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IPPM Working Group                                          B. M Gaonkar
Internet-Draft                                                  S. Jacob
Intended status: Standards Track                                 Juniper
Expires: December 26, 2017                                   G. Fioccola
                                                          Telecom Italia
                                                                   Q. Wu
                                                                  Huawei
                                                      P. Ananthasankaran
                                                                   Nokia
                                                           June 24, 2017


                     Performance Measurement Models
                       draft-bhaprasud-ippm-pm-03

Abstract

   This document defines the performance measurement models for service
   level packets on the network which can be implemented in different
   kind of network scenarios.  Based on the performance matrix, the
   analytics data can be pulled from a live network which is not
   possible at present.This can be used for self evolving networks.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
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   Drafts is at http://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on December 26, 2017.

Copyright Notice

   Copyright (c) 2017 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of



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   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions used in this document . . . . . . . . . . . . . .   3
   3.  Traffic Management Architecture . . . . . . . . . . . . . . .   5
     3.1.  Selection Process . . . . . . . . . . . . . . . . . . . .   5
     3.2.  Metering Process  . . . . . . . . . . . . . . . . . . . .   6
   4.  Performance Measurement Models  . . . . . . . . . . . . . . .   6
     4.1.  Complete data measurement (Monitoring all the traffic)  .   6
     4.2.  Color based data measurement  . . . . . . . . . . . . . .   7
     4.3.  CoS based Data measurement  . . . . . . . . . . . . . . .   7
     4.4.  CoS and Color based Data measurement  . . . . . . . . . .   8
   5.  Active and Passive performance measurements . . . . . . . . .   8
   6.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   8
   7.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   9
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   9.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  10
     9.1.  Normative References  . . . . . . . . . . . . . . . . . .  10
     9.2.  Informative References  . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Introduction

   Today performance monitoring or tracking of the performance
   experienced by customer traffic is a key technology to strengthen
   service offering and verify service level agreement between customers
   and service providers, perform troubleshooting.  The lack of adequate
   monitoring tools to detect an interesting subset of a packet stream,
   as identified by a particular packet attribute(e.g., commit rate or
   DSCP) and measure that packet loss drives an effort to design a new
   method for the performance monitoring of live traffic, possibly easy
   to implement and deploy.  The draft aims to provide fine granularity
   loss, delay and delay variation measurement and define a performance
   measurement model on customer traffic based on a set of constraints
   that are associated with service level agreement such as cos
   attribute, color attribute.  Each customer traffic is corresponding
   to an interesting subset of the same packet stream.  The customer or
   a interesting packet stream can be identified by a list of source or
   destination prefixes, or by ingress or egress interfaces, combing
   with packet attributes such as DSCP or commit rate).Unlike Color and
   COS identification specified in MEF 23.1, this draft doesn't define



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   new Color and CoS identification mechanism, instead, it stick to
   color definition in [RFC2697] and [RFC2698] and COS definition in
   [RFC2474].

   The network would be provisioned with multiple services(e.g., real
   time service, interactive service) having different network
   performance criteria(e.g., bandwidth constraint or packet loss
   constraint for the end to end path) based on the customers'
   requirement.  This models aims at performing Loss, Delay and delay
   variation measurement for these services (belonging to the same
   customer)independently for each defined network performance criteria.

   The class-of-service and packet color classification defined in the
   network is a key factor to classify network traffic and drive traffic
   management mechanism to achieve corresponding network performance
   criteria for each service.  This draft uses the class-of-service
   model and color based model for any given network to define the
   performance measurement for various services with the different
   network performance criteria requirements.

   The proposed models is suitable mainly for passive performance
   measurements but can be considered for active and hybrid performance
   measurements as well.

   This solution models loss, delay an delay variation measurement in
   different kinds of network scenarios.  The different models explained
   here will help to analyse performance pattern, analyze the network
   congestion in a better way and model the network in a better way.
   For instance, Loss measurement is carried out between 2 end points.
   The underlying technology could be an active loss measurement or a
   passive loss measurement.

