| RFC : | rfc3290 |
| Title: | |
| Date: | May 2002 |
| Status: | INFORMATIONAL |
Network Working Group Y. Bernet
Request for Comments: 3290 Microsoft
Category: Informational S. Blake
Ericsson
D. Grossman
Motorola
A. Smith
Harbour Networks
May 2002
An Informal Management Model for Diffserv Routers
Status of this Memo
This memo provides information for the Internet community. It does
not specify an Internet standard of any kind. Distribution of this
memo is unlimited.
Copyright Notice
Copyright (C) The Internet Society (2002). All Rights Reserved.
Abstract
This document proposes an informal management model of Differentiated
Services (Diffserv) routers for use in their management and
configuration. This model defines functional datapath elements
(e.g., classifiers, meters, actions, marking, absolute dropping,
counting, multiplexing), algorithmic droppers, queues and schedulers.
It describes possible configuration parameters for these elements and
how they might be interconnected to realize the range of traffic
conditioning and per-hop behavior (PHB) functionalities described in
the Diffserv Architecture.
Table of Contents
1 Introduction ................................................. 3
2 Glossary ..................................................... 4
3 Conceptual Model ............................................. 7
3.1 Components of a Diffserv Router ............................ 7
3.1.1 Datapath ................................................. 7
3.1.2 Configuration and Management Interface ................... 9
3.1.3 Optional QoS Agent Module ................................ 10
3.2 Diffserv Functions at Ingress and Egress ................... 10
3.3 Shaping and Policing ....................................... 12
3.4 Hierarchical View of the Model ............................. 12
4 Classifiers .................................................. 13
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4.1 Definition ................................................. 13
4.1.1 Filters .................................................. 15
4.1.2 Overlapping Filters ...................................... 15
4.2 Examples ................................................... 16
4.2.1 Behavior Aggregate (BA) Classifier ....................... 16
4.2.2 Multi-Field (MF) Classifier .............................. 17
4.2.3 Free-form Classifier ..................................... 17
4.2.4 Other Possible Classifiers ............................... 18
5 Meters ....................................................... 19
5.1 Examples ................................................... 20
5.1.1 Average Rate Meter ....................................... 20
5.1.2 Exponential Weighted Moving Average (EWMA) Meter ......... 21
5.1.3 Two-Parameter Token Bucket Meter ......................... 21
5.1.4 Multi-Stage Token Bucket Meter ........................... 22
5.1.5 Null Meter ............................................... 23
6 Action Elements .............................................. 23
6.1 DSCP Marker ................................................ 24
6.2 Absolute Dropper ........................................... 24
6.3 Multiplexor ................................................ 25
6.4 Counter .................................................... 25
6.5 Null Action ................................................ 25
7 Queuing Elements ............................................. 25
7.1 Queuing Model .............................................. 26
7.1.1 FIFO Queue ............................................... 27
7.1.2 Scheduler ................................................ 28
7.1.3 Algorithmic Dropper ...................................... 30
7.2 Sharing load among traffic streams using queuing ........... 33
7.2.1 Load Sharing ............................................. 34
7.2.2 Traffic Priority ......................................... 35
8 Traffic Conditioning Blocks (TCBs) ........................... 35
8.1 TCB ........................................................ 36
8.1.1 Building blocks for Queuing .............................. 37
8.2 An Example TCB ............................................. 37
8.3 An Example TCB to Support Multiple Customers ............... 42
8.4 TCBs Supporting Microflow-based Services ................... 44
8.5 Cascaded TCBs .............................................. 47
9 Security Considerations ...................................... 47
10 Acknowledgments ............................................. 47
11 References .................................................. 47
Appendix A. Discussion of Token Buckets and Leaky Buckets ...... 50
Authors' Addresses ............................................. 55
Full Copyright Statement........................................ 56
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1. Introduction
Differentiated Services (Diffserv) [DSARCH] is a set of technologies
which allow network service providers to offer services with
different kinds of network quality-of-service (QoS) objectives to
different customers and their traffic streams. This document uses
terminology defined in [DSARCH] and [NEWTERMS] (some of these
definitions are included here in Section 2 for completeness).
The premise of Diffserv networks is that routers within the core of
the network handle packets in different traffic streams by forwarding
them using different per-hop behaviors (PHBs). The PHB to be applied
is indicated by a Diffserv codepoint (DSCP) in the IP header of each
packet [DSFIELD]. The DSCP markings are applied either by a trusted
upstream node, e.g., a customer, or by the edge routers on entry to
the Diffserv network.
The advantage of such a scheme is that many traffic streams can be
aggregated to one of a small number of behavior aggregates (BA),
which are each forwarded using the same PHB at the router, thereby
simplifying the processing and associated storage. In addition,
there is no signaling other than what is carried in the DSCP of each
packet, and no other related processing that is required in the core
of the Diffserv network since QoS is invoked on a packet-by-packet
basis.
The Diffserv architecture enables a variety of possible services
which could be deployed in a network. These services are reflected
to customers at the edges of the Diffserv network in the form of a
Service Level Specification (SLS - see [NEWTERMS]). Whilst further
discussion of such services is outside the scope of this document
(see [PDBDEF]), the ability to provide these services depends on the
availability of cohesive management and configuration tools that can
be used to provision and monitor a set of Diffserv routers in a
coordinated manner. To facilitate the development of such
configuration and management tools it is helpful to define a
conceptual model of a Diffserv router that abstracts away
implementation details of particular Diffserv routers from the
parameters of interest for configuration and management. The purpose
of this document is to define such a model.
The basic forwarding functionality of a Diffserv router is defined in
other specifications; e.g., [DSARCH, DSFIELD, AF-PHB, EF-PHB].
This document is not intended in any way to constrain or to dictate
the implementation alternatives of Diffserv routers. It is expected
that router implementers will demonstrate a great deal of variability
in their implementations. To the extent that implementers are able
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to model their implementations using the abstractions described in
this document, configuration and management tools will more readily
be able to configure and manage networks incorporating Diffserv
routers of assorted origins.
This model is intended to be abstract and capable of representing the
configuration parameters important to Diffserv functionality for a
variety of specific router implementations. It is not intended as a
guide to system implementation nor as a formal modeling description.
This model serves as the rationale for the design of an SNMP MIB
[DSMIB] and for other configuration interfaces (e.g., other policy-
management protocols) and, possibly, more detailed formal models
(e.g., [QOSDEVMOD]): these should all be consistent with this model.
o Section 3 starts by describing the basic high-level blocks of a
Diffserv router. It explains the concepts used in the model,
including the hierarchical management model for these blocks which
uses low-level functional datapath elements such as Classifiers,
Actions, Queues.
o Section 4 describes Classifier elements.
o Section 5 discusses Meter elements.
o Section 6 discusses Action elements.
o Section 7 discusses the basic queuing elements of Algorithmic
Droppers, Queues, and Schedulers and their functional behaviors
(e.g., traffic shaping).
o Section 8 shows how the low-level elements can be combined to
build modules called Traffic Conditioning Blocks (TCBs) which are
useful for management purposes.
o Section 9 discusses security concerns.
o Appendix A contains a brief discussion of the token bucket and
leaky bucket algorithms used in this model and some of the
practical effects of the use of token buckets within the Diffserv
architecture.
2. Glossary
This document uses terminology which is defined in [DSARCH]. There
is also current work-in-progress on this terminology in the IETF and
some of the definitions provided here are taken from that work. Some
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of the terms from these other references are defined again here in
order to provide additional detail, along with some new terms
specific to this document.
Absolute A functional datapath element which simply discards all
Dropper packets arriving at its input.
