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rfc:rfc1404

Network Working Group B. Stockman Request for Comments: 1404 NORDUnet/SUNET

                                                          January 1993
             A Model for Common Operational Statistics

Status of the Memo

 This memo provides information for the Internet community.  It does
 not specify an Internet standard.  Distribution of this memo is
 unlimited.

Abstract

 This memo describes a model for operational statistics in the
 Internet.  It gives recommendations for metrics, measurements,
 polling periods, storage formats and presentation formats.

Acknowledgements

 The author would like to thank the members of the Operational
 Statistics Working Group of the IETF whose efforts made this memo
 possible.

Table of Contents

 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 ..................................... 7
 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
 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

Stockman [Page 1] RFC 1404 Operational Statistics January 1993

 3.4.1   Chosen Metrics .......................................... 10
 4.      Polling Frequencies ..................................... 11
 4.1     Variables Needing High Resolution Polling ............... 11
 4.2     Variables not Needing High Resolution Polling ........... 11
 5.      Pre-Processing of Raw Statistical Data .................. 12
 5.1     Optimizing and Concentrating Data to Resources .......... 12
 5.2     Aggregation of Data ..................................... 12
 6.      Storing of Statistical Data ............................. 13
 6.1     The Storage Format ...................................... 13
 6.1.1   The Label Section ....................................... 14
 6.1.2   The Device Section ...................................... 14
 6.1.3   The Data Section ........................................ 16
 6.2     Storage Requirement Estimations ......................... 17
 7.      Report Formats .......................................... 18
 7.1     Report Types and Contents ............................... 18
 7.2     Contents of the Reports ................................. 18
 7.2.1   Offered Load by Link .................................... 18
 7.2.2   Offered Load by Customer ................................ 18
 7.2.3   Resource Utilization Reporting .......................... 19
 7.2.3.1 Utilization as Maximum Peak Behavior .................... 19
 7.2.3.2 Utilization as Frequency Distribution of Peaks .......... 19
 8.      Considerations for Future Development ................... 20
 8.1     A Client/Server Based Statistical Exchange System ....... 20
 8.2     Inclusion of Variables not in the Internet Standard MIB . 20
 8.3     Detailed Resource Utilization Statistics ................ 20
 Appendix A  Some formulas for statistical aggregation ........... 21
 Appendix B  An example .......................................... 24
 Security Considerations ......................................... 27
 Author's Address ................................................ 27

1. Introduction

 Today it is not uncommon for many network administrations to collect
 and archive network management metrics that indicate network
 utilization, growth, and outages.  The primary goal is to facilitate
 near-term problem isolation and longer-term network planning within
 the organization.  There is also the larger goal of cooperative
 problem isolation and network planning between network
 administrations.  This larger goal is likely to become increasingly
 important as the Internet continues to grow.
 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 makes it difficult
 to compare data between networks.  Plus, there is not common
 agreement on what metrics should be regularly collected or how they
 should be displayed.

Stockman [Page 2] RFC 1404 Operational Statistics January 1993

 There needs to be an agreed-upon model for
  1) A minimal set of common network management metrics to satisfy the
     goals stated above.
  2) Tools for collecting these metrics.
  3) A common storage format to facilitate the usage of these data by
     common presentation tools.
  4) Common presentation formats.
 Under this Operational Statistics model, collection tools will
 collect and store data in a given format to be retrieved later by
 presentation tools displaying the data in a predefined way.  (See
 figure below.)

Stockman [Page 3] RFC 1404 Operational Statistics January 1993

                   The Operational Statistics Model
 (Collection of common metrics, by commonly available tools, stored in
 a common format, displayed in common formats by commonly available
 presentation tools.)
                    !-----------------------!
                    !       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)      !
                      !-------------------!

Stockman [Page 4] RFC 1404 Operational Statistics January 1993

 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
 conforming to this model.
 The model defines a minimal set of metrics, how these metrics should
 gathered and stored. Finally the model gives recommendations on the
 content and the layout of statistical reports making it possible to
 easily compare networks statistics between NOCs.
 The primary purpose of this model is to define ways and methods on
 how NOCs could most effectively share their operational statistics.
 One intention with this model is to specify a baseline capability
 that NOCs conforming to the this model may support with a minimal
 development effort and a minimal ongoing effort.

