Traffic Engineering Working Group Wai Sum Lai
Internet Draft AT&T Labs
Document: <draft-ietf-tewg-measure-06.txt>
Category: Informational Richard W. Tibbs
Oak City Networks &
Solutions
Steven Van den Berghe
Ghent University/IMEC
July 2003
Requirements for Internet Traffic Engineering Measurement
Status of this Memo
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Abstract
In this document, we identify requirements for supporting the
traffic engineering of IP networks. Requirements for traffic
measurement in service provider environments are presented and
justified, and related issues are discussed.
Highlights of requirements are:
1. To aid network dimensioning, mechanisms to collect node-pair-
based traffic data are required to facilitate the derivation of per-
service-class traffic matrix statistics.
2. For service assurance, the use of higher-order statistics is
required.
3. To preserve representative traffic detail at manageable sample
volumes, packet-sampled measurements are required.
4. To manage large volumes of measured data, use of bulk transfer
and filtering/aggregation mechanisms are required.
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Table of Contents
Status of this Memo................................................1
Abstract...........................................................1
Conventions used in this document..................................3
1. Introduction....................................................3
2. Conclusions and Recommendations.................................4
3. Requirements for TE Measurement and Its Uses....................4
3.1 Requirements for Traffic characterization......................5
3.2 Requirements for Network monitoring............................5
3.3 Requirements for Traffic Matrix Statistics.....................6
3.4 Requirements for Performance Monitoring........................6
3.5 Requirements for Path Characterization.........................7
4. Requirements Summary for TE Measurement Types...................7
4.1 Measurement types related to traffic or performance............8
4.2 Measurement types related to resource usage....................8
5. Requirements for a TE Measurement Information Model.............9
6. Measurement Definitions........................................11
6.1 Active, passive measurements..................................11
6.2 Route, path...................................................11
6.3 Throughput, traffic volume....................................11
APPENDICES........................................................12
APPENDIX A........................................................12
A. Measurement Bases..............................................12
A.1 Flow-based....................................................14
A.2 Interface-based, link-based, node-based.......................14
A.3 Node-pair-based...............................................15
A.4 Path-based....................................................15
APPENDIX B........................................................16
B. Measurement Entities...........................................16
B.1 Entities related to traffic and performance...................16
B.2 Entities related to establishment of connection or path.......18
APPENDIX C........................................................18
C. Packet Sampling and Estimation.................................18
C.1 Packet Sampling...............................................19
C.2 Sampling Issues...............................................19
C.3 Engineering methods for statistical estimation of measures....20
APPENDIX D........................................................20
D. Read-Out Periods...............................................20
D.1 Data Reduction................................................21
D.2 Measurement Interval..........................................21
D.3 Summarization.................................................21
APPENDIX E........................................................22
E. Time Scales for Network Operations.............................22
APPENDIX F........................................................23
F. Use of Traffic Measurement for Traffic control.................23
16. Security Considerations.......................................23
17. References....................................................23
18. Intellectual Property Statement...............................26
19. Acknowledgments...............................................26
20. Author's Addresses............................................26
Full Copyright Statement..........................................27
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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 RFC-2119.
1. Introduction
In this document, we identify requirements for supporting IP network
traffic engineering (TE) [1]. Requirements for traffic measurement
in service provider environments are presented and justified, and
related issues are discussed. To aid network dimensioning,
mechanisms to collect node-pair-based traffic data are required to
facilitate the derivation of per-service-class traffic matrix
statistics. For service assurance, the use of higher-order
statistics is required. To preserve representative traffic detail
at manageable sample volumes, packet-sampled measurements are
required. To manage large volumes of measured data, use of bulk
transfer and filtering/aggregation mechanisms are required.
Requirements for TE measurement are motivated by the needs for
consistency, precision, and effectiveness of the overall TE
function. TE includes measurements, forecasting, planning,
dimensioning, control, and performance monitoring. TE measurement
plays a key role to assure the quality of the other aspects of TE.
Uses of traffic measurement in traffic characterization, network
monitoring, and traffic control are first described. Depending on
the network operations to be performed in these tasks, three
different time scales can be identified, ranging from months,
through days or hours, to minutes or less. To support these
operations, traffic measurement must be able to capture accurately,
within a given confidence interval, the traffic variations and peaks
without degrading network performance and without generating an
immense amount of data. As one consequence of the need to avoid
network performance degradation, specification of a suitable read-
out period (i.e., summarization or aggregation interval) is
essential.
Traffic measurement can be performed on the basis of flows,
interfaces, links, nodes, node-pairs, or paths. Based on these
objects, different measurement entities can be defined, such as
traffic volume, average holding time, bandwidth availability,
throughput, delay, delay variation, packet loss, and resource usage.
Using these measured traffic data, in conjunction with other network
data such as topological data and router configuration data, traffic
matrix and other relevant statistics can be derived for TE purposes.
IP multicast traffic measurement is not explicitly addressed in this
document. Nonetheless, given additional elaboration on tree-based
measurement principles, most of the considerations for different
measurement types (see Appendices A and B) could be applied to IP
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multicast traffic. Such elaboration may be dealt with in a
subsequent document for specific IP multicast-inferred Internet
traffic measurement.
Relevant work done in measurements by other standards organizations
will be applied or adapted, and references to them will be made.
These include, in particular,
. IP Performance Metrics (IPPM) Working Group of the IETF: its
framework document [2] and the associated documents on individual
metrics [3, 4, 5, 6, 7, 8, 9, 10, 11]
. ITU-T: Recommendation I.380/Y.1540 [12] and Recommendation Y.1541
[13]
2. Conclusions and Recommendations
Requirements are given in this document for traffic metrics needed
for successful TE. Principles of best practice in traffic
characterization and performance characterization are described in
the Appendices. For interoperable compatibility and consistency,
requirements for traffic measurement recommended for standardization
include:
(1) Requirements for specific TE measurements
. Node-pair-based traffic data to derive per-service-class traffic
matrix statistics, including statistics of carried load and
offered load (Sections 3.3 and Appendix A)
. Statistics of achieved performance and throughput (Section 3.4)
. A standardized method to detect and record label binding changes
for LDP-signaled label-switched paths, at the ingress-egress pair
level (Section 3.5)
(2) Requirements for traffic data collection methods
. Standardization of measurement definitions and sampling methods,
to achieve uniformity across vendors and operators, and to
preserve sufficient traffic detail at manageable sample volumes
(Section 6 and Appendix C)
. Higher-order statistics to facilitate service assurance (Section
3.1)
. Offline bulk file transfer and standardized filtering/aggregation
mechanisms to manage large volumes of measured traffic data
(Section 5 and Appendix D)
. Linkage between policy mechanisms and TE measurement, possibly
triggered by a measurement-driven event notification (Section 5)
. Standardization of information models for TE measurement (Section
5)
3. Requirements for TE Measurement and Its Uses
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TE measurement is used to collect traffic data for the following
purposes:
. Traffic characterization
. Network monitoring
. Traffic matrix Statistics
. Performance monitoring
. Path characterization
3.1 Requirements for Traffic characterization
. Requirement 1: Standardization of higher-order statistics to
facilitate service assurance.