   Any loss measurement will require 2 counters:

   o  Number of packets transmitted from one end point.

   o  Number of packets received at the other end point.

   This draft explains the different ways to model the above data and
   get meaningful result for the loss, delay and delay variation
   measurement.  The underlying technology could be an MPLS performance
   measurement, or an IP based performance measurement.

2.  Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC2119 [RFC2119].



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   Observation Point  An Observation Point is a location in the network
      where data packets can be observed.  Examples include a line to
      which a probe is attached, a shared medium, such as an Ethernet-
      based LAN, a single port of a router, or a set of interfaces
      (physical or logical) of a router.

   Persistence Data Store  The persistence Data store is a scalable data
      store which collects time based data such as streaming data or
      time series data for network analytics.

   Time Series Data  Time Series Data is a sequence of data points with
      time stamps.  The data points are limited to loss, delay and delay
      variation measurement results in this document.

   Packet Stream  A Packet Stream denotes a set of packets from the
      Observed Packet Stream that flows past some specified point within
      the Metering Process.  An example of a Packet Stream is the output
      of the Selection Process.

   Packet Content  The Packet Content denotes the union of the packet
      header (which includes link layer, network layer, and other
      encapsulation headers) and the packet payload.

   Color Identifier:  It is used to identify the color that applies to
      the data packet.  Color identifier can be assigned to service
      level packet based on commit rate and excess rate set for the
      traffic.  For example, the service level packet will be set with
      "green" color if it is less than committed" rate; the Service
      Level packet will be set with "yellow" color if it is exceeding
      the"committed" rate but less than the "excess" rate.  The service
      frame will be set with "red" color if it is exceeding both the
      "committed" and "excess" rates.

   CoS Identifier:  It is used to identify the CoS that applies to the
      data packet.  CoS identifier can be assigned based on dot1p value
      in C-tag, or DSCP in IP header.

   Complete data measurement:  Complete data measurement is a data
      measurement method which monitors every packet and condense a
      large amount of information about packet arrivals into a small
      number of statistics.  The aim of "monitoring every packet" is to
      ensure that the information reported is not dependent on the
      application.

   Color based data measurement:  Color based data measurement is a data
      measurement method which monitors the data packet with the same
      color identifier.  Color identifier could be "green"
      color,"yellow" color and "red" color.



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   CoS based data measurement:  Color based data measurement is a data
      measurement method which monitors the data packet with the same
      CoS identifier.  COS identifier could be C-Tag Priority Code
      Point(PCP) or DSCP.

   CoS and Color based Data measurement:  CoS and Color based Data
      measurement is a data measurement method which monitors the data
      packet with the specific CoS Identifier and Specific Color
      Identifier as constraints.  The measurement results with CoS
      Identifier and Color Identifier constraints constitute a Network
      Performance matrix.

3.  Traffic Management Architecture

   A stream of packets is observed at an Observation Point of the source
   endpoint and destination endpoints.  Two observation points can also
   be placed at the same endpoint for node monitoring
   [I-D.ietf-ippm-alt-mark], i.e.,one is at ingress interface of the
   endpoint and the other is at the egress interface of the endpoint.  A
   Selection Process inspects each packet to determine whether or not it
   is to be selected for data analytics.  The Selection Process is part
   of the Metering Process, which constructs a report stream on selected
   packets as output, using the Packet Content, and possibly other
   information such as the arrival timestamp.  The report stream on
   selected packets will be stored in the persistence data store for
   real time data analysis or time sequence data analysis.

   The following figure indicates the sequence of the three processes
   (Selection, Metering, and Storing).

                            +-----------+                  +-----------+
                            |Persistence|                  |Persistence|
                            |Data Store |                  |Data Store |
             Src Endpoint   +-----^-----+     Dst Endpoint +------^----+
             +------------------+ |           +------------------+|
             | Metering Process | |           | Metering Process ||
   Observed  | +-----------+    | |           | +-----------+    ||
   Packet--->| | Selection |------+ Observed  | | Selection |    ||
   Stream    | | Process   |--------Packet--->| | Process   |-----+
             | +-----------+    |   Stream    | +-----------+    |
             +------------------+             +------------------+

3.1.  Selection Process

   This section defines the Selection Process and related objects.






