Data values which have been aggregated by finding the maximum over an aggregation time interval will have a tag-class of 'peak.'
Original data values, and data values which have been aggregated by adding them together, will have a tag-class of 'total.' Data values which have been aggregated by finding the maximum over an aggregation time interval will have a tag-class of 'peak.'
The tag-table and variable-field-lists are enclosed in brackets,
making the extent of each obvious. Without the brackets a parser
would have difficulty distinguishing between a variable name
(continuing the variable-field list for this tag) or a tag (starting
the next tag of the tag table). To make the distinction clearer to a
human reader one should use different kinds of brackets for each, for
example {} for the tag-table list and [] for the variable-field
The tag-table and variable-field-lists are enclosed in brackets, making the extent of each obvious. Without the brackets a parser would have difficulty distinguishing between a variable name (continuing the variable-field list for this tag) or a tag (starting the next tag of the tag table). To make the distinction clearer to a human reader one should use different kinds of brackets for each, for example {} for the tag-table list and [] for the variable-field
Lambert Informational [Page 16] RFC 1857 Operational Statistics October 1995
Lambert Informational [Page 16] RFC 1857 Operational Statistics October 1995
lists.
lists.
6.1.3. The Data Section
6.1.3. The Data Section
data-section ::= BEGIN_DATA <FS> <data-field>
[ <FS> <data-field> ] <FS> END_DATA
data-field ::= <time-string> <FS> <tag> <FS>
<poll-delta> <FS> <delta-val-list>
data-section ::= BEGIN_DATA <FS> <data-field> [ <FS> <data-field> ] <FS> END_DATA data-field ::= <time-string> <FS> <tag> <FS> <poll-delta> <FS> <delta-val-list>
delta-val-list ::= LEFT <delta-val> [ <FS> <delta-val> ] RIGHT
delta-val-list ::= LEFT <delta-val> [ <FS> <delta-val> ] RIGHT
poll-delta ::= <integer> delta-val ::= <integer>
poll-delta ::= <integer> delta-val ::= <integer>
FS ::= , | ; | :
LEFT ::= ( | [ | {
RIGHT ::= ) | ] | }
FS ::= , | ; | : LEFT ::= ( | [ | { RIGHT ::= ) | ] | }
A data-field contains values for each variable in the specified tag. A new data field should be written for each separate poll; there should be a one-to-one mapping betwen variables and values. Each data-field begins with the timestamp for this poll followed by the tag defining the polled variables followed by a polling delta value giving the period of time in seconds since the previous poll. The variable values are stored as delta values for counters and as absolute values for non-counter values such as OperStatus. The timestamp is in UTC and the time-zone field in the device section is used to compute the local time for the device being logged.
A data-field contains values for each variable in the specified tag. A new data field should be written for each separate poll; there should be a one-to-one mapping betwen variables and values. Each data-field begins with the timestamp for this poll followed by the tag defining the polled variables followed by a polling delta value giving the period of time in seconds since the previous poll. The variable values are stored as delta values for counters and as absolute values for non-counter values such as OperStatus. The timestamp is in UTC and the time-zone field in the device section is used to compute the local time for the device being logged.
Comma, semicolon or colon may be used as a field separator. Normally one would use commas within a line, semicolon at the end of a line and a colon after keywords such as BEGIN_LABEL.
Comma, semicolon or colon may be used as a field separator. Normally one would use commas within a line, semicolon at the end of a line and a colon after keywords such as BEGIN_LABEL.
Parentheses (), brackets [] or braces {} may be used as LEFT and
RIGHT brackets around tag-name, tag-table and delta-val lists. These
should be used in corresponding pairs, although combinations such as
(], [} etc. are syntactically valid.
Parentheses (), brackets [] or braces {} may be used as LEFT and RIGHT brackets around tag-name, tag-table and delta-val lists. These should be used in corresponding pairs, although combinations such as (], [} etc. are syntactically valid.
6.2. Storage Requirement Estimations
6.2. Storage Requirement Estimations
The header sections are not counted in this example. Assuming that the maximum polling intensity is used for all 12 recommended variables, that the size in ASCII of each variable is eight bytes and that there are no timestamps which are fractional seconds, the following calculations will give an estimate of storage requirements for one year of storing and aggregating statistical data.
The header sections are not counted in this example. Assuming that the maximum polling intensity is used for all 12 recommended variables, that the size in ASCII of each variable is eight bytes and that there are no timestamps which are fractional seconds, the following calculations will give an estimate of storage requirements for one year of storing and aggregating statistical data.