2. The Model

 The model defines three areas of interest on which all underlying
 concepts are based.
      1. The definition of a minimal set of metrics to be gathered
      2. The definition of a format for storing collected statistical
         data.
      3. The definition of methods and formats for generating
         reports.
 The model indicates that old tools used today could be retrofitted
 into the new paradigm. This could be done by providing conversion-
 filters between the old and the new environment tools. In this sense
 this model intends to advocate the development of public domain
 software for use by participating NOCs.
 One basic idea with the model is that statistical data stored at one
 place could be retrieved and displayed at some other place.

2.1 Metrics and Polling Periods

 The intention here is to define a minimal set of metrics that easily
 could be gathered using standard SNMP based network management tools.
 These metrics should hence be available as variables in the Internet
 Standard MIB.
 If the Internet Standard MIB is changed also this minimal set of
 metrics could be reconsidered as there are many metrics viewed as

Stockman [Page 5] RFC 1404 Operational Statistics January 1993

 important but currently not being defined in the standard MIB.  For
 some metrics being highly desirable to collect there are currently no
 way to get them into the Internet Standard MIB as these metrics
 probably are not possible to retrieve using SNMP.  Tools and methods
 in gathering such metrics should be explicitly defined if such
 metrics are to be considered. This is, however, outside of the scope
 of this memo.

2.2 Format for Storing Collected Data

 A format for storing data is defined. The intention is to minimize
 redundant information by using a single header structure where all
 information relevant to a certain set of statistical data is stored.
 This header section will give information on when and where the
 corresponding statistical data where collected.

2.3 Reports

 Some basic classes of reports are suggested with regards to different
 views of network behavior. For this reason reports on totals of
 octets and packets over some period in time are regarded as essential
 to give an overall view of the traffic flows in a network.
 Differentiation between application and protocols to give ideas on
 which type of traffic is dominant is regarded as needed.  Finally
 reports on resource utilization are recommended..
 Depending on the intention with a report the timeperiod over which it
 spans may vary. For capacity planning there may be a need for longer
 term reports while in engineering and operation there may be
 sufficient with reports on weekly or daily basis.

2.4 Security Issues

 There are legal, ethical and political concerns of data sharing.
 People are concerned about showing data that may make one of the
 networks look bad.
 For this reason there is a need to insure integrity, conformity and
 confidentiality of the shared data. To be useful, the same data must
 be collected from all of the involved sites and it must be collected
 at the same interval. To prevent vendors from getting an unfair
 performance information, certain data must not be made available.

Stockman [Page 6] RFC 1404 Operational Statistics January 1993

3. Categorization of Metrics

3.1 Overview

 This section gives a classification of metrics with regard to scope
 and easiness of retrieve. A recommendation of a minimal set of
 metrics is given. The 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.

3.2 Categorization of Metrics Based on Measurement Areas

 The metrics used in evaluating network traffic could be classified
 into (at least) four major categories:
  1. Utilization metrics
  2. Performance metrics
  3. Availability metrics
  4. Stability metrics

3.2.1. Utilization Metrics

 These category describes different aspects of the total traffic being
 forwarded through the network. Possible metrics are:
  1. Total input and output packets and octets.
  2. Various peak metrics.
  3. Per protocol and per application metrics.

3.2.2 Performance Metrics

 These metrics describes the quality of service such as delays and
 congestion situations. Possible metrics are:
  1. RTT metrics on different protocol layers.
  2. Number of collisions on a bus network
  3. Number of ICMP Source Quench messages.
  4. Number of packets dropped.
  5. etc.

3.2.3 Availability Metrics

 This could be considered as the long term accessibility metrics on
 different protocol layers. Possible metrics are:

Stockman [Page 7] RFC 1404 Operational Statistics January 1993

  1. Line availability as percentage uptime.
  2. Route availability
  3. Application availability

3.2.4 Stability Metrics

 These metrics describes short term fluctuations in the network which
 degrades the service level. Also changes in traffic patterns could be
 recognized using these metrics.  Possible metrics are:
  1. Number of fast line status transitions
  2. Number of fast route changes (also known as route flapping)
  3. Number of routes per interface in the tables
  4. Next hop count stability.
  5. Short term ICMP behaviors.