. Requirement 2: Identifying traffic patterns, particularly traffic
peak patterns, and their variations in statistical analysis; this
includes developing traffic profiles to capture daily, weekly, or
seasonal variations.
. Requirement 3: Determining traffic distributions in the network on
the basis of flows, interfaces, links, nodes, node-pairs, paths,
or destinations. (These bases are discussed in Appendix A.)
. Requirement 4: Estimation of the traffic load according to service
classes in different routers and the network.
. Requirement 5: Observing trends for traffic growth and forecasting
of traffic demands.
For example, TE can use measurements to determine the statistical
moments of a traffic flow. As suggested in [14], given the time
series of packet arrivals, a suitable parametric stochastic model
based on the mean and variance of the time series can be
constructed. This traffic model is then used in the ensuing phases
of TE, such as link dimensioning to meet service objectives.
3.2 Requirements for Network monitoring
. Requirement 1: Determining the operational state of the network,
including fault detection.
. Requirement 2: Monitoring the continuity and quality of network
services, to ensure that QoS/CoS objectives are met for various
classes of traffic, to verify the performance of delivered
services, or to serve as a means of sectionalizing performance
issues seen by a customer. [Note 1. QoS reflects the performance
perceivable by a user of a service, while CoS (class of service)
is used by a service provider for internal design and operation of
a network.] [Note 2. Mechanisms for monitoring service
continuity may be service-specific and are not discussed here.]
. Requirement 3: Evaluating the effectiveness of TE policies, or
triggering certain policy-based actions (such as alarm generation,
or path preemption) upon threshold crossing; this may be based on
the use of performance history data.
. Requirement 4: Verifying peering agreements between service
providers by monitoring/measuring the traffic flows over
interconnecting links at border routers (note that peers are in
general not willing to divulge detailed traffic picture inside
their autonomous systems); this includes the estimation of inter-
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and intra-domain traffic, as well as originating, terminating, and
transit traffic that are being exchanged between peers.
An example of using TE measurements in this area might be monitoring
packet loss rates at various points in a network to detect apparent
link failure. Another example is observing traffic at peering
points to ensure that peering agreements are met.
3.3 Requirements for Traffic Matrix Statistics
Requirement 1
Standardization of node-pair-based traffic data to derive per-
service-class traffic matrix statistics, including statistics of
carried load and offered load.
An important set of data for TE is point-to-point or point-to-
multipoint demands. This data may be of significant use in the
provisioning of traffic-engineered intra-domain paths and external
peering in the existing network, as well as planning for the
placement and sizing of new links, routers, or peers.
In current practice, estimates for traffic demands are usually
determined from a combination of traffic projections, customer
prescriptions, and service level agreements. Using the facilities
of SNMP (Simple Network Management Protocol), it is not easy to
obtain network-wide traffic demands from the local interface
measurements taken by different IP routers. As explained in [15,
16], information from diverse network measurements, including flow-
based measurements, and various topological data and router
configuration files are needed to infer the traffic volume.
Some shortcomings in today's method to derive traffic matrix
statistics as above include the volume of data from flow-based
measurement, the lack of sufficient routing control information, and
the need to correlate data from a variety of sources. To avoid some
of these deficiencies and to take advantage of the routing control
offered by MPLS, node-pair-based passive measurement should be
developed.
3.4 Requirements for Performance Monitoring
Requirement 1
Standardization of statistics of achieved performance and
throughput.
Requirement 2
Performance monitoring as a means to trigger LSP restoration
activities.
A major component of performance management is performance
monitoring, i.e., continuous real-time monitoring of the quality or
health of the network and its various elements to ensure a
sustained, uninterrupted delivery of quality service.
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General aspects of measurements required to support the operation,
administration, and maintenance of a network are outside the scope
of this document (see [17, 18, 19, 20] for a discussion of MPLS
OAM). However, performance monitoring is required for TE
measurement since monitoring the quality of delivered services is
essential feedback to the TE function.
This requires the use of measurement, either passively or actively,
to collect information about the operational state of the network
and to track its performance. For a discussion of passive
monitoring and the use of synthetic traffic sources in active
probing, see [21, 22].
Performance degradation can occur as a result of routing
instability, congestion, or failure of network components. Periods
of congestion may be detected when the resource usage of a network
segment consistently exceeds a certain threshold, or when the cross-
router delay is unexpectedly high. Unexpected excessive loss of
packets or throughput drops may be used as a means of fault
detection, and may result in restoration activities.
3.5 Requirements for Path Characterization
Requirement 1
Standardization of a method to detect and record label binding
changes for LDP-signaled label-switched paths, at the ingress-egress
pair level.
In the case of hop-by-hop routed label-switched paths that are
established by Label Distribution Protocol (LDP) signaling, there is
no explicit binding between path end points. This will result in
the use of different label bindings at both the ingress and egress
nodes over time as network topology changes. Although the
forwarding equivalence class (FEC) to label binding information
already exists in the MPLS FTN and LSR MIBs [23, 26], a mechanism is
needed to keep track of binding changes. An example of such a
mechanism may be the periodic exchange of FEC to label binding
information for each ingress-egress pair.
Internet utilities such as ping and traceroute have been useful to
help diagnose network problems and performance debugging. Utilities
with similar functions would be essential for path-oriented
operations like in MPLS. This would include the capability to list,
at any time, (1) for a given path, all the nodes traversed by it,
and (2) for a given node, all the paths originating from it,
transiting through it, and/or terminating on it. A proposal for
path tracing is described in [24]. A proposal to establish basic
MPLS data plane liveness is described in [25].
4. Requirements Summary for TE Measurement Types
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A measurement type is a meaningful and measurable combination of a
measurement basis (Appendix A) and a measurement entity (Appendix
B). Two sets of measurement types, organized in the form of
matrices, are presented in the following two subsections.
4.1 Measurement types related to traffic or performance
The following measurement matrix summarizes the measurement types
related to traffic or performance. Potentially, there can be one
such matrix for each service class.
Bases: Flow Interface, Node Pair Path
Node
Entities: (passive) (passive) (both) (both)
Traffic Volume x(1) x x(3) x(3)
Avg. Hold. Time x x(3)
Avail. Bandwidth x x(3)
Throughput x x(4) x(4)
Delay x(2) x(4) x(4)
Delay Variation x(2) x(4) x(4)
Packet Loss x x x(5) x(5)
Notes:
(1) This measurement type can be used to derive flow size
statistics.
(2) These are 1-point measurements. For a discussion on 1-point
packet delay variation, see [12], Appendix II.
(3) As a starting point, statistics collected by passive measurement
through the MIBs useful for TE [26, 27, 28] may be used.