Lambert Informational [Page 17] RFC 1857 Operational Statistics October 1995
Lambert Informational [Page 17] RFC 1857 Operational Statistics October 1995
Assuming that data is saved according to the scheme
Assuming that data is saved according to the scheme
1 minute non-aggregated saved 1 day,
15 minute aggregation period saved 1 week,
1 hour aggregation period saved 1 month and
1 day aggregation period saved 1 year,
1 minute non-aggregated saved 1 day, 15 minute aggregation period saved 1 week, 1 hour aggregation period saved 1 month and 1 day aggregation period saved 1 year,
this will give:
this will give:
Size of one entry for each aggregation period:
Size of one entry for each aggregation period:
Aggregation periods
Aggregation periods
1 min 15 min 1 hour 1 day
1 min 15 min 1 hour 1 day
Timestamp 14 14 14 14
Tag 5 5 5 5
Poll-Delta 2 3 4 5
Total values 96 96 96 96
Peak values 0 96 192 288
Field separators 14 28 42 56
Timestamp 14 14 14 14 Tag 5 5 5 5 Poll-Delta 2 3 4 5 Total values 96 96 96 96 Peak values 0 96 192 288 Field separators 14 28 42 56
Total entry size 131 242 353 464
Total entry size 131 242 353 464
For each day 60*24 = 1440 entries with a total size of 1440*131 = 189 kB.
For each day 60*24 = 1440 entries with a total size of 1440*131 = 189 kB.
For each week 4*24*7 = 672 entries are stored with a total size of 672*242 = 163 kB.
For each week 4*24*7 = 672 entries are stored with a total size of 672*242 = 163 kB.
For each month 24*30 = 720 entries are stored with a total size of 720*353 = 254 kB.
For each month 24*30 = 720 entries are stored with a total size of 720*353 = 254 kB.
For each year 365 entries are stored with a total size of 365*464 = 169 kB.
For each year 365 entries are stored with a total size of 365*464 = 169 kB.
Grand total estimated storage for during one year = 775 kB.
Grand total estimated storage for during one year = 775 kB.
7. Report Formats
7. Report Formats
This section suggests some report formats and defines the metrics to be used in such reports.
This section suggests some report formats and defines the metrics to be used in such reports.
7.1. Report Types and Contents
7.1. Report Types and Contents
There are longer-term needs for monthly and yearly reports showing long-term tendencies in the network. There are short-term weekly reports giving information about medium-term changes in network
There are longer-term needs for monthly and yearly reports showing long-term tendencies in the network. There are short-term weekly reports giving information about medium-term changes in network
Lambert Informational [Page 18] RFC 1857 Operational Statistics October 1995
Lambert Informational [Page 18] RFC 1857 Operational Statistics October 1995
behavior which could serve as input to the medium-term engineering approach. Finally, there are daily reports giving the instantaneous overviews needed in the daily operations of a network.
behavior which could serve as input to the medium-term engineering approach. Finally, there are daily reports giving the instantaneous overviews needed in the daily operations of a network.
These reports should give information on:
These reports should give information on:
Offered Load Total traffic at external interfaces
Offered Load Segmented by "Customer"
Offered Load Segmented protocol/application.
Offered Load Total traffic at external interfaces Offered Load Segmented by "Customer" Offered Load Segmented protocol/application.
Resource Utilization Link/Router
Resource Utilization Link/Router
7.2. Content of the Reports
7.2. Content of the Reports
7.2.1. Offered Load by Link
7.2.1. Offered Load by Link
Metric categories: input octets per external interface
output octets per external interface
input packets per external interface
output packets per external interface
Metric categories: input octets per external interface output octets per external interface input packets per external interface output packets per external interface
The intent is to visualize the overall trend of network traffic on each connected external interface. This could be done as a bar-chart giving the totals for each of the four metric categories. Based on the time period selected this could be done on a hourly, daily, monthly or yearly basis.
The intent is to visualize the overall trend of network traffic on each connected external interface. This could be done as a bar-chart giving the totals for each of the four metric categories. Based on the time period selected this could be done on a hourly, daily, monthly or yearly basis.
7.2.2. Offered Load by Customer
7.2.2. Offered Load by Customer
Metric categories: input octets per customer
output octets per customer
input packets per customer
output packets per customer
Metric categories: input octets per customer output octets per customer input packets per customer output packets per customer
The recommendation here is to sort the offered load (in decreasing order) by customer. Plot the function F(n), where F(n) is percentage of total traffic offered to the top n customers or the function f(n) where f is the percentage of traffic offered by the nth ranked customers.
The recommendation here is to sort the offered load (in decreasing order) by customer. Plot the function F(n), where F(n) is percentage of total traffic offered to the top n customers or the function f(n) where f is the percentage of traffic offered by the nth ranked customers.
The definition of what is meant by a "customer" has to be done locally at the site where the statistics are being gathered.
The definition of what is meant by a "customer" has to be done locally at the site where the statistics are being gathered.