3.3 Categorization Based on Availability of Metrics

 To be able to retrieve metrics the corresponding variables must be
 possible to access at every network object being part of the
 management domain for which statistics are being collected.
 Some metrics are easily retrievable as being defined as variables in
 the Internet Standard MIB while other metrics may be retrievable as
 being part of some vendor's private enterprise MIB subtree.  Finally
 some metrics are considered as impossible to retrieve due to not
 being possible to include in the SNMP concept or that the actual
 measurement of these metrics would require extensive polling and
 hence download the network with management traffic.
 The metrics being categorized below could each be judged as an
 important metric in evaluating network behaviors.  This list may
 serve for reconsider the decisions on which metric to be regarded as
 reasonable and desirable to collect. If the availability of below
 metrics changes these decisions may change.

3.3.1 Per Interface Variables Already in Internet Standard MIB

    (thus easy to retrieve)
      ifInUcastPkts   (unicast packet in)
      ifOutUcastPkts  (unicast packet out)
      ifInNUcastPkts  (non-unicasts packet in
      ifOutNUcastPkts (non-unicast packet out)
      ifInOctets      (octets in)
      ifOutOctets     (octets out)
      ifOperStatus    (line status)

Stockman [Page 8] RFC 1404 Operational Statistics January 1993

3.3.2 Per Interface Variables in Internet Private Enterprise MIB

    (thus could sometimes be possible to retrieve)
      discarded packets in
      discarded packets out
      congestion events in
      congestion events out
      aggregate errors
      interface resets

3.3.3 Per Interface Variables Needing High Resolution Polling

    (which is hard due to resulting network load)
      interface queue length
      seconds missing stats
      interface unavailable
      route changes
      interface next hop count

3.3.4 Per Interface Variables not in any MIB

    (thus impossible to retrieve using SNMP but possible to include
     in a MIB).
      link layer packets in
      link layer packets out
      link layer octets in
      link layer octets out
      packet interarrival times
      packet size distribution

3.3.5 Per Node Variables

    (not categorized here)
      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
      sys uptime
      poll delta time
      reboot count

Stockman [Page 9] RFC 1404 Operational Statistics January 1993

3.3.6 Metrics not being Retrievable with SNMP

      delays (RTTs) on different protocol layers
      application layer availabilities
      peak behavior metrics

3.4 Recommended Metrics

 A large amount of metrics could be regarded for gathering in the
 process of doing network statistics. To facilitate for this model to
 reach general consensus there is a need to define a minimal set of
 metrics that are both essential and also possible to retrieve in a
 majority of today network objects. As an indication of being
 generally retrievable the presence in the Internet Standard MIB is
 regarded as a mandatory requirement.

3.4.1 Chosen Metrics

 The following metrics were chosen as desirable and reasonable being
 part of the Internet Standard MIB:
 For each interface:
      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)
 For each node:
      ipForwDatagrams (IP forwards)
      ipInDiscards    (IP in discards)
      sysUpTime       (system uptime)
 All of the above metrics are available in the Internet Standard MIB.
 However, there also other metrics which could be recommended such as
 the RTT metric which probably never will be in any MIB.  For such
 metrics other collection tools than SNMP have to be explicitly
 defined. The specification of such tools are outside scope of this
 memo.

Stockman [Page 10] RFC 1404 Operational Statistics January 1993

4. Polling Frequencies

 The reason for the polling is to achieve statistics to serve as base
 for trend and capacity planning. From the operational data it shall
 be possible to derive engineering and management data. It shall be
 noted that all polling and saving values below are recommendation and
 not mandatory.

4.1 Variables Needing High Resolution Polling

 To be able to detect peak behaviors it is recommended that a period
 of maximum 1 minute (60 seconds) is used in the gathering of traffic
 data. The metrics to be gathered at this frequency is:
 for each interface
      ifInOctets      (octets in)
      ifOutOctets     (octets out)
      ifInUcastPkts   (unicast packets in)
      ifOutUcastPkts  (unicast packets out)
 If not possible to gather data at this high polling frequency, it is
 recommended that an even multiple of 60 seconds is used. The initial
 polling frequency value will be part of the stored statistical data
 as described in section 4 below.