Algorithmic A functional datapath element which selectively
Dropper discards packets that arrive at its input, based on a
discarding algorithm. It has one data input and one
output.
Classifier A functional datapath element which consists of filters
that select matching and non-matching packets. Based
on this selection, packets are forwarded along the
appropriate datapath within the router. A classifier,
therefore, splits a single incoming traffic stream into
multiple outgoing streams.
Counter A functional datapath element which updates a packet
counter and also an octet counter for every
packet that passes through it.
Datapath A conceptual path taken by packets with particular
characteristics through a Diffserv router. Decisions
as to the path taken by a packet are made by functional
datapath elements such as Classifiers and Meters.
Filter A set of wildcard, prefix, masked, range and/or exact
match conditions on the content of a packet's
headers or other data, and/or on implicit or derived
attributes associated with the packet. A filter is
said to match only if each condition is satisfied.
Functional A basic building block of the conceptual router.
Datapath Typical elements are Classifiers, Meters, Actions,
Element Algorithmic Droppers, Queues and Schedulers.
Multiplexer A multiplexor.
(Mux)
Multiplexor A functional datapath element that merges multiple
(Mux) traffic streams (datapaths) into a single traffic
stream (datapath).
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Non-work- A property of a scheduling algorithm such that it
conserving services packets no sooner than a scheduled departure
time, even if this means leaving packets queued
while the output (e.g., a network link or connection
to the next element) is idle.
Policing The process of comparing the arrival of data packets
against a temporal profile and forwarding, delaying
or dropping them so as to make the output stream
conformant to the profile.
Queuing A combination of functional datapath elements
Block that modulates the transmission of packets belonging
to a traffic streams and determines their
ordering, possibly storing them temporarily or
discarding them.
Scheduling An algorithm which determines which queue of a set
algorithm of queues to service next. This may be based on the
relative priority of the queues, on a weighted fair
bandwidth sharing policy or some other policy. Such
an algorithm may be either work-conserving or non-
work-conserving.
Service-Level A set of parameters and their values which together
Specification define the treatment offered to a traffic stream by a
(SLS) Diffserv domain.
Shaping The process of delaying packets within a traffic stream
to cause it to conform to some defined temporal
profile. Shaping can be implemented using a queue
serviced by a non-work-conserving scheduling algorithm.
Traffic A logical datapath entity consisting of a number of
Conditioning functional datapath elements interconnected in
Block (TCB) such a way as to perform a specific set of traffic
conditioning functions on an incoming traffic stream.
A TCB can be thought of as an entity with one
input and one or more outputs and a set of control
parameters.
Traffic A set of parameters and their values which together
Conditioning specify a set of classifier rules and a traffic
Specification profile. A TCS is an integral element of a SLS.
(TCS)
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Work- A property of a scheduling algorithm such that it
conserving services a packet, if one is available, at every
transmission opportunity.
3. Conceptual Model
This section introduces a block diagram of a Diffserv router and
describes the various components illustrated in Figure 1. Note that
a Diffserv core router is likely to require only a subset of these
components: the model presented here is intended to cover the case of
both Diffserv edge and core routers.
3.1. Components of a Diffserv Router
The conceptual model includes abstract definitions for the following:
o Traffic Classification elements.
o Metering functions.
o Actions of Marking, Absolute Dropping, Counting, and
Multiplexing.
o Queuing elements, including capabilities of algorithmic
dropping and scheduling.
o Certain combinations of the above functional datapath elements
into higher-level blocks known as Traffic Conditioning Blocks
(TCBs).
The components and combinations of components described in this
document form building blocks that need to be manageable by Diffserv
configuration and management tools. One of the goals of this
document is to show how a model of a Diffserv device can be built
using these component blocks. This model is in the form of a
connected directed acyclic graph (DAG) of functional datapath
elements that describes the traffic conditioning and queuing
behaviors that any particular packet will experience when forwarded
to the Diffserv router. Figure 1 illustrates the major functional
blocks of a Diffserv router.
3.1.1. Datapath
An ingress interface, routing core, and egress interface are
illustrated at the center of the diagram. In actual router
implementations, there may be an arbitrary number of ingress and
egress interfaces interconnected by the routing core. The routing
core element serves as an abstraction of a router's normal routing
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and switching functionality. The routing core moves packets between
interfaces according to policies outside the scope of Diffserv (note:
it is possible that such policies for output-interface selection
might involve use of packet fields such as the DSCP but this is
outside the scope of this model). The actual queuing delay and
packet loss behavior of a specific router's switching
fabric/backplane is not modeled by the routing core; these should be
modeled using the functional datapath elements described later. The
routing core of this model can be thought of as an infinite
bandwidth, zero-delay interconnect between interfaces - properties
like the behavior of the core when overloaded need to be reflected
back into the queuing elements that are modeled around it (e.g., when
too much traffic is directed across the core at an egress interface),
the excess must either be dropped or queued somewhere: the elements
performing these functions must be modeled on one of the interfaces
involved.
The components of interest at the ingress to and egress from
interfaces are the functional datapath elements (e.g., Classifiers,
Queuing elements) that support Diffserv traffic conditioning and
per-hop behaviors [DSARCH]. These are the fundamental components
comprising a Diffserv router and are the focal point of this model.
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+---------------+
| Diffserv |
Mgmt | configuration |
<----+-->| & management |------------------+
SNMP,| | interface | |
COPS | +---------------+ |
etc. | | |
| | |
| v v
| +-------------+ +-------------+
| | ingress i/f | +---------+ | egress i/f |
-------->| classify, |-->| routing |-->| classify, |---->
data | | meter, | | core | | meter |data out
in | | action, | +---------+ | action, |
| | queuing | | queuing |
| +-------------+ +-------------+
| ^ ^
| | |
| | |
| +------------+ |
+-->| QOS agent | |
-------->| (optional) |---------------------+
QOS |(e.g., RSVP)|
cntl +------------+
msgs
Figure 1: Diffserv Router Major Functional Blocks
3.1.2. Configuration and Management Interface
Diffserv operating parameters are monitored and provisioned through
this interface. Monitored parameters include statistics regarding
traffic carried at various Diffserv service levels. These statistics
may be important for accounting purposes and/or for tracking
compliance to Traffic Conditioning Specifications (TCSs) negotiated
with customers. Provisioned parameters are primarily the TCS
parameters for Classifiers and Meters and the associated PHB
configuration parameters for Actions and Queuing elements. The
network administrator interacts with the Diffserv configuration and
management interface via one or more management protocols, such as
SNMP or COPS, or through other router configuration tools such as
serial terminal or telnet consoles.
Specific policy rules and goals governing the Diffserv behavior of a
router are presumed to be installed by policy management mechanisms.
However, Diffserv routers are always subject to implementation limits
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which scope the kinds of policies which can be successfully
implemented by the router. External reporting of such implementation
capabilities is considered out of scope for this document.
3.1.3. Optional QoS Agent Module
Diffserv routers may snoop or participate in either per-microflow or
per-flow-aggregate signaling of QoS requirements [E2E] (e.g., using
the RSVP protocol). Snooping of RSVP messages may be used, for
example, to learn how to classify traffic without actually
participating as a RSVP protocol peer. Diffserv routers may reject
or admit RSVP reservation requests to provide a means of admission
control to Diffserv-based services or they may use these requests to
trigger provisioning changes for a flow-aggregation in the Diffserv
network. A flow-aggregation in this context might be equivalent to a
Diffserv BA or it may be more fine-grained, relying on a multi-field
(MF) classifier [DSARCH]. Note that the conceptual model of such a
router implements the Integrated Services Model as described in
[INTSERV], applying the control plane controls to the data classified
and conditioned in the data plane, as described in [E2E].
