Packet Loss measurement Model
draft-bhaprasud-ippm-pm-01
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draft-bhaprasud-ippm-pm-01
Network Working Group Bharat M Gaonkar
Internet Draft Sudhin Jacob
Intended status: Experimental Juniper Networks
Expires: July 2017 Giuseppe Fioccola
Telecom Italia
Qin Wu
Huawei Technologies
Praveen Ananthasankaran
Nokia
January 27, 2017
Packet Loss measurement Model
draft-bhaprasud-ippm-pm-01.txt
Abstract
This document defines the loss measurement matrix models for service
level packets on the network which can be implemented in different
kind of network scenarios.
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Expires July 4, 2017 [Page 1]
Table of Contents
1. Introduction ..................................................3
2. Terminologies..................................................4
3. Loss Measurement Models........................................5
3.1. Complete data measurement....................................5
3.2. Color based data measurement.................................6
3.3. COS based Data measurement...................................6
3.4. COS and color based Data measurement.........................6
4. Active and Passive performance measurements.....................6
5. Use Case .......................................................7
Appendix A Appendix ...............................................9
Authors' Addresses ................................................9
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1. Introduction
Today, Performance monitoring is a key technology to strengthen
service offers based on enhanced QoE and SLAs. The draft aims to
define performance monitoring loss measurement matrix models for
service level packets on the network.
The network would be provisioned with multiple services having
different SLAs based on the customers' requirement.This models aims
at computing Loss measurement for these services independently for
each defined SLA matrixes.
The class-of-service and packet color classification defined in the
network drives the SLA factors and the implementation to achieve
these SLAs.This draft uses the class-of-service model and color
based model for any given network to define the packet loss measurement
for the different SLAs.
The proposed matrix models is suitable mainly for passive performance
measurements but can be considered for active and hybrid performance
measurements as well.
This solution models loss measurement in different kinds of network
scenarios. The different models explained here will help to analyse
packet loss pattern, analyze the network congestion in a better way
and model the network in a better way.
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 measurement compulation. The
underlying technology could be an MPLS Loss measurement, or based
loss measurement or an IP based loss measurement.
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2. Terminologies
Color Identifier: It is used to identify the color that applies to the
data packet.
COS Identifier: It is used to identify the COS that applies to the
data packet.
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.
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 same
defined SLA matrix.The SLA matrix is an array of Color identifier attribute
and COS identifier attribute.
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3. Loss Measurement Models
3.1. Complete data measurement
This model uses the complete data traffic between the 2
end-points to compute loss measurement. This will result in
computation of loss 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 loss measurment between one
endpoint to other.
3.2. 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 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.
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 be using 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 is used in core networks where the aggregated traffic has
differential priority and loss measurement can be computed on the
committed traffic which is guaranteed in the network when compared
with excess traffic which could be dropped based on network load and
provisioning.
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3.3. COS based Data measurement
This model uses the data traffic in the network which is flowing in a
specific COS to measure the loss in the network.Based on the class
of traffic in the network the transmitted and received packets are
counted to calculate the loss measurement.
Cos is differentiated from Color as COS treats the network streams
with different COS identifier as different classes of traffic
whereas color differentiates a set of packets with different color.
Primary use of this kind of loss measurement is to measure loss
measurement for a specific service which has strict SLAs. The
service could be a point-to-point layer2 service, an MPLS based
service.
3.4. COS and color based Data measurement
This model uses a combination of both Color based data measurement
and Cos based data measurement. Packets are counter 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 violates the SLA.This will count the packet for each
SLA and color separately.
4. 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.
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5. Use Case
+-------+ +-------+
| | | |
P2P service +---------------+ |
| | | |
+-------+ +-------+
Router A Router B
Figure 1
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 calss 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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6. 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.
7. Security Considerations
NA
8.IANA Considerations
NA
9. References
9.1 Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<http://www.rfc-editor.org/info/rfc2119>.
9.2 Informative References
[I-D.ietf-ippm-alt-mark]
Capello, A., Cociglio, M., Fioccola, G., Castaldelli, L.,
and A. Bonda, "Alternate Marking method for passive
performance monitoring", draft-ietf-ippm-alt-mark-00 (work
in progress), July 2016.
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Appendix A. Appendix
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: sjacob@juniper.net
Giuseppe Fioccola
Telecom Italia
Via Reiss Romoli, 274
Torino 10148 Italy
Email: giuseppe.fioccola@telecomitalia.it
Qin Wu
Huawei Technologies Co., Ltd.
101 Software Avenue, Yuhua District
Nanjing, Jiangsu 210012
China
Phone: +86-25-56629042
EMail: sunseawq@huawei.com
Praveen Ananthasankaran
Nokia
Manyata Embassy Tech Park,
Silver Oak (Wing A), Outer Ring Road,
Nagawara, Bangalore-560045
Email: praveen.ananthasankaran@nokia.com
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