4.2 Variables not Needing High Resolution Polling

 The other part of the recommended variables to be gathered, i.e.,
 For each interface:
      ifInNUcastPkts  (non-unicast packets in)
      ifOutNUcastPkts (non-unicast packets out)
      ifInDiscards    (in discards)
      ifOutDiscards   (out discards)
      ifOperStatus    (line status)
 and for each node:
      ipForwDatagrams (IP forwards)
      ipInDiscards    (IP in discards)
      sysUpTime       (system uptime)
 These variables could be gathered at a lower polling rate. No
 specific polling rate is mentioned but it is recommended that the
 period chosen is an even multiple of 60 seconds.

Stockman [Page 11] RFC 1404 Operational Statistics January 1993

5. Pre-Processing of Raw Statistical Data

5.1 Optimizing and Concentrating Data to Resources

 To avoid redundant data being stored in commonly available storage
 there is a need for processing the raw data. For example if a link is
 down there is no need to continuous store a counter that is not
 changing. Using variables such as sysUpTime and Line Status there is
 the possibility of not continuously storing data collected from links
 and nodes where no traffic have been transmitted over some period of
 time.
 Another aspect of processing is to decouple the data from the raw
 interface being polled. The intention should be to convert such data
 into the resource being 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 provided data.

5.2 Aggregation of Data

 A polling period of 1 minute will create the need of aggregating
 stored data.  Aggregation here means that over a period with logged
 entries, a new aggregated entry is created by taking the first and
 last of the previously logged entries over some aggregation period
 and compute a new entry.
 Not to loose information on the peak values the aggregation also
 means that the peak value of the previous aggregation period is
 calculated and stored.
 This gives below layout of aggregated entries
 It is foreseen that over a relatively short period, polled data will
 be logged at the tightest polling period (1 minute).  Regularly these
 data will be pre-processed into the actual files being provided.
 Suggestions for aggregation periods:
 Over a
      24 hour period        aggregate to 15 minutes,
      1 month period        aggregate to 1 hour,
      1 year period         aggregate to 1 day
 Aggregation is the computation of new average and maximum values for
 the aggregation period based on the previous aggregation period data.
 For each aggregation period the maximum, and average values are
 computed and stored. Also other aggregation period could be chosen

Stockman [Page 12] RFC 1404 Operational Statistics January 1993

 when needed. The chosen aggregation period value will be stored
 together with the aggregated data as described below.

6. Storing of Statistical Data

 This section describes a format for storing of statistical data.  The
 goal is to facilitate for a common set of tools for the gathering,
 storing and analysis of statistical data. The format is defined with
 the intention to minimize redundant information and by this minimize
 required storage. If a client server based model for retrieving
 remote statistical data is later being developed, the specified
 storage format should be possible to used as the transmission
 protocol.
 The format is built up by three different sections within the
 statistical storage, a label section, a device section and a data
 section. The label section gives the start and end times for a given
 data section as well as the file where the actual data is stored.
 The device section specifies what is being logged in the
 corresponding data section.
 To facilitate for multiple data sections within one log-file, label
 sections, device sections and data sections may occur more than once.
 Each section type is delimited by a BEGIN-END pair.  Label and device
 sections could either be stored directly in the data-file or as
 separate files where the corresponding data-file is pointed out by
 the data-file entry in the label section.
 A data section must correspond to exactly one label section and one
 device section.  If more label sections and device sections each data
 section will belong to the label section and device section
 immediately prepending the data section if these sections are stored
 within the data-file. How files are physically arranged is outside
 the scope of the document.