Note that a QoS Agent component of a Diffserv router, if present,
might be active only in the control plane and not in the data plane.
In this scenario, RSVP could be used merely to signal reservation
state without installing any actual reservations in the data plane of
the Diffserv router: the data plane could still act purely on
Diffserv DSCPs and provide PHBs for handling data traffic without the
normal per-microflow handling expected to support some Intserv
services.
3.2. Diffserv Functions at Ingress and Egress
This document focuses on the Diffserv-specific components of the
router. Figure 2 shows a high-level view of ingress and egress
interfaces of a router. The diagram illustrates two Diffserv router
interfaces, each having a set of ingress and a set of egress
elements. It shows classification, metering, action and queuing
functions which might be instantiated at each interface's ingress and
egress.
The simple diagram of Figure 2 assumes that the set of Diffserv
functions to be carried out on traffic on a given interface are
independent of those functions on all other interfaces. There are
some architectures where Diffserv functions may be shared amongst
multiple interfaces (e.g., processor and buffering resources that
handle multiple interfaces on the same line card before forwarding
across a routing core). The model presented in this document may be
easily extended to handle such cases; however, this topic is not
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treated further here as it leads to excessive complexity in the
explanation of the concepts.
Interface A Interface B
+-------------+ +---------+ +-------------+
| ingress: | | | | egress: |
| classify, | | | | classify, |
--->| meter, |---->| |---->| meter, |--->
| action, | | | | action, |
| queuing | | routing | | queuing |
+-------------+ | core | +-------------+
| egress: | | | | ingress: |
| classify, | | | | classify, |
<---| meter, |<----| |<----| meter, |<---
| action, | | | | action, |
| queuing | +---------+ | queuing |
+-------------+ +-------------+
Figure 2. Traffic Conditioning and Queuing Elements
In principle, if one were to construct a network entirely out of
two-port routers (connected by LANs or similar media), then it might
be necessary for each router to perform four QoS control functions in
the datapath on traffic in each direction:
- Classify each message according to some set of rules, possibly
just a "match everything" rule.
- If necessary, determine whether the data stream the message is
part of is within or outside its rate by metering the stream.
- Perform a set of resulting actions, including applying a drop
policy appropriate to the classification and queue in question and
perhaps additionally marking the traffic with a Differentiated
Services Code Point (DSCP) [DSFIELD].
- Enqueue the traffic for output in the appropriate queue. The
scheduling of output from this queue may lead to shaping of the
traffic or may simply cause it to be forwarded with some minimum
rate or maximum latency assurance.
If the network is now built out of N-port routers, the expected
behavior of the network should be identical. Therefore, this model
must provide for essentially the same set of functions at the ingress
as on the egress of a router's interfaces. The one point of
difference in the model between ingress and the egress is that all
traffic at the egress of an interface is queued, while traffic at the
ingress to an interface is likely to be queued only for shaping
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purposes, if at all. Therefore, equivalent functional datapath
elements may be modeled at both the ingress to and egress from an
interface.
Note that it is not mandatory that each of these functional datapath
elements be implemented at both ingress and egress; equally, the
model allows that multiple sets of these elements may be placed in
series and/or in parallel at ingress or at egress. The arrangement
of elements is dependent on the service requirements on a particular
interface on a particular router. By modeling these elements at both
ingress and egress, it is not implied that they must be implemented
in this way in a specific router. For example, a router may
implement all shaping and PHB queuing at the interface egress or may
instead implement it only at the ingress. Furthermore, the
classification needed to map a packet to an egress queue (if present)
need not be implemented at the egress but instead might be
implemented at the ingress, with the packet passed through the
routing core with in-band control information to allow for egress
queue selection.
Specifically, some interfaces will be at the outer "edge" and some
will be towards the "core" of the Diffserv domain. It is to be
expected (from the general principles guiding the motivation of
Diffserv) that "edge" interfaces, or at least the routers that
contain them, will implement more complexity and require more
configuration than those in the core although this is obviously not a
requirement.
3.3. Shaping and Policing
Diffserv nodes may apply shaping, policing and/or marking to traffic
streams that exceed the bounds of their TCS in order to prevent one
traffic stream from seizing more than its share of resources from a
Diffserv network. In this model, Shaping, sometimes considered as a
TC action, is treated as a function of queuing elements - see section
7. Algorithmic Dropping techniques (e.g., RED) are similarly treated
since they are often closely associated with queues. Policing is
modeled as either a concatenation of a Meter with an Absolute Dropper
or as a concatenation of an Algorithmic Dropper with a Scheduler.
These elements will discard packets which exceed the TCS.
3.4. Hierarchical View of the Model
From a device-level configuration management perspective, the
following hierarchy exists:
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At the lowest level considered here, there are individual
functional datapath elements, each with their own configuration
parameters and management counters and flags.
At the next level, the network administrator manages groupings of
these functional datapath elements interconnected in a DAG. These
functional datapath elements are organized in self-contained TCBs
which are used to implement some desired network policy (see
Section 8). One or more TCBs may be instantiated at each
interface's ingress or egress; they may be connected in series
and/or in parallel configurations on the multiple outputs of a
preceding TCB. A TCB can be thought of as a "black box" with one
input and one or more outputs (in the data path). Each interface
may have a different TCB configuration and each direction (ingress
or egress) may too.
At the topmost level considered here, the network administrator
manages interfaces. Each interface has ingress and egress
functionality, with each of these expressed as one or more TCBs.
This level of the hierarchy is what was illustrated in Figure 2.
Further levels may be built on top of this hierarchy, in particular
ones for aiding in the repetitive configuration tasks likely for
routers with many interfaces: some such "template" tools for Diffserv
routers are outside the scope of this model but are under study by
other working groups within IETF.
4. Classifiers
4.1. Definition
Classification is performed by a classifier element. Classifiers are
1:N (fan-out) devices: they take a single traffic stream as input and
generate N logically separate traffic streams as output. Classifiers
are parameterized by filters and output streams. Packets from the
input stream are sorted into various output streams by filters which
match the contents of the packet or possibly match other attributes
associated with the packet. Various types of classifiers using
different filters are described in the following sections. Figure 3
illustrates a classifier, where the outputs connect to succeeding
functional datapath elements.
The simplest possible Classifier element is one that matches all
packets that are applied at its input. In this case, the Classifier
element is just a no-op and may be omitted.
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Note that we allow a Multiplexor (see Section 6.5) before the
Classifier to allow input from multiple traffic streams. For
example, if traffic streams originating from multiple ingress
interfaces feed through a single Classifier then the interface number
could be one of the packet classification keys used by the
Classifier. This optimization may be important for scalability in
the management plane. Classifiers may also be cascaded in sequence
to perform more complex lookup operations whilst still maintaining
such scalability.
Another example of a packet attribute could be an integer
representing the BGP community string associated with the packet's
best-matching route. Other contextual information may also be used
by a Classifier (e.g., knowledge that a particular interface faces a
Diffserv domain or a legacy IP TOS domain [DSARCH] could be used when
determining whether a DSCP is present or not).
unclassified classified
traffic traffic
+------------+
| |--> match Filter1 --> OutputA
------->| classifier |--> match Filter2 --> OutputB
| |--> no match --> OutputC
+------------+
Figure 3. An Example Classifier
The following BA classifier separates traffic into one of three
output streams based on matching filters:
Filter Matched Output Stream
-------------- ---------------
Filter1 A
Filter2 B
no match C
Where the filters are defined to be the following BA filters
([DSARCH], Section 4.2.1):
Filter DSCP
------ ------
Filter1 101010
Filter2 111111
Filter3 ****** (wildcard)
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4.1.1. Filters
A filter consists of a set of conditions on the component values of a
packet's classification key (the header values, contents, and
attributes relevant for classification). In the BA classifier
example above, the classification key consists of one packet header
field, the DSCP, and both Filter1 and Filter2 specify exact-match
conditions on the value of the DSCP. Filter3 is a wildcard default
filter which matches every packet, but which is only selected in the
event that no other more specific filter matches.