(4) Active measurements based on IPPM metrics are currently in use
for node-pairs; they may be developed for paths.
(5) Besides active measurements based on IPPM, path loss may
possibly be inferred from the difference between ingress and egress
traffic statistics at the two endpoints of a path. However, such
inference for the cumulative losses between a given node pair over
multiple routes may be less useful, since different routes may have
different loss characteristics.
4.2 Measurement types related to resource usage
Another measurement matrix summarizes measurement types comprising
the different usage, one for each network resource object such as
router (processor and memory), link, and buffer, by different
classes of traffic:
. control (e.g., routing control) traffic
. signaling traffic
. user traffic from different service classes
Bases: Node Link Buffer
Entities:
Control Util. x x x
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Signaling Util. x x x
Service Class Util. x x x
The amount of control and signaling traffic carried by a network is
a function of many factors. To name a few, they include the size
and topology of the network, the control and signaling protocols
used, the amount of user traffic carried, the number of failure
events, etc. Also, flooding of link-state advertisement (LSA)
messages in Interior Gateway Protocols (IGP, such as OSPF or IS-IS)
may cause significant routing control traffic during events such as
an LSA storm as a result of failures due to fiber cuts or failed
power supply. The above utilization measurements for control and
signaling traffic are intended to help develop guidelines for the
proper dimensioning and apportionment of network resources so that a
given level of user traffic can be adequately supported.
5. Requirements for a TE Measurement Information Model
Requirement 1
Standardization of an information model for TE measurement.
Requirement 2
Standardization of offline bulk file transfer and standardized
filtering/aggregation mechanisms to manage large volumes of measured
traffic data (see Appendix D for further discussion).
Several approaches and options for repository technology are now
broadly discussed. Relationships between TE measure information
models on other information models (e.g., the COPS Policy Information
Base, PIB) that drive network outcomes are of particular importance.
For an example of a PIB, see [29].
Linkages should be considered between policy mechanisms and TE
measures. This is useful because, while policy-driven networking is
well-developed between the policy repositories, policy decision
points and policy enforcement points, policy content is very likely
the output of TE applications [30]. **Since TE applications are
dependent upon TE measures, it is advantageous to provide
traceability between the measures and the engineering changes made as
a consequence of them.** An example of a client application that
might be driven by TE measures through a PIB is found in [31, 32].
Measures (represented by their estimates) should be centrally stored
and collected in a logical sense. This does not preclude distributed
storage for purposes of volume management or security/survivability,
but alludes to the need for a consistent retrieval mechanism (e.g.,
NFS). Two methods are: (1) extend MIBs with new definitions for TE
measure estimates or extend PIBs with new objects and use the COPS
feedback extensions to get statistics stored at the policy
enforecement points, and (2) create data depositories through more
centralized facilities, such as relational databases or LDAP (see
[29]). Both methods have merits as collection processes for TE
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measures, and are simple examples spanning a wide spectrum of
solutions. These two methods are discussed here for expository
purposes, not to exclude other solutions.
Using MIBs allows well-established SNMP protocol and related
applications to retrieve data from the network elements being
measured. This is inherently "vendor-neutral," allowing commonly
defined TE measurements to be stored for retrieval in a common MIB
definition, regardless of network element vendor, technology or other
differences.
A centralized data storage facility has the advantage that TE
applications (such as offline and online TE, or measurement-based
admission control) can be performed without invasive retrieval of
data from network-wide MIBs.
It is possible that both the distributed MIB-based and centralized
repository-based approaches (or another approach altogether) should
be considered jointly. However, this is not mandatory: TE systems
could rely solely on events from distributed measurement points,
e.g., based on threshold checking in every device. Even in this
case, a centralized storage should be in place to log these events so
to provide a linkage between the observed behavior and resulting
configuration.
Although this document focuses on the motivation for providing TE
measurement information, it is assumed that this information should
be provided to the participating devices by means of a communication
protocol that would be used between the aforementioned participating
devices and a presumably centralized entity that would aim at
storing, maintaining and updating this information, as well as
making appropriate decisions at the right time and under various
conditions.
This communication protocol should have the following
characteristics:
1. The protocol should make use of a reliable transport mode, given
the importance of configuration information.
2. The protocol architecture should provide a means for dynamically
provisioning the configuration information to the participating
devices, so that it may introduce/contribute to a high level of
automation in the actual TE measurement operation.
3. The protocol should support one or more reporting mechanisms that
may be used for statistical information retrieval. Reporting
mechanisms can be either polling-based (explicit requests) or event-
based (asynchronous reports).
4. The protocol should support the appropriate security mechanisms
to provide some guarantees as far as the preservation of the
confidentiality of the configuration information is concerned.
5. The protocol should support reporting at regular intervals, and
can optionally support asynchronous conditional reporting (e.g.,
whenever a value crosses a threshold).
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6. Measurement Definitions
Requirement 1
Standardization of measurement definitions and sampling methods, to
achieve uniformity across vendors and operators and to preserve
sufficient traffic detail at manageable sample volumes.
It is critical to minimize the possibilities of inconsistencies
arising from, e.g., differing statistical definitions, overlapping
data collection, processing at different protocol levels, and
similar inconsistencies by different vendors or network operators.
Uniform measurement definitions across vendors and operators should
be enforced as far as possible.
As this is a requirements document and not a document for
measurement definitions, the intent of this section is not to
provide definition of terms used. Rather, it is to highlight the
difference in usage of closely related terms and describe terms used
herein. Nor is this section exhaustive, since needs for other
measures may arise in practice (for examples of other closely-
related metrics see [33]).
6.1 Active, passive measurements
These terms are used in the sense of [2]. In an active measurement,
test packets, or probes, are injected into the network. Data
collected about these packets are taken as representative of the
behavior of the network. Passive measurements are in-service, non-
intrusive, and so can be performed directly on the user traffic.
For a discussion of sampling issues related to both active and
passive measurements, see Appendix C.
6.2 Route, path
A route is any unidirectional sequence of nodes and links, for
sending packets from a source node to a destination node. A path
refers to an MPLS tunnel, i.e., a label-switched path (LSP) [34],
this LSP possibly being a traffic-engineered LSP. Measurements on
non-traffic-engineered LSPs may be collected to support the possible
future traffic-engineering of those LSPs. (Note: What is defined
as a route here is referred to as a path in [2]. The route/path
distinction is made here to facilitate applications to MPLS.)
It should be pointed out that there are also methods for creating
paths with other technologies such as frame relay or ATM. The
measurement described in this document may apply to these
technologies with suitable adaptation. To simplify description,
reference is made to MPLS only in what follows.
6.3 Throughput, traffic volume
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Both quantities can be applied to a network, a network segment, or
an individual network element. Thus, measurement points need to be
appropriately defined when a specific measurement is to be performed
(e.g., from a given ingress node to another egress or a set of
egress nodes).