6.1 The Storage Format

  stat-data ::=
  <label-section><FS><device-section><FS><data-section><FS>
  [<device-section><FS><data-section><FS>]
  FS ::= "," | <LF> | <LF> # any text here <LF>
 The file must start with a label specification followed by a device
 specification followed by a data section. If the storing of logged
 data is for some reason interrupted a new label specification should
 be inserted when the storing is restarted. If the device being logged
 is changed this should be indicated as a new label and a new device

Stockman [Page 13] RFC 1404 Operational Statistics January 1993

 specification.
 It shall here be noted that the actual physical storage of data is a
 local decision and can vary a lot. There can be one data-file per
 interface or multiple interfaces logged within the same data-file.
 Label and device sections may be stored in a separate file as well as
 within the data-file.

6.1.1 The Label Section

  label-section ::=  "BEGIN_LABEL"  <FS>
                     <start_time>   <FS>
                     <stop_time>    <FS>
                     <data_file>    <FS>
                     "END_LABEL"
  start-time  ::= <time-string>
  end-time    ::= <time-string>
  file-name   ::= <ascii-string>
  time-string ::= <year><month><day><hour><minute><second>
  year        ::= <digit><digit><digit><digit>
  month       ::= 01 | ... | 12
  hour        ::= 00 | ... | 23
  minute      ::= 00 | ... | 59
  second      ::= 00 | ... | 59
  digit       ::=  0 | ... | 9
  ascii-string ::= same as MIB II definition of <ascii-string>
 The times defines start and stop times for the related set of logged
 data. The time is in UTC.

6.1.2 The Device Section

  device-section ::= "BEGIN_DEVICE" <FS>
                     <device-field> <FS>
                     "END_DEVICE"
  device-field   ::= <networkname><FS><routername><FS><linkname><FS>
                     <bw-value><FS><bw-sort><FS><proto-type><FS>
                     <proto-addr><FS><time-zone><FS><tag-table>
                     [<tag-table>]
  networkname    ::= <ascii-string>
  routername     ::= <fully qualified domain name>
  linkname       ::= <ascii-string>

Stockman [Page 14] RFC 1404 Operational Statistics January 1993

  bw-value       ::= <actual bandwidth value>
  bw-sort        ::= "bps" | "Kbps" | "Mbps" | "Gbps" | "Tbps"
  proto-type     ::= "IP" | "DECNET" | "X.25" | "CLNS"
  proto-addr     ::= <network-address depending on proto-type>
  timezone       ::= <"+" | "-"><00 | ... | 12><00 | 30>
  tag-table      ::= <tag><FS><tag-class><FS><variable-field>
                     [<FS><variable-field>]
  tag-class      ::= "total" | "peak"
  variable-field ::= <variable-name> <FS> <initial-polling-period><FS>
                     <aggregation-period>
  tag            ::= <ascii-string>
  variable-name  ::= <ascii-string>
  initial-polling-period ::= <digit>[<digit>]
  aggregation-period     ::= <digit>[<digit>]
 The network name is a human readable string indicating to which
 network the logged data belong.
 The routername is the fully qualified name relevant for the network
 architecture where the router is installed.
 The linkname is a human readable string indicating the the
 connectivity of the link where from the logged data is gathered.
 The bandwidth should be the numerical value followed by the sort
 being used. Valid sorts are bps, Kbps, Mbps, Tbps.
 The prototype filed describes to which network architecture the
 interface being logged is connected. Valid types are IP, DECNET, X.25
 and CLNP.
 The network address is the unique numeric address of the interface
 being logged. The actual form of this address is dependent of the
 protocol type as indicated in the proto-type field. For Internet
 connected interfaces the "three-dot" notation should be used.
 The time-zone indicates the timedifference that should be added to
 the timestamp in the datasection to give the local time for the
 logged interface.
 The tag-table lists all the 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 one separate tag in the table has to be used for
 each period.

Stockman [Page 15] RFC 1404 Operational Statistics January 1993

 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 being processed the actual
 period covered may have changed as compared to the initial polling
 period and this should be noted in the aggregation period field.  The
 initial polling period and aggregation period should be given in
 seconds.
 As aggregation also means the computation of the max value for the
 previously polled data, the aggregation process have to extend the
 tag table to include these maximum values. This could be done in
 different ways. The variable field for the aggregated variables is
 extended to also include the peak values from the previous period.
 Another possibility is to create new tags for the peak values. To be
 able to differentiate between polled raw data, aggregated total and
 aggregated peak values some kind of unique naming of such entities
 has to be implemented.