In general there are a set of possible component conditions including
exact, prefix, range, masked and wildcard matches. Note that ranges
can be represented (with less efficiency) as a set of prefixes and
that prefix matches are just a special case of both masked and range
matches.
In the case of a MF classifier, the classification key consists of a
number of packet header fields. The filter may specify a different
condition for each key component, as illustrated in the example below
for a IPv4/TCP classifier:
Filter IPv4 Src Addr IPv4 Dest Addr TCP SrcPort TCP DestPort
------ ------------- -------------- ----------- ------------
Filter4 172.31.8.1/32 172.31.3.X/24 X 5003
In this example, the fourth octet of the destination IPv4 address and
the source TCP port are wildcard or "don't care".
MF classification of IP-fragmented packets is impossible if the
filter uses transport-layer port numbers (e.g., TCP port numbers).
MTU-discovery is therefore a prerequisite for proper operation of a
Diffserv network that uses such classifiers.
4.1.2. Overlapping Filters
Note that it is easy to define sets of overlapping filters in a
classifier. For example:
Filter IPv4 Src Addr IPv4 Dest Addr
------ ------------- --------------
Filter5 172.31.8.X/24 X/0
Filter6 X/0 172.30.10.1/32
A packet containing {IP Dest Addr 172.31.8.1, IP Src Addr
172.30.10.1} cannot be uniquely classified by this pair of filters
and so a precedence must be established between Filter5 and Filter6
in order to break the tie. This precedence must be established
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either (a) by a manager which knows that the router can accomplish
this particular ordering (e.g., by means of reported capabilities),
or (b) by the router along with a mechanism to report to a manager
which precedence is being used. Such precedence mechanisms must be
supported in any translation of this model into specific syntax for
configuration and management protocols.
As another example, one might want first to disallow certain
applications from using the network at all, or to classify some
individual traffic streams that are not Diffserv-marked. Traffic
that is not classified by those tests might then be inspected for a
DSCP. The word "then" implies sequence and this must be specified by
means of precedence.
An unambiguous classifier requires that every possible classification
key match at least one filter (possibly the wildcard default) and
that any ambiguity between overlapping filters be resolved by
precedence. Therefore, the classifiers on any given interface must
be "complete" and will often include an "everything else" filter as
the lowest precedence element in order for the result of
classification to be deterministic. Note that this completeness is
only required of the first classifier that incoming traffic will meet
as it enters an interface - subsequent classifiers on an interface
only need to handle the traffic that it is known that they will
receive.
This model of classifier operation makes the assumption that all
filters of the same precedence be applied simultaneously. Whilst
convenient from a modeling point-of-view, this may or may not be how
the classifier is actually implemented - this assumption is not
intended to dictate how the implementation actually handles this,
merely to clearly define the required end result.
4.2. Examples
4.2.1. Behavior Aggregate (BA) Classifier
The simplest Diffserv classifier is a behavior aggregate (BA)
classifier [DSARCH]. A BA classifier uses only the Diffserv
codepoint (DSCP) in a packet's IP header to determine the logical
output stream to which the packet should be directed. We allow only
an exact-match condition on this field because the assigned DSCP
values have no structure, and therefore no subset of DSCP bits are
significant.
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The following defines a possible BA filter:
Filter8:
Type: BA
Value: 111000
4.2.2. Multi-Field (MF) Classifier
Another type of classifier is a multi-field (MF) classifier [DSARCH].
This classifies packets based on one or more fields in the packet
(possibly including the DSCP). A common type of MF classifier is a
6-tuple classifier that classifies based on six fields from the IP
and TCP or UDP headers (destination address, source address, IP
protocol, source port, destination port, and DSCP). MF classifiers
may classify on other fields such as MAC addresses, VLAN tags, link-
layer traffic class fields, or other higher-layer protocol fields.
The following defines a possible MF filter:
Filter9:
Type: IPv4-6-tuple
IPv4DestAddrValue: 0.0.0.0
IPv4DestAddrMask: 0.0.0.0
IPv4SrcAddrValue: 172.31.8.0
IPv4SrcAddrMask: 255.255.255.0
IPv4DSCP: 28
IPv4Protocol: 6
IPv4DestL4PortMin: 0
IPv4DestL4PortMax: 65535
IPv4SrcL4PortMin: 20
IPv4SrcL4PortMax: 20
A similar type of classifier can be defined for IPv6.
4.2.3. Free-form Classifier
A Free-form classifier is made up of a set of user definable
arbitrary filters each made up of {bit-field size, offset (from head
of packet), mask}:
Classifier2:
Filter12: OutputA
Filter13: OutputB
Default: OutputC
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Filter12:
Type: FreeForm
SizeBits: 3 (bits)
Offset: 16 (bytes)
Value: 100 (binary)
Mask: 101 (binary)
Filter13:
Type: FreeForm
SizeBits: 12 (bits)
Offset: 16 (bytes)
Value: 100100000000 (binary)
Mask: 111111111111 (binary)
Free-form filters can be combined into filter groups to form very
powerful filters.
4.2.4. Other Possible Classifiers
Classification may also be performed based on information at the
datalink layer below IP (e.g., VLAN or datalink-layer priority) or
perhaps on the ingress or egress IP, logical or physical interface
identifier (e.g., the incoming channel number on a channelized
interface). A classifier that filters based on IEEE 802.1p Priority
and on 802.1Q VLAN-ID might be represented as:
Classifier3:
Filter14 AND Filter15: OutputA
Default: OutputB
Filter14: -- priority 4 or 5
Type: Ieee8021pPriority
Value: 100 (binary)
Mask: 110 (binary)
Filter15: -- VLAN 2304
Type: Ieee8021QVlan
Value: 100100000000 (binary)
Mask: 111111111111 (binary)
Such classifiers may be the subject of other standards or may be
proprietary to a router vendor but they are not discussed further
here.
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5. Meters
Metering is defined in [DSARCH]. Diffserv network providers may
choose to offer services to customers based on a temporal (i.e.,
rate) profile within which the customer submits traffic for the
service. In this event, a meter might be used to trigger real-time
traffic conditioning actions (e.g., marking) by routing a non-
conforming packet through an appropriate next-stage action element.
Alternatively, by counting conforming and/or non-conforming traffic
using a Counter element downstream of the Meter, it might also be
used to help in collecting data for out-of-band management functions
such as billing applications.
Meters are logically 1:N (fan-out) devices (although a multiplexor
can be used in front of a meter). Meters are parameterized by a
temporal profile and by conformance levels, each of which is
associated with a meter's output. Each output can be connected to
another functional element.
Note that this model of a meter differs slightly from that described
in [DSARCH]. In that description the meter is not a datapath element
but is instead used to monitor the traffic stream and send control
signals to action elements to dynamically modulate their behavior
based on the conformance of the packet. This difference in the
description does not change the function of a meter. Figure 4
illustrates a meter with 3 levels of conformance.
In some Diffserv examples (e.g., [AF-PHB]), three levels of
conformance are discussed in terms of colors, with green representing
conforming, yellow representing partially conforming and red
representing non-conforming. These different conformance levels may
be used to trigger different queuing, marking or dropping treatment
later on in the processing. Other example meters use a binary notion
of conformance; in the general case N levels of conformance can be
supported. In general there is no constraint on the type of
functional datapath element following a meter output, but care must
be taken not to inadvertently configure a datapath that results in
packet reordering that is not consistent with the requirements of the
relevant PHB specification.