A cumulative plot could be useful as an overview of how traffic is distributed among users since it enables one to quickly pick off what fraction of the traffic comes from what number of "users."
A cumulative plot could be useful as an overview of how traffic is distributed among users since it enables one to quickly pick off what fraction of the traffic comes from what number of "users."
Lambert Informational [Page 19] RFC 1857 Operational Statistics October 1995
Lambert Informational [Page 19] RFC 1857 Operational Statistics October 1995
A method of displaying both average and peak behaviors in the same bar chart is to compute both the average value over some period and the peak value during the same period. The average and peak values are then displayed in the same bar.
A method of displaying both average and peak behaviors in the same bar chart is to compute both the average value over some period and the peak value during the same period. The average and peak values are then displayed in the same bar.
7.2.3. Resource Utilization Reporting
7.2.3. Resource Utilization Reporting
7.2.3.1. Utilization as Maximum Peak Behavior
7.2.3.1. Utilization as Maximum Peak Behavior
Link utilization is used to capture information on network loading. The polling interval must be small enough to be significant with respect to variations in human activity, since this is the activity that drives variations in network loading. On the other hand, there is no need to make it smaller than an interval over which excessive delay would notably impact productivity. For this reason, 30 minutes is a good estimate of the time at which people remain in one activity and over which prolonged high delay will affect their productivity. To track 30 minute variations, there is a need to sample twice as frequently, i.e., every 15 minutes. Use of the polling period of 10 minutes recommended above should be sufficient to capture variations in utilization.
Link utilization is used to capture information on network loading. The polling interval must be small enough to be significant with respect to variations in human activity, since this is the activity that drives variations in network loading. On the other hand, there is no need to make it smaller than an interval over which excessive delay would notably impact productivity. For this reason, 30 minutes is a good estimate of the time at which people remain in one activity and over which prolonged high delay will affect their productivity. To track 30 minute variations, there is a need to sample twice as frequently, i.e., every 15 minutes. Use of the polling period of 10 minutes recommended above should be sufficient to capture variations in utilization.
A possible format for reporting utilizations seen as peak behaviors is to use a method of combining averages and peak measurements onto the same diagram. Compare for example peak-meters on audio-equipment. If, for example, a diagram contains the daily totals for some period, then the peaks would be the most busy hour during each day. If the diagram were totals on an hourly basis then the peak would be the maximum ten-minute period in each hour.
A possible format for reporting utilizations seen as peak behaviors is to use a method of combining averages and peak measurements onto the same diagram. Compare for example peak-meters on audio-equipment. If, for example, a diagram contains the daily totals for some period, then the peaks would be the most busy hour during each day. If the diagram were totals on an hourly basis then the peak would be the maximum ten-minute period in each hour.
By combining the average and the maximum values for a certain time period, it should be possible to detect line utilization and bottlenecks due to temporary high loads.
By combining the average and the maximum values for a certain time period, it should be possible to detect line utilization and bottlenecks due to temporary high loads.
7.2.3.2. Utilization Visualized as a Frequency Distribution of Peaks
7.2.3.2. Utilization Visualized as a Frequency Distribution of Peaks
Another way of visualizing line utilization is to put the ten-minute samples in a histogram showing the relative frequency among the samples versus the load.
Another way of visualizing line utilization is to put the ten-minute samples in a histogram showing the relative frequency among the samples versus the load.
8. Considerations for Future Development
8. Considerations for Future Development
This memo is the first effort at formalizing a common basis for operational statistics. One major guideline in this work has been to keep the model simple to facilitate the easy integration of this model by vendors and NOCs into their operational tools.
This memo is the first effort at formalizing a common basis for operational statistics. One major guideline in this work has been to keep the model simple to facilitate the easy integration of this model by vendors and NOCs into their operational tools.
Lambert Informational [Page 20] RFC 1857 Operational Statistics October 1995
Lambert Informational [Page 20] RFC 1857 Operational Statistics October 1995
There are, however, some ideas that could progress further to expand the scope and usability of the model.
There are, however, some ideas that could progress further to expand the scope and usability of the model.
8.1. A Client/Server Based Statistical Exchange System
8.1. A Client/Server Based Statistical Exchange System
A possible path for development could be the definition of a client/server based architecture for providing Internet access to operational statistics. Such an architecture envisions that each NOC install a server which provides locally collected information in a variety of forms for clients.
A possible path for development could be the definition of a client/server based architecture for providing Internet access to operational statistics. Such an architecture envisions that each NOC install a server which provides locally collected information in a variety of forms for clients.