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   Selection Process:  A Selection Process takes the Observed Packet
      Stream as its input and selects a subset of that stream as its
      output.

   Selection State:  A Selection Process may maintain state information
      for use by the Selection Process.  At a given time, the Selection
      State may depend on packets observed at and before that time, and
      other variables.  Examples include sequence numbers of packets at
      the input of Selectors,a timestamp of observation of the packet at
      the Observation Point,indicators of whether the packet was
      selected by a given Selector.

   Selector:  A Selector defines the action of a Selection Process on a
      single packet of its input.  If selected, the packet becomes an
      element of the output Packet Stream.

      The Selector can make use of the following information in
      determining whether a packet is selected:

      *  COS Identifier in the Packet Content;

      *  Traffic attribute such as Color identifier;

      *  Combination of CoS Identifier and Color Identifier

3.2.  Metering Process

   A Metering Process selects packets from the Observed Packet Stream
   using a Selection Process, and produces as output a Report Stream
   concerning the selected packets.

4.  Performance Measurement Models

4.1.  Complete data measurement (Monitoring all the traffic)

   This model uses the complete data traffic between the 2 end-points to
   compute loss measurement, delay and delay variation.  This will
   result in computation of loss, delay and delay variation measurement
   for the entire traffic in the network in one direction.  This is
   primarily used in cases of backbone traffic where traffic from
   different services are aggregated and send into the core network.
   This will count all the packet, this gives the overall measurment
   between one endpoint to other.








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4.2.  Color based data measurement

   This is same as the above section of "complete data measurement" with
   a minor difference, only monitoring the data packet with specific
   color identifier.

   In this model the packets are counted in the following Way: Count
   specific data traffic with different color identifier between 2 end
   points for loss, delay and delay variation measurement.  One example
   of Color based data measurement is to count two type of color based
   traffic:

   o  Count all committed traffic between the 2 end-point for loss
      measurement.

   o  Count all Excess traffic which is beyond the committed traffic for
      the specific network.

   o  The probe carries the time stamps, which can later be used for
      calculating the service outage.

   o  This method can be used for mapping the overall customer traffic
      along with EIR, based on the EIR provider can increase the
      bandwidth and charge him.

   When both of these are combined then it becomes the model for
   complete traffic as mentioned in the above section.

   In practice the Color of traffic can use any mechanism based on the
   network encapsulation.As long as the packets could be treated
   differently based on the underlying encapsulation this mechanism
   could be used.

   This can be used for measuring the whole traffic of the customer who
   dont want cos level measurement.Ideally this can be used for provider
   who extend bandwidth for small providers, point to point services
   etc.

4.3.  CoS based Data measurement

   This model uses the data traffic in the network which is flowing in a
   specific CoS to measure the loss, delay and delay variation in the
   network.  Based on the class of traffic in the network the
   transmitted and received packets are counted to calculate the packets
   transfered per service level.  The time stamp will be captured along
   with the packet count to measure the service down time.  This model
   measures the performance per service level.  This data can be stored
   on the routers which can be used to plot the live analytics.



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   Primary use of this kind of measurement is to measure packet loss
   delay and delay variation for a specific service which needs to meet
   network performance requirements.  The service could be a point-to-
   point layer2 service, an MPLS based service.

4.4.  CoS and Color based Data measurement

   This model uses a combination of both Color based data measurement
   and CoS based data measurement.  Packets are counted for a specific
   CoS with a specific Color.This can count both in profile packet which
   are green and yellow which are out profile packets.  This will not
   count the red packet which doesn't meet network performance
   requirements.The packets will be counted per service level with CIR
   and EIR along with time stamps to find the service outage and loss.
   The per service level counting for COS and color will give more
   granular level data for poloting service graph and if some service is
   continously exceeding the bandwidth this data can be used for
   charging the end customer for extra bandwidth usage or increase the
   bandwidth based on usage basis.