Throughput of a network, as a measure of delivered performance,
refers to the maximum sustainable rate of transferring packets
successfully across the network, under given network conditions,
e.g., a given traffic mix, while meeting quality of service (QoS)
objectives. This usage is consistent with the definition of
throughput for a network interconnect device as specified in [35].
For real-time network control, active measurement of throughput by
probing may be used to determine the currently available capacity of
a network to carry additional traffic. (Note: Goodput is a related
term referring to a proportion of the traffic successfully
transmitted; similarly, badput refers to a proportion of the traffic
lost or being corrupted.)
Traffic volume, reflecting the traffic carried, is the amount of
traffic measured during a given period of time. Passive measurement
of the traffic volume is usually used to estimate the long-term
offered traffic for the purposes of network dimensioning in the
capacity-management and network-planning processes (see Appendix E
on Time Scales for Network Operations). A network should be
properly dimensioned so that its throughput is adequate to handle
the expected traffic volume. Hence, traffic volume measurement
should be performed on a regular basis.
Throughput at a cross-section, or specific point in the network, is
expressed in terms of number of data units per time unit. Traffic
volume is expressed in data units with reference to a read-out
period (see Appendix D on Read-Out Periods). For transmission
systems, the data unit is usually a multiple of either bits or
bytes. For processing systems, the data unit is usually a multiple
of packets.
APPENDICES
APPENDIX A
A. Measurement Bases
Measurements can be classified on the basis of where, and at which
level of aggregation the traffic data are gathered. This is similar
to the concept of a *population of interest* as specified in ITU-T
Recommendation I.380/Y.1540. As defined therein, this refers to a
set of packets, possibly relative to a particular pair of source and
destination hosts, for the purposes of defining performance
parameters. However, measurement bases as used here may not have
any association with a source-destination pair. This is to be
described in more details below. Currently, the different
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measurement bases to be defined below have not been explicitly
specified in the IPPM Framework [2].
In this document, the focus is on service providers as organizations
requiring traffic and performance measurements. (However, customer-
based measurements of enterprise networks may have similar issues.)
Service providers will make decisions on how to perform the
measurements needed, and there are various tradeoffs involved. One
option is to obtain the measurements directly from the network
elements themselves, e.g., via SNMP. Collecting the measurements on
the operational network elements such as routers is sometimes a
performance concern. Currently, there is a number of third-party
measurement/monitoring products available. Hence, another option is
to deploy such equipment, which might have performance advantages
but also introduces additional cost.
Regardless of the type of measurement source, either a network
element or a third-party product, measurements should be collected,
as far as possible, by a measurement source without requiring
coordination with other measurement sources. Thus, it is desirable
to perform those measurements that do not require the use of
specialized monitoring equipment connected to the network at
multiple locations. While each measurement source may act
autonomously with regard to taking measurements, a network operator
may specify some network-wide policy regarding measurement
scheduling. Such policy may be, say, the use of the same time of
day, the same measurement interval, or measurement intervals that
are multiples of each other (e.g., nested intervals with
synchronized boundaries). A schedule therefore should include such
time information as the start, the duration, and periodicity of a
certain measurement. Also note that the accuracy of traffic
measurement is highly dependent on the synchronization capabilities
of the measurement devices that will be involved in the measurement
procedures. While synchronization issues are out of the scope of
this document, they should be explicitly addressed whenever a
measurement campaign is to be launched, whatever its scope and its
frequency.
The following measurement bases are considered in this document:
. Flow-based
. Interface-based, link-based, node-based
. Node-pair-based
. Path-based
Passive measurements are prevalent today for TE purposes. However,
the above measurement bases may result in active or passive
measurements. For example, an active measurement may be a two-point
delay metric such as type-P-one-way-delay defined in [4], and
obtained by time-stamping probe packets at selected ingress and
egress points; a passive measurement may be to obtain packet inter-
arrival times by time-stamping successive packets of the traffic at
a selected point in the network. Note that both active and passive
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measurements are subject to the same sampling and time-source
accuracy concerns.
MPLS has certain advantages when compared with conventional IP
networks, from the perspective of the difficulty involved in
obtaining unambiguous measurements. **As different service
providers will adopt different technologies, technology-neutral
solutions to the problem of obtaining measurements are needed as far
as possible.**
Applicability of traffic measurements to the derivation of traffic
matrix statistics and performance monitoring has been described in
Section 3.
A.1 Flow-based
This is conceptually similar to the call detail record (CDR) in
circuit-switched telecommunications networks. It is primarily used
on interfaces at access routers, edge routers, or aggregation
routers, rather than on backbone routers in the core network. Like
CDR measurements, flow-based records are used to collect detailed
information about a flow. This includes such information as source
and destination IP addresses/port numbers, protocol, type of
service, timestamps for the start and end of a flow, packet count,
octet count, etc.
As flow is a fine-grained object, measuring every flow that passes
through all the edge devices may not be scalable or feasible.
Hence, per-flow data are usually used in a special study conducted
on a non-continuous schedule and on selected routers only. Sampling
of flow-based measurements may also be needed to reduce both the
amount of data collected and the associated overhead.
A.2 Interface-based, link-based, node-based
While active measurements are often not useful at a single point,
passive measurements can be taken at each network element. For
example, SNMP uses passive monitoring to collect raw data on an
interface at an edge or backbone router. These data are stored in
MIBs (Management Information Bases) and include counts on packets
and octets sent/received, packet discards, errored packets. Such
measurements may have the disadvantage that the identity of each
flow is lost.
To reduce the overhead in managing multiple links between the same
ingress and egress points, there is proposal to aggregate links for
network optimization [36]. Component links in such a bundled link
will have the same routing constraints, resource classes, and
attributes. Multiple links are treated as a single IP link.
Traffic measurements, such as bandwidth availability, throughput,
etc., should consider the measurement implications for bundled
links, and should not inhibit link bundling. (For example, a single
IP link may presumably be referenced as a pair of IP addresses that
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are assigned to both extremities of the link. An implicit issue
that may need to be resolved relates to the exact characterization
of the traffic that will be conveyed in each component link, since a
couple of IP addresses may not be sufficient for such link-based
measurement.) Also, such measurements should be protocol
independent and media independent to ensure portability and
commonality in the measurements.
A.3 Node-pair-based
Active measurements by probing, as specified in the IPPM framework
for example, can be conducted between each pair of (major) routing
hubs for determining edge-to-edge performance of a core network.
This complements the passive measurements of the previous sub-
section, which provide local views of the performance of individual
network elements.
In contrast to performance statistics, traffic loading statistics
require passive measurements of the actual traffic. In circuit-
switched telecommunications networks, each established call has an
associated source/destination node-pair. By maintaining a set of
node-pair data registers [usage, call attempts (so-called "peg
count" in telephony operation and management), overflow, etc.] in
each switch, node-pair-based measurements for traffic statistics
such as the load between a given node pair are taken directly. In
IP networks, currently such node-pair-based measurements are
difficult to establish due to the dynamic and asymmetric properties
of IP routing. However, it is possible to infer them from flow-
based passive measurements and other network information, such as
routing table snapshots. A problem with this approach is that flow-
based measurement data are voluminous. Also, another problem that
must be accounted for is the routing changes among the multiple
routes due to, e.g., a change in the configuration of intra-domain
routing, or a change in inter-domain policies made by another
autonomous system. These issues were discussed in Section 3.4 on
Traffic Matrix Statistics.