Using a query language, the client should be able to define the network object, the interface, the metrics and the time period to be provided. Using a TCP-based protocol, the server will transmit the requested data. Once these data are received by the client, they could be processed and presented by a variety of tools. One possibility is to have an X-Window based tool that displays defined diagrams from data, supporting such diagrams being fed into the X- Window tool directly from the statistical server. Another complementary method would be to generate PostScript output to print the diagrams. In all cases it should be possible to store the retrieved data locally for later processing.
Using a query language, the client should be able to define the network object, the interface, the metrics and the time period to be provided. Using a TCP-based protocol, the server will transmit the requested data. Once these data are received by the client, they could be processed and presented by a variety of tools. One possibility is to have an X-Window based tool that displays defined diagrams from data, supporting such diagrams being fed into the X- Window tool directly from the statistical server. Another complementary method would be to generate PostScript output to print the diagrams. In all cases it should be possible to store the retrieved data locally for later processing.
The client/server approach is discussed further by Henry Clark in RFC 1856.
The client/server approach is discussed further by Henry Clark in RFC 1856.
8.2. Inclusion of Variables not in the Internet Standard MIB
8.2. Inclusion of Variables not in the Internet Standard MIB
As has been pointed out above in the categorization of metrics, there are metrics which certainly could have been recommended if they were available in the Internet Standard MIB. To facilitate the inclusion of such metrics in the set of recommended metrics, it will be necessary to specify a subtree in the Internet Standard MIB containing variables judged necessary in the scope of performing operational statistics.
As has been pointed out above in the categorization of metrics, there are metrics which certainly could have been recommended if they were available in the Internet Standard MIB. To facilitate the inclusion of such metrics in the set of recommended metrics, it will be necessary to specify a subtree in the Internet Standard MIB containing variables judged necessary in the scope of performing operational statistics.
8.3. Detailed Resource Utilization Statistics
8.3. Detailed Resource Utilization Statistics
One area of interest not covered in the above description of metrics and presentation formats is to present statistics on detailed views of the traffic flows. Such views could include statistics on a per application basis and on a per protocol basis. Today such metrics are not part of the Internet Standard MIB. Tools like the NSF NNStat are being used to gather information of this kind. A possible way to achieve such data could be to define an NNStat MIB or to include such variables in the above suggested operational statistics MIB subtree.
One area of interest not covered in the above description of metrics and presentation formats is to present statistics on detailed views of the traffic flows. Such views could include statistics on a per application basis and on a per protocol basis. Today such metrics are not part of the Internet Standard MIB. Tools like the NSF NNStat are being used to gather information of this kind. A possible way to achieve such data could be to define an NNStat MIB or to include such variables in the above suggested operational statistics MIB subtree.
Lambert Informational [Page 21] RFC 1857 Operational Statistics October 1995
Lambert Informational [Page 21] RFC 1857 Operational Statistics October 1995
APPENDIX A
APPENDIX A
Some formulas for statistical aggregation
Some formulas for statistical aggregation
The following naming conventions are used:
The following naming conventions are used:
For poll values poll(n)_j
For poll values poll(n)_j
n = Polling or aggregation period
j = Entry number
n = Polling or aggregation period j = Entry number
poll(900)_j is thus the 15 minute total value.
poll(900)_j is thus the 15 minute total value.
For peak values peak(n,m)_j
For peak values peak(n,m)_j
n = Period over which the peak is calculated
m = The peak period length
j = Entry number
n = Period over which the peak is calculated m = The peak period length j = Entry number
peak(3600,900)_j is thus the maximum 15 minute period calculated over 1 hour.
peak(3600,900)_j is thus the maximum 15 minute period calculated over 1 hour.
Assume a polling over 24 hour period giving 1440 logged entries.
Assume a polling over 24 hour period giving 1440 logged entries.
=========================
=========================
Without any aggregation we have
Without any aggregation we have
poll(60)_1
......
poll(60)_1440
poll(60)_1 ...... poll(60)_1440
========================
========================
15 minute aggregation will give 96 entries of total values
15 minute aggregation will give 96 entries of total values
poll(900)_1
....
poll(900)_96
poll(900)_1 .... poll(900)_96
j=(n+14)
poll(900)_k = SUM poll(60)_j n=1,16,31,...1426
j=n k=1,2,....,96
j=(n+14) poll(900)_k = SUM poll(60)_j n=1,16,31,...1426 j=n k=1,2,....,96
There will also be 96 one-minute peak values.
There will also be 96 one-minute peak values.
Lambert Informational [Page 22] RFC 1857 Operational Statistics October 1995
Lambert Informational [Page 22] RFC 1857 Operational Statistics October 1995
j=(n+14)
peak(900,60)_k = MAX poll(60)_j n=1,16,31,....,1426
j=n k=1,2,....,96
j=(n+14) peak(900,60)_k = MAX poll(60)_j n=1,16,31,....,1426 j=n k=1,2,....,96
=======================
=======================
The next aggregation step is from 15 minutes to 1 hour. This gives 24 totals.
