A Model for Common Operational Statistics

Network Working Group                                         M. Lambert
Request For Comments: 1857              Pittsburgh Supercomputing Center
Obsoletes: 1404                                             October 1995
Category: Informational

               A Model for Common Operational Statistics

Status of this Memo

   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.

Abstract

   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.

Acknowledgements

   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.

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 ..................................... 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
   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

1.  Introduction

   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.

   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.

   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 interchange format to facilitate the usage of these
        data by common presentation tools and

   4)   Common presentation formats.

   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.)

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)      !
                        !-------------------!

   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.

   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.

   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.

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 and

   3)   The definition of methods and formats for generating reports.

   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.

   One basic idea of 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

   Here the value is 0.

   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.

   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.

2.2.  Format for Storing Collected Data

   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.

2.3.  Reports

   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.

   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.

2.4.  Security Issues

   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.

   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.

3.  Categorization of Metrics

3.1.  Overview

   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.

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:

    o Utilization metrics
    o Performance metrics
    o Availability metrics
    o Stability metrics

3.2.1.  Utilization Metrics

   This category describes different aspects of the total traffic being
   forwarded through the network. Possible metrics include:

    o Total input and output packets and octets
    o Various peak metrics
    o Per protocol and per application metrics

3.2.2.  Performance Metrics

   These metrics relate to quality of service issues such as delays and
   congestion situations. Possible metrics include:

    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

3.2.3.  Availability Metrics

These metrics could be viewed as gauging long term accessibility on
different protocol layers. Possible metrics include:

    o Line availability as percentage uptime
    o Route availability
    o Application availability

3.2.4.  Stability Metrics

   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:

    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

3.3.  Categorization Based on Availability of Metrics

   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.

   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).

   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.

3.3.1.  Per Interface Variables Already in Internet Standard MIB (thus
        easy to retrieve)

           ifInUcastPkts   (unicast packets in)
           ifOutUcastPkts  (unicast packets out)
           ifInNUcastPkts  (non-unicast packets in
           ifOutNUcastPkts (non-unicast packets out)
           ifInOctets      (octets in)
           ifOutOctets     (octets out)
           ifOperStatus    (line status)

3.3.2.  Per Interface Variables in Internet Private Enterprise MIB (thus
        could sometimes be retrievable)

           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 Known 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
           system uptime
           poll delta time
           reboot count

3.3.6.  Metrics not Retrievable with SNMP

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

3.4.  Recommended Metrics

   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.

   The following metrics from the Internet Standard MIB were chosen as
   being desirable and reasonable:

   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)

4.  Polling Frequencies

   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.

4.1.  Variables Needing High Resolution Polling

   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:

   for each interface

           ifInOctets      (octets in)
           ifOutOctets     (octets out)
           ifInUcastPkts   (unicast packets in)
           ifOutUcastPkts  (unicast packets out)

   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.

4.2.  Variables not Needing High Resolution Polling

   The remainder 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)

   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.

5.  Pre-Processing of Raw Statistical Data

5.1.  Optimizing and Concentrating Data to Resources

   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
   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.

   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.

5.2.  Aggregation of Data

   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.

   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.

   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.

   Details of the layout of the aggregated entries in the data file are
   given in section 6.1.3.

   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

6.  Storing of Statistical Data

   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.

   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.

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.

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 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.

   Here are some examples of valid data files:

       <label-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.

       <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
   the tag-name    lists in their label sections.

      <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.

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>

   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.

   start-time       ::= <time-string>
   stop-time        ::= <time-string>
   data-file-name   ::= <ASCII-string>

   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>

   The start-time and stop-time are specified in UTC.

   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

   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]

   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

   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>

   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 link-name is a human readable string indicating the connectivity
   of the link where from the logged data is gathered.

   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 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 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.

   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
   lists.

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>

   delta-val-list   ::= LEFT <delta-val> [ <FS> <delta-val> ] RIGHT

   poll-delta       ::= <integer>
   delta-val        ::= <integer>

   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.

   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.

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.

   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,

   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 = 189
   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 year 365 entries are stored with a total size of 365*464 =
   169 kB.

   Grand total estimated storage for during one year = 775 kB.

7.  Report Formats

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

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
   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:

         Offered Load              Total traffic at external interfaces
         Offered Load              Segmented by "Customer"
         Offered Load              Segmented protocol/application.

         Resource Utilization      Link/Router

7.2.  Content 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 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

       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 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 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

7.2.3.1.  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.

   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.

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.

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.

   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

   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.

   The client/server approach is discussed further by Henry Clark in
   RFC 1856.

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.

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.

APPENDIX A

Some formulas for statistical aggregation

   The following naming conventions are 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)_1440

       ========================

       15 minute aggregation will give 96 entries of total values

           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


          There will also be 96 one-minute peak values.

                           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.

                              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.

                             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

       ===================

   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)
           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. 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

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);
      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:

   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]
   }
      ...

   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);
      ...


Security Considerations

   Security issues are discussed in Section 2.4.

Author's Address

   Michael H. Lambert
   Pittsburgh Supercomputing Center
   4400 Fifth Avenue
   Pittsburgh, PA  15213
   USA

   Phone: +1 412 268-4960
   Fax:  +1 412 268-8200
   EMail: lambert@psc.edu