Packet Loss measurement Model
draft-bhaprasud-ippm-pm-02
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| Authors | Bharat M Gaonkar , Giuseppe Fioccola , Qin Wu , Praveen Ananthasankaran | ||
| Last updated | 2017-03-05 | ||
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draft-bhaprasud-ippm-pm-02
IPPM Working Group B. M Gaonkar
Internet-Draft S. Jacob
Intended status: Standards Track Juniper
Expires: September 6, 2017 G. Fioccola
Telecom Italia
Q. Wu
Huawei
P. Ananthasankaran
Nokia
March 5, 2017
Packet Loss measurement Model
draft-bhaprasud-ippm-pm-02
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.
Status of This Memo
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This Internet-Draft will expire on September 6, 2017.
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to this document. Code Components extracted from this document must
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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. Loss Measurement Models . . . . . . . . . . . . . . . . . . . 6
4.1. Complete data measurement (Monitoring all the traffic) . 6
4.2. Color based data measurement . . . . . . . . . . . . . . 6
4.3. COS based Data measurement . . . . . . . . . . . . . . . 7
4.4. COS and color based Data measurement . . . . . . . . . . 7
5. Active and Passive performance measurements . . . . . . . . . 7
6. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 8
7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 9
8. Security Considerations . . . . . . . . . . . . . . . . . . . 9
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 9
10.1. Normative References . . . . . . . . . . . . . . . . . . 9
10.2. Informative References . . . . . . . . . . . . . . . . . 9
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 based on enhanced QoE and SLAs. The lack of
adequate 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 define loss measurement
matrix models for multiple customer service flows on the network.
Each customer service flow is corresponding to an interesting subset
of the same packet stream. The customer or packet stream can be
identified by a list of source or destination prefixes, or by ingress
or egress interfaces.
The network would be provisioned with multiple services(e.g., real
time service, interactive service) having different SLAs(e.g.,
bandwidth constraint or end packet loss constraint for the end to end
path) based on the customers' requirement. This models aims at
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computing Loss measurement for these services (belonging to the same
customer)independently for each defined SLA matrixes.
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 SLA for each service.
This draft uses the class-of-service model and color based model for
any given network to define the packet loss measurement for various
services with the different SLA requirements.
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.
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].
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.
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Time Series Data Time Series Data is a sequence of data points with
time stamps. The data points are limited to loss 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. 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. 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.
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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. Loss 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. 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.
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 measurement.One example of Color based data
measurement is to count two type of color based traffic:
Count all committed traffic between the 2 end-point for loss
measurement.
Count all Excess traffic which is beyond the committed traffic for
the specific network.
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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.
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 in the network.Based on the class of
traffic in the network the transmitted and received packets are
counted to calculate the loss measurement.
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.
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 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.
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.
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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.
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+--+ +--+
| | | |
+--+ +--+
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-idr-ls-distribution] and [RFC4271].
9. IANA Considerations
IANA maintains the registry for the TLVs. BGP TE Performance TLV
will require one new type code per TLV defined in this document.
10. References
10.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", March 1997.
10.2. Informative References
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[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
Qin Wu
Huawei
101 Software Avenue, Yuhua District
Nanjing, Jiangsu 210012
China
Email: bill.wu@huawei.com
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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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