The next aggregation step is from 15 minutes to 1 hour. This gives 24 totals.
j=(n+3)
poll(3600)_k = SUM poll(900)_j n=1,5,9,.....,93
j=n k=1,2,....,24
j=(n+3) poll(3600)_k = SUM poll(900)_j n=1,5,9,.....,93 j=n k=1,2,....,24
and 24 one-minute peaks calculated over each hour.
and 24 one-minute peaks calculated over each hour.
j=(n+3)
peak (3600,60)_k = MAX peak(900,60)_j n=1,5,9,.....,93
j=n k=1,2,....24
j=(n+3) peak (3600,60)_k = MAX peak(900,60)_j n=1,5,9,.....,93 j=n k=1,2,....24
and finally 24 15-minute peaks calculated over each hour:
and finally 24 15-minute peaks calculated over each hour:
j=(n+3)
peak (3600,900) = MAX poll(900)_j n=1,5,9,.....,93
j=n
j=(n+3) peak (3600,900) = MAX poll(900)_j n=1,5,9,.....,93 j=n
===================
===================
The next aggregation step is from 1 hour to 24 hours. For each day with 1440 entries as above this will give
The next aggregation step is from 1 hour to 24 hours. For each day with 1440 entries as above this will give
j=(n+23)
poll(86400)_k = SUM poll(3600)_j n=1,25,51,.......
j=n k=1,2............
j=(n+23) poll(86400)_k = SUM poll(3600)_j n=1,25,51,....... j=n k=1,2............
j=(n+23)
peak(86400,60)_k = MAX peak(3600,60)_j n=1,25,51,....
j=n k=1,2.........
j=(n+23) peak(86400,60)_k = MAX peak(3600,60)_j n=1,25,51,.... j=n k=1,2.........
which gives the busiest 1 minute period over 24 hours.
which gives the busiest 1 minute period over 24 hours.
j=(n+23)
peak(86400,900)_k = MAX peak(3600,900)_j n=1,25,51,....
j=n k=1,2,........
j=(n+23) peak(86400,900)_k = MAX peak(3600,900)_j n=1,25,51,.... j=n k=1,2,........
which gives the busiest 15 minute period over 24 hours.
which gives the busiest 15 minute period over 24 hours.
j=(n+23)
j=(n+23)
Lambert Informational [Page 23] RFC 1857 Operational Statistics October 1995
Lambert Informational [Page 23] RFC 1857 Operational Statistics October 1995
peak(86400,3600)_k = MAX poll(3600)_j n=1,25,51,....
j=n k=1,2,........
peak(86400,3600)_k = MAX poll(3600)_j n=1,25,51,.... j=n k=1,2,........
which gives the busiest 1 hour period over 24 hours.
which gives the busiest 1 hour period over 24 hours.
===================
===================
There will probably be a difference between the three peak values in the final 24 hour aggregation. A smaller peak period will give higher values than a longer one, i.e., if adjusted to be numerically comparable.
There will probably be a difference between the three peak values in the final 24 hour aggregation. A smaller peak period will give higher values than a longer one, i.e., if adjusted to be numerically comparable.
poll(86400)/3600 < peak(86400,3600) < peak(86400,900)*4
< peak(86400,60)*60
poll(86400)/3600 < peak(86400,3600) < peak(86400,900)*4 < peak(86400,60)*60
APPENDIX B
APPENDIX B
An example
An example
Assuming below data storage:
Assuming below data storage:
BEGIN_DEVICE:
...
{
UNI-1,total: [ifInOctet, 60, 60,ifOutOctet, 60, 60];
BRD-1,total: [ifInNUcastPkts,300,300,ifOutNUcastPkts,300,300]
}
...
_装置を始めてください: ... UNI-1、合計: [ifInOctet、60 60 ifOutOctet、60、60]; BRD-1、合計: [ifInNUcastPkts、30万300、ifOutNUcastPkts、30万300]、…
which gives
どれ、付与
BEGIN_DATA:
19920730000000,UNI-1,60:(val1-1,val2-1);
19920730000060,UNI-1,60:(val1-2,val2-2);
19920730000120,UNI-1,60:(val1-3,val2-3);
19920730000180,UNI-1,60:(val1-4,val2-4);
19920730000240,UNI-1,60:(val1-5,val2-5);
19920730000300,UNI-1,60:(val1-6,val2-6);
19920730000300,BRD-1,300:(val1-7,val2-7);
19920730000360,UNI-1,60:(val1-8,val2-8);
...