A.4 Path-based
The ability of MPLS to use fixed preferred paths for routing traffic
gives the means to develop path-based measurements. This may enable
the development of methodologies for such functions as admission
control and performance verification of delivered service.
Like a flow, a path is associated with a pair of nodes. However,
path is a more coarse-grained object than flow, as paths are usually
used to carry aggregated traffic (from different flows). In
addition, when routing changes occur, the amount of traffic to be
carried by a path will either not be affected or be merged with that
of another path. Because of these properties, path-based
measurements are more scalable and may be used to provide more
readily an accurate, network-wide, view of the traffic demands. For
example, the traffic between a given pair of nodes may be inferred
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from the aggregate of the traffic carried by all paths either
terminated by or passed through the same node-pair.
APPENDIX B
B. Measurement Entities
A measurement entity defines what is measured: it is a quantity for
which data collection must be performed with a certain measurement.
A measurement type can be specified by a (meaningful) combination of
a measurement entity with the measurement basis described in
Appendix A.
An important issue with any measurement is measurement precision
and/or accuracy. However, this issue is not dealt with here since
each measurement type will potentially have its own unique
requirements. For example, see [4], Section 3.7, for a discussion
on error issues for one-way delay.
B.1 Entities related to traffic and performance
Some of the measurement entities listed below, such as throughput,
delay, delay variation, and packet loss, are related to the
respective IPPM performance metrics or the I.380/Y.1540 performance
parameters.
. Traffic volume (mean and variance, in number of bits, bytes, or
packets transferred, as counted over a given time interval), on a
per service class basis, at various aggregation levels (IP address
prefix, interface, link, node, node-pair, path, network edge,
customer, or autonomous system)
Note: (1) This is a measurement for the traffic carried by a
network, a network segment, or an individual network element; it
is used to derive the carried load or carried traffic intensity
[37]. When measured during the busy period, this entity is
normally used to estimate the traffic offered. However, the
estimation procedure should take into account such factors as
congestion, which may result in a decreased volume of carried
traffic. In addition, congestion may lead to user behavior such
as reattempt or abandonment, which may affect the actual traffic
offered. (2) To reduce uncertainty in traffic estimation, second-
order measures may need to be developed. Beyond the use of
variance as in current practice, further study is needed for the
feasibility of other second-order techniques. (3) Measurement of
traffic volumes over interconnecting links at border routers can
be used to estimate the traffic exchange between peers for
contract verification.
. Average holding time (e.g., flow duration or lifetime, duration of
an MPLS path), on a per service class basis
Note: (1) When MPLS TE is used, this is similar to call holding
time in telecommunications networks. Call attempts, usage, and
call holding time are three busy-hour entities that should be
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independently measured for both call-dependent and load-dependent
engineering. This is important especially when the call busy hour
and the load busy hour during a day are non-coincident, due to the
hour-to-hour variation of call holding times. (2) The holding
time statistics of long-living static paths reflect the effect of
network equipment failures, link outages, or scheduled
maintenance, and hence may be used to derive information about up-
time or service availability. (3) It is desirable to be able to
gather, by passive means, the up-time durations for each pair of
label bindings in the label-forwarding information base for labels
distributed by different protocols (such as LDP, RSVP-TE, MP-BGP,
or BGP). Then, the derivation of LSP average holding time does
not need to be finely correlated with network events such as
link/node failures. (Note that routers measure only the holding
times, with their averages being typically computed offline.)
. Available bandwidth of a link or path - useful for load balancing,
measurement-based admission control to determine the feasibility
of creating a new MPLS tunnel (real-time information can be used
for dynamic establishment)
For more information on available bandwidth see [38].
. Throughput (in bits per second, bytes per second, or packets per
second)
Note: (1) This is the rate at which a given amount of traffic
excluding lost, misdelivered, or errored packets, that passes
between a set of end points, where end points can be logically or
physically defined. The condition of the network, e.g., normal or
high load, under which the measurement is taken should be noted.
(2) The protocol level at which a throughput measurement is taken
must be specified, as the packet payload and packet overheads are
protocol dependent. (3) The average packet size may be inferred
from the bit rate and packet rate measurements, when performed on
the basis of an individual router. This quantity is useful to
gauge router performance, since router operations are typically
packet-oriented and small packets are more processing-intensive.
. Delay (e.g., cross-router delay from node-based measurement may be
used to measure queueing delay within a router; end-to-end one-way
or round-trip packet delay can be obtained by node-pair-based
measurement)
Note: The condition of the network, e.g., normal or high load,
under which the measurement is taken should be noted. This is
useful to determine if delay objectives are met.
. Delay variation
Note: There are several methods to measure this quantity as
specified in ITU-T and IPPM. (1) In Y.1540, measurements are
defined for both 2-point and 1-point IP packet delay variation.
However, 2-points methods are being specified as normative. (2)
In IPPM [9], the concept of a selection function is introduced
that allows for the explicit designation of selected packets whose
one-way delay values are compared to compute one-way delay
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variation. For example, to define a method of measurement, a
selection function can be specified to select the consecutive
packets within a specified interval, or to select the maximum and
minimum one-way delays within a specified interval.
. Packet loss
Note: (1) While packet losses due to transmission and/or protocol
errors may not be traffic related, unexpected excessive loss may
be used as a means of fault detection. (2) In most active
measurements, the cause of packet loss is not distinguished.
However, it may be desirable to distinguish (e.g., by passive
means) packet losses due to policing or network congestion. The
former is a result of user violation of service contract and the
network operator should not be penalized for it. The latter,
whether intentional or unintentional, is caused by network
conditions such as buffer overflow, router forwarding process
busy, and may not be the user's fault. When policing is done by a
network, measurement of non-conforming packets at the edge
provides an indication on the extent to which the network is
carrying this type of packets (which can potentially be dropped if
network gets congested). Loss due to congestion of any packets,
including loss of non-conforming packets, is a useful measure in
TE to account for resource management. (3) Long-term averages can
be measured by the I.380/Y.1540 IP packet loss ratio or by the
IPPM Poisson sampling of one-way loss. However, during the
convergence times associated with routing updating, the loss may
be high enough as to cause service unavailability. This effect
needs to be captured and statistics such as loss patterns, burst
loss, or severe loss ratio may be useful.
. Resource usage, such as link/router utilization, buffer occupancy
(e.g., fraction of arriving packets finding the buffer above a
given set of thresholds)
Note: (1) Depending on the architecture of a router, router
utilization measurements may include processor and memory (e.g.,
forwarding tables) utilization for each of the line cards and/or
the central unit. (2) Trigger points may be set when resource
usage consistently exceeds a certain threshold.