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unmetered metered
traffic traffic
+---------+
| |--------> conformance A
--------->| meter |--------> conformance B
| |--------> conformance C
+---------+
Figure 4. A Generic Meter
A meter, according to this model, measures the rate at which packets
making up a stream of traffic pass it, compares the rate to some set
of thresholds, and produces some number of potential results (two or
more): a given packet is said to be "conformant" to a level of the
meter if, at the time that the packet is being examined, the stream
appears to be within the rate limit for the profile associated with
that level. A fuller discussion of conformance to meter profiles
(and the associated requirements that this places on the schedulers
upstream) is provided in Appendix A.
5.1. Examples
The following are some examples of possible meters.
5.1.1. Average Rate Meter
An example of a very simple meter is an average rate meter. This
type of meter measures the average rate at which packets are
submitted to it over a specified averaging time.
An average rate profile may take the following form:
Meter1:
Type: AverageRate
Profile: Profile1
ConformingOutput: Queue1
NonConformingOutput: Counter1
Profile1:
Type: AverageRate
AverageRate: 120 kbps
Delta: 100 msec
A Meter measuring against this profile would continually maintain a
count that indicates the total number and/or cumulative byte-count of
packets arriving between time T (now) and time T - 100 msecs. So
long as an arriving packet does not push the count over 12 kbits in
the last 100 msec, the packet would be deemed conforming. Any packet
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that pushes the count over 12 kbits would be deemed non-conforming.
Thus, this Meter deems packets to correspond to one of two
conformance levels: conforming or non-conforming, and sends them on
for the appropriate subsequent treatment.
5.1.2. Exponential Weighted Moving Average (EWMA) Meter
The EWMA form of Meter is easy to implement in hardware and can be
parameterized as follows:
avg_rate(t) = (1 - Gain) * avg_rate(t') + Gain * rate(t)
t = t' + Delta
For a packet arriving at time t:
if (avg_rate(t) > AverageRate)
non-conforming
else
conforming
"Gain" controls the time constant (e.g., frequency response) of what
is essentially a simple IIR low-pass filter. "Rate(t)" measures the
number of incoming bytes in a small fixed sampling interval, Delta.
Any packet that arrives and pushes the average rate over a predefined
rate AverageRate is deemed non-conforming. An EWMA Meter profile
might look something like the following:
Meter2:
Type: ExpWeightedMovingAvg
Profile: Profile2
ConformingOutput: Queue1
NonConformingOutput: AbsoluteDropper1
Profile2:
Type: ExpWeightedMovingAvg
AverageRate: 25 kbps
Delta: 10 usec
Gain: 1/16
5.1.3. Two-Parameter Token Bucket Meter
A more sophisticated Meter might measure conformance to a token
bucket (TB) profile. A TB profile generally has two parameters, an
average token rate, R, and a burst size, B. TB Meters compare the
arrival rate of packets to the average rate specified by the TB
profile. Logically, tokens accumulate in a bucket at the average
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rate, R, up to a maximum credit which is the burst size, B. When a
packet of length L arrives, a conformance test is applied. There are
at least two such tests in widespread use:
Strict conformance
Packets of length L bytes are considered conforming only if there
are sufficient tokens available in the bucket at the time of
packet arrival for the complete packet (i.e., the current depth is
greater than or equal to L): no tokens may be borrowed from future
token allocations. For examples of this approach, see [SRTCM] and
[TRTCM].
Loose conformance
Packets of length L bytes are considered conforming if any tokens
are available in the bucket at the time of packet arrival: up to L
bytes may then be borrowed from future token allocations.
Packets are allowed to exceed the average rate in bursts up to the
burst size. For further discussion of loose and strict conformance
to token bucket profiles, as well as system and implementation
issues, see Appendix A.
A two-parameter TB meter has exactly two possible conformance levels
(conforming, non-conforming). Such a meter might appear as follows:
Meter3:
Type: SimpleTokenBucket
Profile: Profile3
ConformanceType: loose
ConformingOutput: Queue1
NonConformingOutput: AbsoluteDropper1
Profile3:
Type: SimpleTokenBucket
AverageRate: 200 kbps
BurstSize: 100 kbytes
5.1.4. Multi-Stage Token Bucket Meter
More complicated TB meters might define multiple burst sizes and more
conformance levels. Packets found to exceed the larger burst size
are deemed non-conforming. Packets found to exceed the smaller burst
size are deemed partially-conforming. Packets exceeding neither are
deemed conforming. Some token bucket meters designed for Diffserv
networks are described in more detail in [SRTCM, TRTCM]; in some of
these references, three levels of conformance are discussed in terms
of colors with green representing conforming, yellow representing
partially conforming, and red representing non-conforming. Note that
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these multiple-conformance-level meters can sometimes be implemented
using an appropriate sequence of multiple two-parameter TB meters.
A profile for a multi-stage TB meter with three levels of conformance
might look as follows:
Meter4:
Type: TwoRateTokenBucket
ProfileA: Profile4
ConformanceTypeA: strict
ConformingOutputA: Queue1
ProfileB: Profile5
ConformanceTypeB: strict
ConformingOutputB: Marker1
NonConformingOutput: AbsoluteDropper1
Profile4:
Type: SimpleTokenBucket
AverageRate: 100 kbps
BurstSize: 20 kbytes
Profile5:
Type: SimpleTokenBucket
AverageRate: 100 kbps
BurstSize: 100 kbytes
5.1.5. Null Meter
A null meter has only one output: always conforming, and no
associated temporal profile. Such a meter is useful to define in the
event that the configuration or management interface does not have
the flexibility to omit a meter in a datapath segment.
Meter5:
Type: NullMeter
Output: Queue1
6. Action Elements
The classifiers and meters described up to this point are fan-out
elements which are generally used to determine the appropriate action
to apply to a packet. The set of possible actions that can then be
applied include:
- Marking
- Absolute Dropping
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- Multiplexing
- Counting
- Null action - do nothing
The corresponding action elements are described in the following
sections.
6.1. DSCP Marker
DSCP Markers are 1:1 elements which set a codepoint (e.g., the DSCP
in an IP header). DSCP Markers may also act on unmarked packets
(e.g., those submitted with DSCP of zero) or may re-mark previously
marked packets. In particular, the model supports the application of
marking based on a preceding classifier match. The mark set in a
packet will determine its subsequent PHB treatment in downstream
nodes of a network and possibly also in subsequent processing stages
within this router.
DSCP Markers for Diffserv are normally parameterized by a single
parameter: the 6-bit DSCP to be marked in the packet header.
Marker1:
Type: DSCPMarker
Mark: 010010
6.2. Absolute Dropper
Absolute Droppers simply discard packets. There are no parameters
for these droppers. Because this Absolute Dropper is a terminating
point of the datapath and has no outputs, it is probably desirable to
forward the packet through a Counter Action first for instrumentation
purposes.
AbsoluteDropper1:
Type: AbsoluteDropper
Absolute Droppers are not the only elements than can cause a packet
to be discarded: another element is an Algorithmic Dropper element
(see Section 7.1.3). However, since this element's behavior is
closely tied the state of one or more queues, we choose to
distinguish it as a separate functional datapath element.
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6.3. Multiplexor
It is occasionally necessary to multiplex traffic streams into a
functional datapath element with a single input. A M:1 (fan-in)
multiplexor is a simple logical device for merging traffic streams.
It is parameterized by its number of incoming ports.