_データを始めてください: 19920730000000 UNI-1、60: (val1-1、val2-1)。 19920730000060 UNI-1、60: (val1-2、val2-2)。 19920730000120 UNI-1、60: (val1-3、val2-3)。 19920730000180 UNI-1、60: (val1-4、val2-4)。 19920730000240 UNI-1、60: (val1-5、val2-5)。 19920730000300 UNI-1、60: (val1-6、val2-6)。 19920730000300 BRD-1,300: (val1-7、val2-7)。 19920730000360 UNI-1、60: (val1-8、val2-8)。 ...
Aggregation to 15 minutes gives
数分が与える15への集合
BEGIN_DEVICE:
...
_装置を始めてください: ...
Lambert Informational [Page 24] RFC 1857 Operational Statistics October 1995
操作上の[24ページ]RFC1857統計1995年10月の情報のランバート
{
UNI-1,total: [ifInOctet, 60,900,ifOutOctet, 60,900];
BRD-1,total: [ifInNUcastPkts,300,900,ifOutNUcastPkts,300,900];
UNI-2,peak: [ifInOctet, 60,900,ifOutOctet, 60,900];
BRD-2,peak: [ifInNUcastPkts,300,900,ifOutNUcastPkts,300,900]
}
...
UNI-1、合計: [; ifInOctet、6万900、ifOutOctet、6万900、]BRD-1、合計:、[ifInNUcastPkts、30万900 ifOutNUcastPkts、30万900]; UNI-2、ピーク: [; ifInOctet、6万900、ifOutOctet、6万900、]BRD-2、ピーク:、[ifInNUcastPkts、30万900、ifOutNUcastPkts、30万900]、…
where UNI-1 is the 15 minute total
BRD-1 is the 15 minute total
UNI-2 is the 1 minute peak over 15 minute (peak = peak(1))
BRD-2 is the 5 minute peak over 15 minute (peak = peak(1))
UNI-1がどこの15の微小な総BRD-1であるかが総UNI-2が15分以上の1分のピークである15分である、(ピーク=ピーク(1))BRD-2は15分以上の5分のピークです。(=ピーク(1))に最大限にしてください。
which gives
どれ、付与
BEGIN_DATA:
19920730000900,UNI-1,900:(tot-val1,tot-val2);
19920730000900,BRD-1,900:(tot-val1,tot-val2);
19920730000900,UNI-2,900:(peak(1)-val1,peak(1)-val2);
19920730000900,BRD-2,900:(peak(1)-val1,peak(1)-val2);
19920730001800,UNI-1,900:(tot-val1,tot-val2);
19920730001800,BRD-1,900:(tot-val1,tot-val2);
19920730001800,UNI-2,900:(peak(1)-val1,peak(1)-val2);
19920730001800,BRD-2,900:(peak(1)-val1,peak(1)-val2);
...
_データを始めてください: 19920730000900 UNI-1,900: (幼児-val1、幼児-val2)。 19920730000900 BRD-1,900: (幼児-val1、幼児-val2)。 19920730000900 UNI-2,900: (ピーク(1)-val1、ピーク(1)-val2)。 19920730000900 BRD-2,900: (ピーク(1)-val1、ピーク(1)-val2)。 19920730001800 UNI-1,900: (幼児-val1、幼児-val2)。 19920730001800 BRD-1,900: (幼児-val1、幼児-val2)。 19920730001800 UNI-2,900: (ピーク(1)-val1、ピーク(1)-val2)。 19920730001800 BRD-2,900: (ピーク(1)-val1、ピーク(1)-val2)。 ...
Next aggregation step to 1 hour generates:
1時間への次の集合ステップは以下を発生させます。
BEGIN_DEVICE:
...