B.2 Entities related to establishment of connection or path
Where connection admission control is used, a measurement entity for
monitoring network performance may be the proportion of connections
denied admission. Also, it may be useful to score the requested
bandwidth within the traffic parameters for the setup request.
Corresponding to the number of call attempts (i.e., peg count) in
telecommunications networks, the number of connection requests, the
number of flows, etc., may be measured in given read-out periods to
characterize the traffic.
APPENDIX C
C. Packet Sampling and Estimation
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C.1 Packet Sampling
A wide spectrum of operational applications can be built on traffic
measurement. However, different applications usually require
traffic measurements at different levels of temporal and spatial
granularity. To achieve an effective tradeoff between
implementation complexity and the range of operational tasks to be
enabled, a passive measurement framework based on packet sampling is
proposed in [39].
The use of packet sampling has two motivations. First, the enormous
volumes of traffic require that some form of data reduction to be
used. Second, simple data reduction by aggregation at the
measurement point will not provide sufficiently detailed views for
all network management applications or exploratory studies. For
this reason, packet sampling is proposed as a means to reduce data
volume while still retaining representative detail.
The primary aim of the proposal [39] is to define a minimal set of
primitive packet selection operations out of which all sampling
operations that are necessary to support measurement-based
applications can be composed. Operations currently under
consideration include filtering and statistical sampling, and also
hash-based packet selection, a method that can be used to support
the determination of spatial traffic flows across a domain [40].
Whichever method is used, the interpretation of the stream of
measurements arising from sampled packets must be both transparent
and standard. Other goals are to specify a means to format and
export measurements, and a means to manage the configuration of the
sampling and export operations.
The proposal positions these functions to provide a basic packet
sampled measurement service to higher level "consumers." A typical
consumer is a network management application that sits behind a
remote measurement collector. Such measurements can support
applications for a number of tasks: troubleshooting, demand
characterization, scenario evaluation and what-ifs. Another type of
consumer is a higher level on-router measurement application. One
potential class of examples is composite measurements (e.g., inter-
packet delay statistics) formed from a number of individual packet
measurements. Another class is network security applications, e.g.,
IP traceback [41]. For some applications, the ability to have low
latency between packet measurement and reporting will be
particularly useful.
C.2 Sampling Issues
The concept of read-out periods applies to both active and passive
measurements. This concept is consistent with the sampling issues
for a series of measurements as developed in [2], for example. See
sections 10 and 11 of that document for important distinctions
between "singletons, samples, and statistics." The procedure of
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Poisson sampling, for example, may be used within a read-out period
to select a subset of total packet events that are chosen as the
sample. Then a statistic (e.g., mean or variance) can be computed
over that sample and associated with the read-out period. Although
[2] does not discuss traffic volume measures such as a traffic
matrix, the same sampling issues arise for the traffic matrix and
other passive measurements.
C.3 Engineering methods for statistical estimation of measures
The use of the well-established methods of optimal estimation [42,
43, 44, 45] to obtain estimates of the measures for TE is
recommended. This draws upon several facts:
. Internet traffic is inherently band-limited, but non-stationary;
. Internet traffic may be heavy-tailed and possess strong short-term
correlations;
. A stationary, band-limited process can be approximated arbitrarily
closely by optimal estimation methods based on a finite number of
past samples.
Standard procedures for de-trending the raw data to provide "trend +
stationary" decompositions should be adopted. An example is the use
of Autoregressive Integrated Moving Average (ARIMA) models, where
first differences are applied to the raw (non-stationary) data,
yielding a stationary derived process. Then, the methods of optimal
estimation can be applied in a practical setting (e.g., finite sample
counts) to the derived stationary process to produce quality
estimates of the measures defined herein. As the original raw
process may be any of the measurements discussed in this document,
the above procedure may be applied without loss of generality to
measures of delay, loss, or complex measures of network state such as
path characteristics, etc.
In addition, these methods need to be applied across multiple time-
scales, so that TE applications can work with measures related to:
. long-term trends over days, weeks, and months;
. busy-hour characterizations; and
. statistics and correlation properties on the order of seconds [46].
The above estimation procedures apply equally to traffic workload,
traffic performance, or other estimates of network state, such as the
state of routes.
APPENDIX D
D. Read-Out Periods
A measurement infrastructure must be able to scale with the size and
the speed of a network as it evolves. Hence, it is important to
minimize the amount of data to be collected, and to condense the
collected data by periodic summarization over read-out periods.
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D.1 Data Reduction
Techniques to manage large volumes of measured data are needed to
prevent network performance from being adversely affected by the
unnecessarily excessive loading of router control processors, router
memories, transmission facilities, and the administrative support
systems.
For example, offline bulk file transfer may be used as a method to
manage large volumes of measured traffic data. Bulk transfer from
routers to collection devices can help reduce the packet processing
overhead experienced by using other management interfaces. Also,
data correlation or filtering rules may be set up to suppress
redundant data, or to aggregate flows into suitable classes with the
corresponding aggregation of statistics. These types of data
reduction may be used as an appropriate or acceptable means for
pruning down the overall volume of traffic data that a TE system may
ultimately have to store, maintain, and process.
D.2 Measurement Interval
A measurement interval is the time interval over which measurements
are taken. Some traffic data must be collected continuously, while
others by sampling, or on a scheduled basis. For example, peak
loads and peak periods can be identified only by continuous
measurement as traffic typically fluctuates irregularly during the
whole day. If traffic variations are regular and predictable, it
may be possible to measure the expected normal load on pre-
determined portions of the day. Such duration of peak traffic is
referred to as a busy period. Special studies on selected segments
of the network may be conducted on a scheduled basis. Occasionally
unexpected events or other decision support needs may arise that
require ad-hoc, unscheduled measurement, with the involvement of the
network operator, and in such a case measurements may be activated
manually. For instance, active throughput measurement may be used
to identify alternate routes for spreading traffic to avoid future
periods of network congestion, based on observations of current
local congestion events.
D.3 Summarization
A measurement interval consists of a sequence of consecutive read-
out periods. Summarization is usually done by integrating the raw
data over a pre-specified read-out period. The granularity of this
period must be suitably chosen. It should be short enough to
capture, with acceptable accuracy, the bursty nature of the traffic,
i.e., the traffic variations and peaks. Since measurements
represent a load for the router (if third-party measurement devices
are not employed), the read-out period should not be so short that
router performance is degraded while a voluminous quantity of data
is produced. Also, read-out may be started when the measured data
exceeds a preset threshold, or when the space allocated for
temporarily holding the data in a router is exhausted.
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For a multi-service IP network, each service typically has its own
traffic characteristics and performance objectives. To ensure that
CoS-specific features are reflected in the measurement process,
different read-out periods may be needed for different classes of
service.