Mux1:
Type: Multiplexor
Output: Queue2
6.4. Counter
One passive action is to account for the fact that a data packet was
processed. The statistics that result might be used later for
customer billing, service verification or network engineering
purposes. Counters are 1:1 functional datapath elements which update
a counter by L and a packet counter by 1 every time a L-byte sized
packet passes through them. Counters can be used to count packets
about to be dropped by an Absolute Dropper or to count packets
arriving at or departing from some other functional element.
Counter1:
Type: Counter
Output: Queue1
6.5. Null Action
A null action has one input and one output. The element performs no
action on the packet. Such an element is useful to define in the
event that the configuration or management interface does not have
the flexibility to omit an action element in a datapath segment.
Null1:
Type: Null
Output: Queue1
7. Queuing Elements
Queuing elements modulate the transmission of packets belonging to
the different traffic streams and determine their ordering, possibly
storing them temporarily or discarding them. Packets are usually
stored either because there is a resource constraint (e.g., available
bandwidth) which prevents immediate forwarding, or because the
queuing block is being used to alter the temporal properties of a
traffic stream (i.e., shaping). Packets are discarded for one of the
following reasons:
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- because of buffering limitations.
- because a buffer threshold is exceeded (including when shaping
is performed).
- as a feedback control signal to reactive control protocols such
as TCP.
- because a meter exceeds a configured profile (i.e., policing).
The queuing elements in this model represent a logical abstraction of
a queuing system which is used to configure PHB-related parameters.
The model can be used to represent a broad variety of possible
implementations. However, it need not necessarily map one-to-one
with physical queuing systems in a specific router implementation.
Implementors should map the configurable parameters of the
implementation's queuing systems to these queuing element parameters
as appropriate to achieve equivalent behaviors.
7.1. Queuing Model
Queuing is a function which lends itself to innovation. It must be
modeled to allow a broad range of possible implementations to be
represented using common structures and parameters. This model uses
functional decomposition as a tool to permit the needed latitude.
Queuing systems perform three distinct, but related, functions: they
store packets, they modulate the departure of packets belonging to
various traffic streams and they selectively discard packets. This
model decomposes queuing into the component elements that perform
each of these functions: Queues, Schedulers, and Algorithmic
Droppers, respectively. These elements may be connected together as
part of a TCB, as described in section 8.
The remainder of this section discusses FIFO Queues: typically, the
Queue element of this model will be implemented as a FIFO data
structure. However, this does not preclude implementations which are
not strictly FIFO, in that they also support operations that remove
or examine packets (e.g., for use by discarders) other than at the
head or tail. However, such operations must not have the effect of
reordering packets belonging to the same microflow.
Note that the term FIFO has multiple different common usages: it is
sometimes taken to mean, among other things, a data structure that
permits items to be removed only in the order in which they were
inserted or a service discipline which is non-reordering.
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7.1.1. FIFO Queue
In this model, a FIFO Queue element is a data structure which at any
time may contain zero or more packets. It may have one or more
thresholds associated with it. A FIFO has one or more inputs and
exactly one output. It must support an enqueue operation to add a
packet to the tail of the queue and a dequeue operation to remove a
packet from the head of the queue. Packets must be dequeued in the
order in which they were enqueued. A FIFO has a current depth, which
indicates the number of packets and/or bytes that it contains at a
particular time. FIFOs in this model are modeled without inherent
limits on their depth - obviously this does not reflect the reality
of implementations: FIFO size limits are modeled here by an
algorithmic dropper associated with the FIFO, typically at its input.
It is quite likely that every FIFO will be preceded by an algorithmic
dropper. One exception might be the case where the packet stream has
already been policed to a profile that can never exceed the scheduler
bandwidth available at the FIFO's output - this would not need an
algorithmic dropper at the input to the FIFO.
This representation of a FIFO allows for one common type of depth
limit, one that results from a FIFO supplied from a limited pool of
buffers, shared between multiple FIFOs.
In an implementation, packets are presumably stored in one or more
buffers. Buffers are allocated from one or more free buffer pools.
If there are multiple instances of a FIFO, their packet buffers may
or may not be allocated out of the same free buffer pool. Free
buffer pools may also have one or more thresholds associated with
them, which may affect discarding and/or scheduling. Other than
this, buffering mechanisms are implementation specific and not part
of this model.
A FIFO might be represented using the following parameters:
Queue1:
Type: FIFO
Output: Scheduler1
Note that a FIFO must provide triggers and/or current state
information to other elements upstream and downstream from it: in
particular, it is likely that the current depth will need to be used
by Algorithmic Dropper elements placed before or after the FIFO. It
will also likely need to provide an implicit "I have packets for you"
signal to downstream Scheduler elements.
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7.1.2. Scheduler
A scheduler is an element which gates the departure of each packet
that arrives at one of its inputs, based on a service discipline. It
has one or more inputs and exactly one output. Each input has an
upstream element to which it is connected, and a set of parameters
that affects the scheduling of packets received at that input.
The service discipline (also known as a scheduling algorithm) is an
algorithm which might take any of the following as its input(s):
a) static parameters such as relative priority associated with each
of the scheduler's inputs.
b) absolute token bucket parameters for maximum or minimum rates
associated with each of the scheduler's inputs.
c) parameters, such as packet length or DSCP, associated with the
packet currently present at its input.
d) absolute time and/or local state.
Possible service disciplines fall into a number of categories,
including (but not limited to) first come, first served (FCFS),
strict priority, weighted fair bandwidth sharing (e.g., WFQ), rate-
limited strict priority, and rate-based. Service disciplines can be
further distinguished by whether they are work-conserving or non-
work-conserving (see Glossary). Non-work-conserving schedulers can
be used to shape traffic streams to match some profile by delaying
packets that might be deemed non-conforming by some downstream node:
a packet is delayed until such time as it would conform to a
downstream meter using the same profile.
[DSARCH] defines PHBs without specifying required scheduling
algorithms. However, PHBs such as the class selectors [DSFIELD], EF
[EF-PHB] and AF [AF-PHB] have descriptions or configuration
parameters which strongly suggest the sort of scheduling discipline
needed to implement them. This document discusses a minimal set of
queue parameters to enable realization of these PHBs. It does not
attempt to specify an all-embracing set of parameters to cover all
possible implementation models. A minimal set includes:
a) a minimum service rate profile which allows rate guarantees for
each traffic stream as required by EF and AF without specifying
the details of how excess bandwidth between these traffic streams
is shared. Additional parameters to control this behavior should
be made available, but are dependent on the particular scheduling
algorithm implemented.
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b) a service priority, used only after the minimum rate profiles of
all inputs have been satisfied, to decide how to allocate any
remaining bandwidth.
c) a maximum service rate profile, for use only with a non-work-
conserving service discipline.
Any one of these profiles is composed, for the purposes of this
model, of both a rate (in suitable units of bits, bytes or larger
chunks in some unit of time) and a burst size, as discussed further
in Appendix A.
By way of example, for an implementation of the EF PHB using a strict
priority scheduling algorithm that assumes that the aggregate EF rate
has been appropriately bounded by upstream policing to avoid
starvation of other BAs, the service rate profiles are not used: the
minimum service rate profile would be defaulted to zero and the
maximum service rate profile would effectively be the "line rate".
Such an implementation, with multiple priority classes, could also be
used for the Diffserv class selectors [DSFIELD].
Alternatively, setting the service priority values for each input to
the scheduler to the same value enables the scheduler to satisfy the
minimum service rates for each input, so long as the sum of all
minimum service rates is less than or equal to the line rate.