{
UNI-1,total: [ifInOctet, 60,3600,ifOutOctet, 60,3600];
BRD-1,total: [ifInNUcastPkts,300,3600,ifOutNUcastPkts,300,3600];
UNI-2,peak: [ifInOctet, 60,3600,ifOutOctet, 60,3600];
BRD-2,peak: [ifInNUcastPkts,300, 900,ifOutNUcastPkts,300, 900];
UNI-3,peak: [ifInOctet, 900,3600,ifOutOctet, 900,3600];
BRD-3,peak: [ifInNUcastPkts,900,3600,ifOutNUcastPkts,900,3600]
}
_装置を始めてください: ... UNI-1、以下を合計してください、[ifInOctet、3600 60、3600]; 合計: BRD-1、[ifInNUcastPkts、300、3600、ifOutNUcastPkts300、3600]; (ifOutOctet、UNI-2)が最大限にする60: [60、3600]; ifInOctet、60、3600、ifOutOctet、BRD-2、以下に最大限にしてください[ifInNUcastPkts、300、900、ifOutNUcastPkts300、900]; UNI-3、ピークに達してください:、[900、3600]; ifInOctet、900、3600、ifOutOctet、BRD-3、以下に最大限にしてください[ifInNUcastPkts、900、3600、ifOutNUcastPkts900、3600]。
where UNI-1 is the one hour total BRD-1 is the one hour total UNI-2 is the 1 minute peak over 1 hour (peak of peak = peak(2)) BRD-2 is the 5 minute peak over 1 hour (peak of peak = peak(2)) UNI-3 is the 15 minute peak over 1 hour (peak = peak(1)) BRD-3 is the 15 minute peak over 1 hour (peak = peak(1))
BRD-1がUNI-1が1時間の合計であることの総UNI-2が1時間以上の1分のピークである1時間である、(ピーク=ピーク(2))BRD-2のピークが1時間以上の5分のピークである、(ピーク=ピーク(2))UNI-3のピークが1時間以上の15分のピークである、(ピーク=ピーク(1))BRD-3は1時間以上の15分のピークです。(=ピーク(1))に最大限にしてください。
Lambert Informational [Page 25] RFC 1857 Operational Statistics October 1995
操作上の[25ページ]RFC1857統計1995年10月の情報のランバート
which gives
どれ、付与
BEGIN_DATA:
19920730003600,UNI-1,3600:(tot-val1,tot-val2);
19920730003600,BRD-1,3600:(tot-val1,tot-val2);
19920730003600,UNI-2,3600:(peak(2)-val1,peak(2)-val2);
19920730003600,BRD-2,3600:(peak(2)-val1,peak(2)-val2);
19920730003600,UNI-3,3600:(peak(1)-val1,peak(1)-val2);
19920730003600,BRD-3,3600:(peak(1)-val1,peak(1)-val2);
19920730007200,UNI-1,3600:(tot-val1,tot-val2);
19920730007200,BRD-1,3600:(tot-val1,tot-val2);
19920730007200,UNI-2,3600:(peak(2)-val1,peak(2)-val2);
19920730007200,BRD-2,3600:(peak(2)-val1,peak(2)-val2);
19920730007200,UNI-3,3600:(peak(1)-val1,peak(1)-val2);
19920730007200,BRD-3,3600:(peak(1)-val1,peak(1)-val2);
...
_データを始めてください: 19920730003600 UNI-1、3600: (幼児-val1、幼児-val2)。 19920730003600 BRD-1、3600: (幼児-val1、幼児-val2)。 19920730003600 UNI-2、3600: (ピーク(2)-val1、ピーク(2)-val2)。 19920730003600 BRD-2、3600: (ピーク(2)-val1、ピーク(2)-val2)。 19920730003600 UNI-3、3600: (ピーク(1)-val1、ピーク(1)-val2)。 19920730003600 BRD-3、3600: (ピーク(1)-val1、ピーク(1)-val2)。 19920730007200 UNI-1、3600: (幼児-val1、幼児-val2)。 19920730007200 BRD-1、3600: (幼児-val1、幼児-val2)。 19920730007200 UNI-2、3600: (ピーク(2)-val1、ピーク(2)-val2)。 19920730007200 BRD-2、3600: (ピーク(2)-val1、ピーク(2)-val2)。 19920730007200 UNI-3、3600: (ピーク(1)-val1、ピーク(1)-val2)。 19920730007200 BRD-3、3600: (ピーク(1)-val1、ピーク(1)-val2)。 ...
Finally aggregation step to 1 day generates:
最終的に1日への集合ステップは以下を発生させます。
BEGIN_DEVICE:
...
{
UNI-1,total: [ifInOctet, 60,86400,ifOutOctet, 60,86400];
BRD-1,total: [ifInNUcastPkts, 300,86400,ifOutNUcastPkts, 300,86400];
UNI-2,peak: [ifInOctet, 60,86400,ifOutOctet, 60,86400];
BRD-2,peak: [ifInNUcastPkts, 300, 900,ifOutNUcastPkts, 300, 900];
UNI-3,peak: [ifInOctet, 900,86400,ifOutOctet, 900,86400];
BRD-3,peak: [ifInNUcastPkts, 900,86400,ifOutNUcastPkts, 900,86400];
UNI-4,peak: [ifInOctet, 3600,86400,ifOutOctet, 3600,86400];
BRD-4,peak: [ifInNUcastPkts,3600,86400,ifOutNUcastPkts,3600,86400]
}
...