5.  Active and Passive performance measurements

   This model reinforces the use of well known methodologies for passive
   performance measurements.  A very simple, flexible and
   straightforward mechanism is presented in [I-D.ietf-ippm-alt-mark].
   The basic idea is to virtually split traffic flows into consecutive
   batches of packets:each block represents a measurable entity
   unambiguously recognizable thanks to the alternate marking.  This
   approach, called Alternate Marking method, is efficient both for
   passive performance monitoring and for active performance monitoring.
   Most of the applications requires passive packet loss measurement for
   a better accuracy.  Instead, in same cases, it is desirable to have
   only active delay measurements (e.g TWAMP or OWAMP), because it is
   enough.

6.  Use Cases

   Consider a provider running point to point service between router A
   and B for his customer "X".Customer "X" has voice traffic which
   requires special treatment,then he requires attention for database
   traffic.  The customer "X" has SLA with the provider.  Now the
   challenge faced by the provider is how to measure the traffic of
   customer "X" for each class and calculate the bandwidth, moreover the
   provider has to see whether the "X" is sending traffic which is
   exceeding the level so that he can make tariff accordingly.  This
   problem is solved by the above models which can measures the packet
   for each class of traffic and tabulates the data.  Later point of
   time this data can be pulled for evaluation.



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            +-------+              +-------+
            |       |              |       |
            |       +--------------+       |
            |       | P2P service  |       |
            +-------+              +-------+
             Router A               Router B

                               Figure 1: P2P


   The same considerations can be applicable in a multipoint to
   multipoint scenario (e.g.  VPN or Data Center interconnections).  In
   this case Customer "X" has multiple ingress endpoints and multiple
   egress endpoints.  The proposed matrix model is composed by the
   number of flows of "X" in the multipoint scenario and by class-of-
   service and color classification.  So the SLA matrix is a reference
   for the analysis and evaluation phase.

            +--+                      +--+
            |  |                      |  |
            +--+                      +--+
          Router A1                  Router B1
            +--+                      +--+
            |  |     MP2MP service    |  |
            +--+                      +--+
          Router A2                  Router B2
             .                          .
             .                          .
             .                          .
            +--+                      +--+
            |  |                      |  |
            +--+                      +--+
          Router An                  Router Bn

                              Figure 2: MP2MP


7.  Acknowledgements

   We would like to thank Brian Trammell for giving us the opportunity
   to present our draft.We would like to thank Greg Mirsky for the
   comments.

8.  Security Considerations

   This document does not introduce security issues beyond those
   discussed in [I-D.ietf-ippm-alt-mark].




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9.  References

9.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", March 1997.

9.2.  Informative References

   [I-D.ietf-ippm-alt-mark]
              Fioccola, G., Capello, A., Cociglio, M., Castaldelli, L.,
              Chen, M., Zheng, L., Mirsky, G., and T. Mizrahi,
              "Alternate Marking method for passive performance
              monitoring", draft-ietf-ippm-alt-mark-04 (work in
              progress), March 2017.

Authors' Addresses

   Bharat M Gaonkar
   Juniper Networks
   1133 Innovation Way
   Sunnyvale, California  94089
   USA

   Email: gbharat@juniper.net


   Sudhin Jacob
   Juniper Networks
   1133 Innovation Way
   Sunnyvale, California  94089
   USA

   Email: gbharat@juniper.net


   Giuseppe Fioccola
   Telecom Italia
   Via Reiss Romoli, 274
   Torino  10148
   Italy

   Email: giuseppe.fioccola@telecomitalia.it








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   Qin Wu
   Huawei
   101 Software Avenue, Yuhua District
   Nanjing, Jiangsu  210012
   China

   Email: bill.wu@huawei.com


   Praveen Ananthasankaran
   Nokia
   Manyata Embassy Tech Park, Silver Oak (Wing A),
   Outer Ring Road, Nagawara
   Bangalore  560045
   Inda

   Email: praveen.ananthasankaran@nokia.com


































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