For example, a non-work-conserving scheduler, allocating spare
bandwidth equally between all its inputs, might be represented using
the following parameters:
Scheduler1:
Type: Scheduler2Input
Input1:
MaxRateProfile: Profile1
MinRateProfile: Profile2
Priority: none
Input2:
MaxRateProfile: Profile3
MinRateProfile: Profile4
Priority: none
A work-conserving scheduler might be represented using the following
parameters:
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Scheduler2:
Type: Scheduler3Input
Input1:
MaxRateProfile: WorkConserving
MinRateProfile: Profile5
Priority: 1
Input2:
MaxRateProfile: WorkConserving
MinRateProfile: Profile6
Priority: 2
Input3:
MaxRateProfile: WorkConserving
MinRateProfile: none
Priority: 3
7.1.3. Algorithmic Dropper
An Algorithmic Dropper is an element which selectively discards
packets that arrive at its input, based on a discarding algorithm.
It has one data input and one output. In this model (but not
necessarily in a real implementation), a packet enters the dropper at
its input and either its buffer is returned to a free buffer pool or
the packet exits the dropper at the output.
Alternatively, an Algorithmic Dropper can be thought of as invoking
operations on a FIFO Queue which selectively remove a packet and
return its buffer to the free buffer pool based on a discarding
algorithm. In this case, the operation could be modeled as being a
side-effect on the FIFO upon which it operated, rather than as having
a discrete input and output. This treatment is equivalent and we
choose the one described in the previous paragraph for this model.
One of the primary characteristics of an Algorithmic Dropper is the
choice of which packet (if any) is to be dropped: for the purposes of
this model, we restrict the packet selection choices to one of the
following and we indicate the choice by the relative positions of
Algorithmic Dropper and FIFO Queue elements in the model:
a) selection of a packet that is about to be added to the tail of a
queue (a "Tail Dropper"): the output of the Algorithmic Dropper
element is connected to the input of the relevant FIFO Queue
element.
b) a packet that is currently at the head of a queue (a "Head
Dropper"): the output of the FIFO Queue element is connected to
the input of the Algorithmic Dropper element.
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Other packet selection methods could be added to this model in the
form of a different type of datapath element.
The Algorithmic Dropper is modeled as having a single input. It is
possible that packets which were classified differently by a
Classifier in this TCB will end up passing through the same dropper.
The dropper's algorithm may need to apply different calculations
based on characteristics of the incoming packet (e.g., its DSCP). So
there is a need, in implementations of this model, to be able to
relate information about which classifier element was matched by a
packet from a Classifier to an Algorithmic Dropper. In the rare
cases where this is required, the chosen model is to insert another
Classifier element at this point in the flow and for it to feed into
multiple Algorithmic Dropper elements, each one implementing a drop
calculation that is independent of any classification keys of the
packet: this will likely require the creation of a new TCB to contain
the Classifier and the Algorithmic Dropper elements.
NOTE: There are many other formulations of a model that could
represent this linkage that are different from the one described
above: one formulation would have been to have a pointer from one
of the drop probability calculation algorithms inside the dropper
to the original Classifier element that selects this algorithm.
Another way would have been to have multiple "inputs" to the
Algorithmic Dropper element fed from the preceding elements,
leading eventually back to the Classifier elements that matched
the packet. Yet another formulation might have been for the
Classifier to (logically) include some sort of "classification
identifier" along with the packet along its path, for use by any
subsequent element. And yet another could have been to include a
classifier inside the dropper, in order for it to pick out the
drop algorithm to be applied. These other approaches could be
used by implementations but were deemed to be less clear than the
approach taken here.
An Algorithmic Dropper, an example of which is illustrated in Figure
5, has one or more triggers that cause it to make a decision whether
or not to drop one (or possibly more than one) packet. A trigger may
be internal (the arrival of a packet at the input to the dropper) or
it may be external (resulting from one or more state changes at
another element, such as a FIFO Queue depth crossing a threshold or a
scheduling event). It is likely that an instantaneous FIFO depth
will need to be smoothed over some averaging interval before being
used as a useful trigger. Some dropping algorithms may require
several trigger inputs feeding back from events elsewhere in the
system (e.g., depth-smoothing functions that calculate averages over
more than one time interval).
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+------------------+ +-----------+
| +-------+ | n |smoothing |
| |trigger|<----------/---|function(s)|
| |calc. | | |(optional) |
| +-------+ | +-----------+
| | | ^
| v | |Depth
Input | +-------+ no | ------------+ to Scheduler
---------->|discard|--------------> |x|x|x|x|------->
| | ? | | ------------+
| +-------+ | FIFO
| |yes |
| | | | |
| | v | count + |
| +---+ bit-bucket|
+------------------+
Algorithmic
Dropper
Figure 5. Example of Algorithmic Dropper from Tail of a Queue
A trigger may be a boolean combination of events (e.g., a FIFO depth
exceeding a threshold OR a buffer pool depth falling below a
threshold). It takes as its input some set of dynamic parameters
(e.g., smoothed or instantaneous FIFO depth), and some set of static
parameters (e.g., thresholds), and possibly other parameters
associated with the packet. It may also have internal state (e.g.,
history of its past actions). Note that, although an Algorithmic
Dropper may require knowledge of data fields in a packet, as
discovered by a Classifier in the same TCB, it may not modify the
packet (i.e., it is not a marker).
The result of the trigger calculation is that the dropping algorithm
makes a decision on whether to forward or to discard a packet. The
discarding function is likely to keep counters regarding the
discarded packets (there is no appropriate place here to include a
Counter Action element).
The example in Figure 5 also shows a FIFO Queue element from whose
tail the dropping is to take place and whose depth characteristics
are used by this Algorithmic Dropper. It also shows where a depth-
smoothing function might be included: smoothing functions are outside
the scope of this document and are not modeled explicitly here, we
merely indicate where they might be added.
RED, RED-on-In-and-Out (RIO) and Drop-on-threshold are examples of
dropping algorithms. Tail-dropping and head-dropping are effected by
the location of the Algorithmic Dropper element relative to the FIFO
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Queue element. As an example, a dropper using a RIO algorithm might
be represented using 2 Algorithmic Droppers with the following
parameters:
AlgorithmicDropper1: (for in-profile traffic)
Type: AlgorithmicDropper
Discipline: RED
Trigger: Internal
Output: Fifo1
MinThresh: Fifo1.Depth > 20 kbyte
MaxThresh: Fifo1.Depth > 30 kbyte
SampleWeight .002
MaxDropProb 1%
AlgorithmicDropper2: (for out-of-profile traffic)
Type: AlgorithmicDropper
Discipline: RED
Trigger: Internal
Output: Fifo1
MinThresh: Fifo1.Depth > 10 kbyte
MaxThresh: Fifo1.Depth > 20 kbyte
SampleWeight .002
MaxDropProb 2%
Another form of Algorithmic Dropper, a threshold-dropper, might be
represented using the following parameters:
AlgorithmicDropper3:
Type: AlgorithmicDropper
Discipline: Drop-on-threshold
Trigger: Fifo2.Depth > 20 kbyte
Output: Fifo1
7.2. Sharing load among traffic streams using queuing
Queues are used, in Differentiated Services, for a number of
purposes. In essence, they are simply places to store traffic until
it is transmitted. However, when several queues are used together in
a queuing system, they can also achieve effects beyond that for given
traffic streams. They can be used to limit variation in delay or
impose a maximum rate (shaping), to permit several streams to share a
link in a semi-predictable fashion (load sharing), or to move
variation in delay from some streams to other streams.
Traffic shaping is often used to condition traffic, such that packets
arriving in a burst will be "smoothed" and deemed conforming by
subsequent downstream meters in this or other nodes. In [DSARCH] a
shaper is described as a queuing element controlled by a meter which
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defines its temporal profile. However, this representation of a
shaper differs substantially from typical shaper implementations.