_装置を始めてください: ... 合計..合計..ピークに達する..ピークに達する..ピーク..最大限にする..ピークに達する..最大限にする
where
UNI-1 is the 24 hour total
BRD-1 is the 24 hour total
UNI-2 is the 1 minute peak over 24 hour
(peak of peak of peak = peak(3))
UNI-3 is the 15 minute peak over 24 hour (peak of peak = peak(2))
UNI-4 is the 1 hour peak over 24 hour (peak = peak(1))
BRD-2 is the 5 minute peak over 24 hour
(peak of peak of peak = peak(3))
BRD-3 is the 15 minute peak over 24 hour (peak of peak = peak(2))
BRD-4 is the 1 hour peak over 24 hour (peak = peak(1))
BRD-1がUNI-1が24時間の合計であることの総UNI-2が24時間以上の1分のピークである24時間である、(ピーク=ピーク(3))UNI-3のピークのピークが24時間以上の15分のピークである、(ピーク=ピーク(2))UNI-4のピークが24時間以上の1時間のピークである、(ピーク=ピーク(1))BRD-2が24時間以上の5分のピークである、(ピーク=ピーク(3))BRD-3のピークのピークが24時間以上の15分のピークである、(ピーク=ピーク(2))BRD-4のピークは24時間以上の1時間のピークです。(=ピーク(1))に最大限にしてください。
which gives
どれ、付与
Lambert Informational [Page 26] RFC 1857 Operational Statistics October 1995
操作上の[26ページ]RFC1857統計1995年10月の情報のランバート
BEGIN_DATA:
19920730086400,UNI-1,86400:(tot-val1,tot-val2);
19920730086400,BRD-1,86400:(tot-val1,tot-val2);
19920730086400,UNI-2,86400:(peak(3)-val1,peak(3)-val2);
19920730086400,BRD-2,86400:(peak(3)-val1,peak(3)-val2);
19920730086400,UNI-3,86400:(peak(2)-val1,peak(2)-val2);
19920730086400,BRD-3,86400:(peak(2)-val1,peak(2)-val2);
19920730086400,UNI-4,86400:(peak(1)-val1,peak(1)-val2);
19920730086400,BRD-4,86400:(peak(1)-val1,peak(1)-val2);
19920730172800,UNI-1,86400:(tot-val1,tot-val2);
19920730172800,BRD-1,86400:(tot-val1,tot-val2);
19920730172800,UNI-2,86400:(peak(3)-val1,peak(3)-val2);
19920730172800,BRD-2,86400:(peak(3)-val1,peak(3)-val2);
19920730172800,UNI-3,86400:(peak(2)-val1,peak(2)-val2);
19920730172800,UNI-3,86400:(peak(2)-val1,peak(2)-val2);
19920730172800,UNI-4,86400:(peak(1)-val1,peak(1)-val2);
19920730172800,BRD-4,86400:(peak(1)-val1,peak(1)-val2);
...
_データを始めてください: 19920730086400 UNI-1、86400: (幼児-val1、幼児-val2)。 19920730086400 BRD-1、86400: (幼児-val1、幼児-val2)。 19920730086400 UNI-2、86400: (ピーク(3)-val1、ピーク(3)-val2)。 19920730086400 BRD-2、86400: (ピーク(3)-val1、ピーク(3)-val2)。 19920730086400 UNI-3、86400: (ピーク(2)-val1、ピーク(2)-val2)。 19920730086400 BRD-3、86400: (ピーク(2)-val1、ピーク(2)-val2)。 19920730086400 UNI-4、86400: (ピーク(1)-val1、ピーク(1)-val2)。 19920730086400 BRD-4、86400: (ピーク(1)-val1、ピーク(1)-val2)。 19920730172800 UNI-1、86400: (幼児-val1、幼児-val2)。 19920730172800 BRD-1、86400: (幼児-val1、幼児-val2)。 19920730172800 UNI-2、86400: (ピーク(3)-val1、ピーク(3)-val2)。 19920730172800 BRD-2、86400: (ピーク(3)-val1、ピーク(3)-val2)。 19920730172800 UNI-3、86400: (ピーク(2)-val1、ピーク(2)-val2)。 19920730172800 UNI-3、86400: (ピーク(2)-val1、ピーク(2)-val2)。 19920730172800 UNI-4、86400: (ピーク(1)-val1、ピーク(1)-val2)。 19920730172800 BRD-4、86400: (ピーク(1)-val1、ピーク(1)-val2)。 ...
Security Considerations
セキュリティ問題
Security issues are discussed in Section 2.4.
セクション2.4で安全保障問題について議論します。
Author's Address
作者のアドレス
Michael H. Lambert Pittsburgh Supercomputing Center 4400 Fifth Avenue Pittsburgh, PA 15213 USA
マイケル・H.Lambertピッツバーグスーパーコンピューティングセンター4400五番街PA15213ピッツバーグ(米国)
Phone: +1 412 268-4960 Fax: +1 412 268-8200 EMail: lambert@psc.edu
以下に電話をしてください。 +1 412 268-4960Fax: +1 412 268-8200 メールしてください: lambert@psc.edu
Lambert Informational [Page 27]
ランバートInformationalです。[27ページ]
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