6.1.3 The Data Section

  data-section    ::= "BEGIN_DATA"<FS>
                      <data-field><LF>
                      "END_DATA"
  data-field      ::= <timestamp><FS><tag><FS>
                      <poll-delta><FS><delta-val>
                      [<FS><delta-val>]
  poll-delta  ::= <digit> [<digit>]
  tag         ::= <ascii-string>
  delta-value ::= <digit> [<digit>]
  timestamp   ::= <year><month><day><hour><minute><second>
  year        ::= <digit><digit><digit><digit>
  month       ::= 01 | ... | 12
  hour        ::= 00 | ... | 23
  minute      ::= 00 | ... | 59
  second      ::= 00 | ... | 59
  digit       ::=  0 | ... | 9
 The datafield contains the polled data from a set of variables as
 defined by the corresponding tag field. 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.

Stockman [Page 16] RFC 1404 Operational Statistics January 1993

6.2 Storage Requirement Estimations

 The header sections are not counted in this example.  Assuming the
 the maximum polling intensity is used for all the 12 recommended
 variables and assuming the size in ascii of each variable is 8 bytes
 will give the below calculations based on one year of storing and
 aggregating statistical data.
 Assuming that data is saved according to the below scheme
      1 minute non-aggregated           saved 1 day.
      15 minute aggregation period      saved 1 week.
      1 hour aggregation period         saved 1 month.
      1 day aggregation period          saved 1 year.
 this will give:
 Size of one entry for each aggregation period:
                               Aggregation periods
                    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
  Total entry size   131         242         353        464
 For each day 60*24 = 1440 entries with a total size of 1440*131 = 187
 Kbytes.
 For each weak 4*24*7 = 672 entries are stored with a total size of
 672*242 = 163 Kbytes
 For each month 24*30 = 720 entries are stored with a total size of
 720*353 = 254 Kbytes
 For each year 365 entries are stored with a total size of 365*464 =
 169 Kbytes.
 Grand total estimated storage for during one year = 773 Kbytes.

Stockman [Page 17] RFC 1404 Operational Statistics January 1993

7. Report Formats

 This section suggest some report formats and defines the metrics to
 be used in such reports.

7.1 Report Types and Contents

 There is the longer term needs for monthly and yearly reports showing
 the long term tendencies in the network. There are the short term
 weekly reports giving indications on the medium term changes in the
 network behavior which could serve as input in the medium term
 engineering approach.  Finally there is the daily reports giving
 instantaneous overviews needed in the daily operations of a network.
 These reports should give information on:
    Offered Load              Total traffic at external interfaces.
    Offered Load              Segmented by "Customer".
    Offered Load              Segmented protocol/application.
    Resource Utilization      Link/Router.

7.2 Contents of the Reports

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
 The intention 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

  Metric categories: input  octets  per customer
                     output octets  per customer
                     input  packets per customer
                     output packets per customer
 The recommendation is here 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 n'th ranked

Stockman [Page 18] RFC 1404 Operational Statistics January 1993

 customers.
 The definition of what should be meant by a customer has to be done
 locally at the site where the statistics are being gathered.
 The cumulative could be useful as an overview of how the traffic is
 distributed among users since it enables to quickly pick off what
 fraction of of the traffic comes from what number of "users."
 A method of displaying both average and peak-behaviors in the same
 bar-diagram 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

7.2.3.1 Utilization as Maximum Peak Behavior

 The 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 loading in network variation. 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 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. Using above
 recommended polling period of 10 minutes this will hence be
 sufficient to capture variations in utilizations.
 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 was totals on hourly basis then the peak would be the maximum
 10 minutes period for each hour.
 By combining the average and the maximum values for a certain
 timeperiod it will 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

 Another way of visualizing line utilization is to put the 10 minutes
 samples in a histogram showing the relative frequency among the
 samples vs. the load.

Stockman [Page 19] RFC 1404 Operational Statistics January 1993

8. Considerations for Future Development

 This memo is the first effort in formalizing a common basis for
 operational statistics. One major guideline in this work has been to
 keep the model simple to facilitate for vendors and NOCs to easily
 integrate this model in their operational tools.
 There are, however, some ideas that could be progressed further to
 expand the scope and usability of the model.