APPENDIX E
E. Time Scales for Network Operations
The information collected by traffic measurement can be provided to
the end user or application either in real time, or for record
(i.e., data retention) in non-real time, depending on the activities
to be performed and the network actions to be taken. Traffic
control will generally require real-time information. For network
planning and capacity management as described below, information may
be provided in non-real time after the processing of raw data.
Broadly speaking, the following three time scales can be classified,
according to the use of observed traffic information for network
operations [14].
Network planning
Information that changes on the order of months is used to make
traffic forecasts as a basis for network extensions and long-term
network configuration. That is, for planning the topology of the
network, planning alternative routes to survive failures or
determining where capacity must be augmented in advance of projected
traffic growth. Long-term planning includes the selection and
timing of the introduction of new architectures, technologies and
vendors, in alignment with financial forecasts and market
assessments.
Capacity management
Intermediate-scale (e.g., six months or less) capacity planning
deals with detailed implementation of the build plan, short-lead-
time activities and out-of-plan events. It typically uses a
rolling-month forecast of traffic and demand. Information that
changes on the order of days or hours is used to manage the deployed
facilities, by taking appropriate maintenance or engineering actions
to optimize utilization. For example, new MPLS paths may be set up
or existing paths modified while meeting service level agreements.
Also, load balancing may be performed, or traffic may be rerouted
for re-optimization after a failure.
Real-time network control
Information that changes on the order of minutes or less is used to
adapt to the current network conditions in near real time. Thus, to
combat localized congestion, traffic management actions may perform
temporary rerouting to redistribute the load. Upon detecting a
failure, traffic may be diverted to pre-established, secondary
routes until more optimized routes can be arranged.
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APPENDIX F
F. Use of Traffic Measurement for Traffic control
Destination-based per-hop IP routing and forwarding provides a
network operator with primitive and limited control over the routing
of traffic flows. The routing control offered by MPLS can be used
to avoid some of the deficiencies of IP routing. In this context, a
primary use of traffic measurement is to engineer the use of label-
switched paths to achieve service goals for the network.
Examples of traffic control are:
. Adaptively optimizing network performance in response to network
events, e.g., rerouting to work around congestion or failures.
. Providing a feedback mechanism in the reverse flow messaging of
RSVP-TE [47] or CR-LDP [48] signaling in MPLS to report on actual
topology state information such as link bandwidth availability.
(An example of such a feedback mechanism is described in [49];
however, care should be exercised to ensure network stability and
consistency for any mechanism that makes direct operational use of
measurement.)
. Support of measurement-based admission control, adaptive resource
management, or other techniques, e.g., by predicting the future
demands of the aggregate of existing flows so that admission
decisions can be made on new flows.
An example of TE measurements used to enable a traffic control
mechanism is to configure policing mechanisms in response to traffic
load and performance measurements. A network operator could
selectively throttle low-priority flows to improve near-real-time
performance of higher-priority flows, and maintain tighter QoS
envelopes. Another example would be to use measurement results for
feedback into IGP routing decisions, e.g., for adjusting the link
weights based on them.
16. Security Considerations
The principles and concepts related to Internet traffic measurement
as discussed in this document do not by themselves affect the
security of the Internet. However, it is assumed that any
measurement systems that are developed or deployed by a service
provider are responsible for providing sufficient data integrity
(e.g., to prevent forgery of measurement records) and
confidentiality (e.g., by restricting attention only to the packet
headers of interest). It is also assumed that a service provider
will take proper precautions to ensure that access to its
measurement systems and all associated data is secure by using
appropriate authentication techniques. Methods to achieve these
security considerations are not addressed in this document.
17. References
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Normative References
References 1, 2, and 34 below are considered normative.
Informative References
1 D.O. Awduche, A. Chiu, A. Elwalid, I. Widjaja, and X. Xiao,
"Overview and Principles of Internet Traffic Engineering," RFC
3272, May 2002.
2 V. Paxson, G. Almes, J. Mahdavi, and M. Mathis, "Framework for IP
Performance Metrics," RFC 2330, May 1998.
3 J. Mahdavi and V. Paxson, "IPPM Metrics for Measuring
Connectivity," RFC 2678, September 1999.
4 G. Almes, S. Kalidindi, and M. Zekauskas, "A One-way Delay Metric
for IPPM," RFC 2679, September 1999.
5 G. Almes, S. Kalidindi, and M. Zekauskas, "A One-way Packet Loss
Metric for IPPM," RFC 2680, September 1999.
6 G. Almes, S. Kalidindi, and M. Zekauskas, "A Round-trip Delay
Metric for IPPM," RFC 2681, September 1999.
7 M. Mathis and M. Allman, "A Framework for Defining Empirical Bulk
Transfer Capacity Metrics," RFC 3148, July 2001.
8 R. Koodli and R. Ravikanth, "One-way Loss Pattern Sample
Metrics," RFC 3357, August 2002.
9 C. Demichelis and P. Chimento, "IP Packet Delay Variation Metric
for IP Performance Metrics (IPPM)," RFC 3393, November 2002.
10 V. Raisanen, G. Grotefeld, and A. Morton, "Network performance
measurement with periodic streams," RFC 3432, November 2002.
11 H. Uijterwaal and M. Kaeo, "One-way Metric Applicability
Statement," Internet-Draft, Work in Progress, November 2002.
12 ITU-T Recommendation I.380/Y.1540, "Internet Protocol Data
Communication Service -- IP Packet Transfer and Availability
Performance Parameters," First Issued February 1999, Revised
December 2002.
13 ITU-T Recommendation Y.1541, "Network Performance Objectives for
IP-Based Services," May 2002.
14 G. Ash, "Traffic Engineering & QoS Methods for IP-, ATM-, & TDM-
Based Multiservice Networks," Internet-Draft, Work in Progress,
October 2001.
15 A. Feldmann, A. Greenberg, C. Lund, N. Reingold, J. Rexford, and
F. True, "Deriving Traffic Demands for Operational IP Networks:
Methodology and Experience," Proc. ACM SIGCOMM 2000, Stockholm,
Swedan.
16 A. Feldmann, A. Greenberg, C. Lund, N. Reingold, and J. Rexford,
"NetScope: Traffic Engineering for IP Networks," IEEE Network,
March/April 2000.
17 T.D. Nadeau, M. Morrow, G. Swallow, and D. Allan, " OAM
Requirements for MPLS Networks," Internet-Draft, Work in
Progress.
18 N. Harrison, P. Willis, S. Davari, E. Cuevas, B. Mack-Crane, E.
Franze, H. Ohta, T. So, S. Goldfless, and F. Chen, "Requirements
for OAM in MPLS Networks," Internet-Draft, Work in Progress, May
2001.
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19 ITU-T Draft Recommendation Y.1710, "Requirements for OAM
Functionality for MPLS Networks," May 2001.
20 ITU-T Draft Recommendation Y.1711, "OAM Mechanisms for MPLS
Networks," May 2001.
