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
P. Ananthasankaran
June 24, 2017

Performance Measurement Models


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.

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Table of Contents

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 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:

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.

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.
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.

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.
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:

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.

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:

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.

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.

         +-------+              +-------+
         |       |              |       |
         |       +--------------+       |
         |       | 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].

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", Internet-Draft draft-ietf-ippm-alt-mark-04, 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
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