8.1 A Client/Server Based Statistical Exchange System

 A possible way of 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
 should install a server who 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 is received by the client they could
 be processed and presented by a variety of tools needed. One
 possibility is to have an X-Window based tool that displays defined
 diagrams from data, supporting such types of diagrams being feed into
 the X-window tool directly from the statistical server. Another
 complementary method would be to generate PostScript output to be
 able to print the diagrams. In all cases there should be the
 possibility to store the retrieved data locally for later processing.

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 being
 available in the Internet Standard MIB. To facilitate for such
 metrics to be part of 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

 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

Stockman [Page 20] RFC 1404 Operational Statistics January 1993

 achieve such data could be to define a NNStat MIB or to include such
 variables in the above suggested operational statistics MIB subtree.

APPENDIX A

  Some formulas for statistical aggregation
  The following naming conventions are being used:
      For poll values poll(n)_j
      n = Polling or aggregation period
      j = Entry number
  poll(900)_j is thus the 15 minute total value.
      For peak values peak(n,m)_j
      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.
  Assume a polling over 24 hour period giving 1440 logged entries.
  =========================
  Without any aggregation we have
      poll(60)_1
      ......
      poll(60)_1439
  ========================
  15 minute aggregation will give 96 entries of total values
      poll(900)_1
      ....
      poll(900)_96
                    j=(n+14)

Stockman [Page 21] RFC 1404 Operational Statistics January 1993

      poll(900)_k = SUM  poll(60)_j  n=1,16,31,...1425
                    j=n              k=1,2,....,96
     There will also be 96 1 minute peak values.
                      j=(n+14)
     peak(900,60)_k = MAX poll(60)_000j  n=1,16,31,....,1425
                      j=n                k=1,2,....,96
  =======================
  Next aggregation step is from 15 minute 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
  and 24 1 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
  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
  ===================
  Next aggregation step is from 1 hour to 24 hour
  For each day with 1440 entries as above this will give
                      j=(n+23)

Stockman [Page 22] RFC 1404 Operational Statistics January 1993

      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.........
          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,........
          which gives the busiest 15 minute period over 24 hours.
                           j=(n+23)
      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.
  ===================
 There will probably be a difference between the three peak values in
 the final 24 hour aggregation. Smaller peak period will give higher
 values than longer, i.e., if adjusted to be numerically comparable.
  poll(86400)/3600 < peak(86400,3600) < peak(86400,900)*4
         < peak(86400,60)*60

Stockman [Page 23] RFC 1404 Operational Statistics January 1993

APPENDIX B

  An example
  Assuming below data storage:
  BEGIN_DEVICE
      ....
     UNI-1,total,ifInOctet,      60, 60,ifOutOctet,      60, 60
     BRD-1,total,ifInNUcastPkts,300,300,ifOutNUcastPkts,300,300
     ....
  which gives
  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
     ...
  Aggregation to 15 minutes gives
  BEGIN_DEVICE
      ....
      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
      ....
  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))
  which gives
  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

Stockman [Page 24] RFC 1404 Operational Statistics January 1993

     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
     ......
  Next aggregation step to 1 hour generates:
  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
  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))
  which gives
  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
     ......
  Finally aggregation step to 1 day generates:
  UNI-1,total,ifInOctet,60,86400,ifOutOctet,60,86400

Stockman [Page 25] RFC 1404 Operational Statistics January 1993

  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
  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))
  which gives
  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
     ......

Stockman [Page 26] RFC 1404 Operational Statistics January 1993

Security Considerations

 Security issues are discussed in Section 2.4.

Author's Address

 Bernhard Stockman
 NORDUnet/SUNET NOC
 Royal Institute of Technology
 Drottning Kristinas Vag 37B
 S-100 44 Stockholm, Sweden
 Phone:  +46 8 790-6519
 Fax  :  +46 8 241-179
 Email:  boss@sunet.se

Stockman [Page 27]

/data/webs/external/dokuwiki/data/pages/rfc/rfc1404.txt · Last modified: 1993/01/20 01:09 (external edit)