21 S. Waldbusser, R.G. Cole, C. Kalbfleisch, and D. Romascanu, "An
Introduction to the RMON Family of MIB Modules," Internet-Draft,
Work in Progress, Jan 2003.
22 C. Kalbfleisch, R.G. Cole, and D. Romascanu, "Definition of
Managed Objects for Synthetic Sources for Performance Monitoring
Algorithms," Internet-Draft, Work in Progress, Sept 2002.
23 T.D. Nadeau, C. Srinivasan, and A. Viswanathan, "Multiprotocol
Label Switching (MPLS) FEC-To-NHLFE (FTN) Management Information
Base," Internet-Draft, Work in Progress, January 2002.
24 R. Bonica, K. Kompella, and D. Meyer, "Tracing Requirements for
Generic Tunnels," Internet-Draft, Work in Progress, August 2002.
25 K. Kompella, P. Pan, N. Sheth, D. Cooper, G. Swallow, S. Wadhwa,
and R. Bonica, "Detecting MPLS Data Plane Liveness," Internet-
Draft, Work in Progress, October 2002.
26 C. Srinivasan, A. Viswanathan, and T.D. Nadeau, "Multiprotocol
Label Switching (MPLS) Label Switch Router (LSR) Management
Information Base," Internet-Draft, Work in Progress, January
2002.
27 C. Srinivasan, A. Viswanathan, and T.D. Nadeau, "Multiprotocol
Label Switching (MPLS) Traffic Engineering Management Information
Base," Internet-Draft, Work in Progress, January 2002.
28 K. Kompella, "A Traffic Engineering MIB," Internet-Draft, Work in
Progress, September 2002.
29 R. Yavatkar, D. Pendarakis, and R. Guerin, "A Framework for
Policy-based Admission Control," RFC 2753, January 2000.
30 D. Rawlins, A. Kulkarni, M. Bokaemper, and K.H. Chan, "Framework
for Policy Usage Feedback for Common Open Policy Service with
Policy Provisioning (COPS-PR)," Internet-Draft, Work in Progress,
December 2002.
31 C. Jacquenet, "An IP Forwarding Policy Information Base,"
Internet-Draft, Work in Progress, January 2003.
32 C. Jacquenet, "A COPS client-type for IP traffic engineering,"
Internet-Draft, Work in Progress, January 2003.
33 S. Sen and J. Wang, "Analyzing Peer-to-Peer traffic Across Large
Networks," Internnet Measurement Workshop, 2002.
34 E. Rosen, A. Viswanathan, and R. Callon, "Multiprotocol Label
Switching Architecture," RFC 3031, January 2001.
35 S. Bradner (Editor), "Benchmarking Terminology for Network
Interconnection Devices," RFC 1242, July 1991.
36 K. Kompella, Y. Rekhter, and L. Berger, "Link Bundling in MPLS
Traffic Engineering," Internet-Draft, Work in Progress, February
2001.
37 W.S. Lai, "Traffic Measurement for Dimensioning and Control of IP
Networks," Internet Performance and Control of Network Systems II
Conference, SPIE Proceedings, Vol. 4523, Denver, Colorado, 21-22
August 2001, pp. 359-367.
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38 M. Jain and C. Dovrolis, "End-to-End Available Bandwidth:
Measurement Methodology, Dynamics, and Relation with TCP
Throughput," Proc. ACM SIGCOMM'2002, August 19-23, 2002,
Pittsburgh, Pennsylvania.
39 N.G. Duffield (Editor), "A Framework for Passive Packet
Measurement," Internet-Draft, Work in Progress, September 2002.
40 N.G. Duffield and M. Grossglauser, "Trajectory Sampling for
Direct Traffic Observation," IEEE/ACM Trans. on Networking, 9(3),
pp. 280-292, June 2001.
41 C. Partridge, C. Jones, D. Waitzman, and A. Snoeren, "New
Protocols to Support Internet Traceback," Internet-Draft, Work in
Progress, November 2001.
42 S. Haykin, Ed., "Kalman Filtering and Neural Networks," Wiley
Interscience, 2001.
43 A. Papoulis, "Probability, Random Variables and Stochastic
Processes," 3rd Ed., McGraw-Hill, 1991.
44 A. Gelb, Ed., "Applied Optimal Estimation," MIT Press, 1974.
45 I. R. Petersen, V. A. Ugrinovskii, A. V. Savkin, "Robust Control
Design Using H<\infinity> Methods," Springer, 2000.
46 V. Bolotin, J. Coombs-Reyes, D. Heyman, Y. Levy, and D. Liu, "IP
Traffic Characterization for Planning and Control," Proc. ITC16,
Edinburgh, Scotland, June 1999.
47 D. Awduche, L. Berger, D. Gan, T. Li, V. Srinivasan, and G.
Swallow, "RSVP-TE: Extensions to RSVP for LSP Tunnels," RFC 3209,
December 2001.
48 B. Jamoussi (Editor), "Constraint-Based LSP Setup using LDP," RFC
3212, January 2002.
49 P. Ashwood-Smith, B. Jamoussi, D. Fedyk, and D. Skalecki,
"Improving Topology Data Base Accuracy with Label Switched Path
Feedback in Constraint Based Label Distribution Protocol,"
Internet-Draft, Work in Progress, November 2002.
18. Intellectual Property Statement
AT&T Corp. may own intellectual property applicable to packet
sampling as presented in references [39, 40] and summarized in
Appendix C.1. AT&T is currently reviewing its licensing intent
relative to the Intellectual Property and will notify the IETF when
AT&T has made a determination of that intent.
19. Acknowledgments
Thanks to the inputs from Gerald Ash, Jim Boyle, Robert Cole,
Enrique Cuevas, Ruediger Geib, Christian Jacquenet, Merike Kaeo, Ed
Kern, Spyros Kontogiorgis, Alfred Morton, Thomas Nadeau, Dimitri
Papadimitriou, Moshe Segal, Jing Shen, Bert Wijnen, Raymond Zhang,
and the Scampi and Tequila projects. Special thanks to Blaine
Christian for starting this work and contributing to the initial
versions. Nick Duffield provided Appendix C.1 on packet sampling.
20. Author's Addresses
Lai, et al Category - Expiration [Page 26]
Internet-Draft Framework for Internet Traffic Measurement July 2003
Wai Sum Lai
AT&T Labs
Room D5-3D18
200 Laurel Avenue
Middletown, NJ 07748, USA
Phone: +1 732-420-3712
Email: wlai@att.com
Richard W. Tibbs
Oak City Networks & Solutions
304 Harvey St.
Radford, VA 24141, USA
Phone: +1 540 639 2145
Email: drtibbs@oakcitysolutions.com
Steven Van den Berghe
Ghent University/IMEC
St. Pietersnieuwsstraat 41
B-9000 Ghent, Belgium
Phone: ++32 9 264 99 86
E-mail: steven.vandenberghe@intec.ugent.be
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Lai, et al Category - Expiration [Page 27]