In the model described here, a shaper is realized by using a non-
work-conserving Scheduler. Some implementations may elect to have
queues whose sole purpose is shaping, while others may integrate the
shaping function with other buffering, discarding, and scheduling
associated with access to a resource. Shapers operate by delaying
the departure of packets that would be deemed non-conforming by a
meter configured to the shaper's maximum service rate profile. The
packet is scheduled to depart no sooner than such time that it would
become conforming.
7.2.1. Load Sharing
Load sharing is the traditional use of queues and was theoretically
explored by Floyd & Jacobson [FJ95], although it has been in use in
communications systems since the 1970's.
[DSARCH] discusses load sharing as dividing an interface among
traffic classes predictably, or applying a minimum rate to each of a
set of traffic classes, which might be measured as an absolute lower
bound on the rate a traffic stream achieves or a fraction of the rate
an interface offers. It is generally implemented as some form of
weighted queuing algorithm among a set of FIFO queues i.e., a WFQ
scheme. This has interesting side-effects.
A key effect sought is to ensure that the mean rate the traffic in a
stream experiences is never lower than some threshold when there is
at least that much traffic to send. When there is less traffic than
this, the queue tends to be starved of traffic, meaning that the
queuing system will not delay its traffic by very much. When there
is significantly more traffic and the queue starts filling, packets
in this class will be delayed significantly more than traffic in
other classes that are under-using their available capacity. This
form of queuing system therefore tends to move delay and variation in
delay from under-used classes of traffic to heavier users, as well as
managing the rates of the traffic streams.
A side-effect of a WRR or WFQ implementation is that between any two
packets in a given traffic class, the scheduler may emit one or more
packets from each of the other classes in the queuing system. In
cases where average behavior is in view, this is perfectly
acceptable. In cases where traffic is very intolerant of jitter and
there are a number of competing classes, this may have undesirable
consequences.
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7.2.2. Traffic Priority
Traffic Prioritization is a special case of load sharing, wherein a
certain traffic class is deemed so jitter-intolerant that if it has
traffic present, that traffic must be sent at the earliest possible
time. By extension, several priorities might be defined, such that
traffic in each of several classes is given preferential service over
any traffic of a lower class. It is the obvious implementation of IP
Precedence as described in [RFC 791], of 802.1p traffic classes
[802.1D], and other similar technologies.
Priority is often abused in real networks; people tend to think that
traffic which has a high business priority deserves this treatment
and talk more about the business imperatives than the actual
application requirements. This can have severe consequences;
networks have been configured which placed business-critical traffic
at a higher priority than routing-protocol traffic, resulting in
collapse of the network's management or control systems. However, it
may have a legitimate use for services based on an Expedited
Forwarding (EF) PHB, where it is absolutely sure, thanks to policing
at all possible traffic entry points, that a traffic stream does not
abuse its rate and that the application is indeed jitter-intolerant
enough to merit this type of handling. Note that, even in cases with
well-policed ingress points, there is still the possibility of
unexpected traffic loops within an un-policed core part of the
network causing such collapse.
8. Traffic Conditioning Blocks (TCBs)
The Classifier, Meter, Action, Algorithmic Dropper, Queue and
Scheduler functional datapath elements described above can be
combined into Traffic Conditioning Blocks (TCBs). A TCB is an
abstraction of a set of functional datapath elements that may be used
to facilitate the definition of specific traffic conditioning
functionality (e.g., it might be likened to a template which can be
replicated multiple times for different traffic streams or different
customers). It has no likely physical representation in the
implementation of the data path: it is invented purely as an
abstraction for use by management tools.
This model describes the configuration and management of a Diffserv
interface in terms of a TCB that contains, by definition, zero or
more Classifier, Meter, Action, Algorithmic Dropper, Queue and
Scheduler elements. These elements are arranged arbitrarily
according to the policy being expressed, but always in the order
here. Traffic may be classified; classified traffic may be metered;
each stream of traffic identified by a combination of classifiers and
meters may have some set of actions performed on it, followed by drop
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algorithms; packets of the traffic stream may ultimately be stored
into a queue and then be scheduled out to the next TCB or physical
interface. It is permissible to omit elements or include null
elements of any type, or to concatenate multiple functional datapath
elements of the same type.
When the Diffserv treatment for a given packet needs to have such
building blocks repeated, this is performed by cascading multiple
TCBs: an output of one TCB may drive the input of a succeeding one.
For example, consider the case where traffic of a set of classes is
shaped to a set of rates, but the total output rate of the group of
classes must also be limited to a rate. One might imagine a set of
network news feeds, each with a certain maximum rate, and a policy
that their aggregate may not exceed some figure. This may be simply
accomplished by cascading two TCBs. The first classifies the traffic
into its separate feeds and queues each feed separately. The feeds
(or a subset of them) are now fed into a second TCB, which places all
input (these news feeds) into a single queue with a certain maximum
rate. In implementation, one could imagine this as the several
literal queues, a CBQ or WFQ system with an appropriate (and complex)
weighting scheme, or a number of other approaches. But they would
have the same externally measurable effect on the traffic as if they
had been literally implemented with separate TCBs.
8.1. TCB
A generalized TCB might consist of the following stages:
- Classification stage
- Metering stage
- Action stage (involving Markers, Absolute Droppers, Counters,
and Multiplexors)
- Queuing stage (involving Algorithmic Droppers, Queues, and
Schedulers)
where each stage may consist of a set of parallel datapaths
consisting of pipelined elements.
A Classifier or a Meter is typically a 1:N element, an Action,
Algorithmic Dropper, or Queue is typically a 1:1 element and a
Scheduler is a N:1 element. A complete TCB should, however, result
in a 1:1 or 1:N abstract element. Note that the fan-in or fan-out of
an element is not an important defining characteristic of this
taxonomy.
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8.1.1. Building blocks for Queuing
Some particular rules are applied to the ordering of elements within
a Queuing stage within a TCB: elements of the same type may appear
more than once, either in parallel or in series. Typically, a
queuing stage will have relatively many elements in parallel and few
in series. Iteration and recursion are not supported constructs (the
elements are arranged in an acyclic graph). The following inter-
connections of elements are allowed:
- The input of a Queue may be the input of the queuing block, or
it may be connected to the output of an Algorithmic Dropper, or
to an output of a Scheduler.
- Each input of a Scheduler may be connected to the output of a
Queue, to the output of an Algorithmic Dropper, or to the
output of another Scheduler.
- The input of an Algorithmic Dropper may be the first element of
the queuing stage, the output of another Algorithmic Dropper,
or it may be connected to the output of a Queue (to indicate
head-dropping).
- The output of the queuing block may be the output of a Queue,
an Algorithmic Dropper, or a Scheduler.
Note, in particular, that Schedulers may operate in series such so
that a packet at the head of a Queue feeding the concatenated
Schedulers is serviced only after all of the scheduling criteria are
met. For example, a Queue which carries EF traffic streams may be
served first by a non-work-conserving Scheduler to shape the stream
to a maximum rate, then by a work-conserving Scheduler to mix EF
traffic streams with other traffic streams. Alternatively, there
might be a Queue and/or a dropper between the two Schedulers.
Note also that some non-sensical scenarios (e.g., a Queue preceding
an Algorithmic Dropper, directly feeding into another Queue), are
prohibited.
8.2. An Example TCB
A SLS is presumed to have been negotiated between the customer and
the provider which specifies the handling of the customer's traffic,
as defined by a TCS) by the provider's network. The agreement might
be of the following form:
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DSCP PHB Profile Treatment
---- --- ------- ----------------------
001001 EF Profile4 Discard non-conforming.
001100 AF11 Profile5 Shape to profile, tail-drop when full.
001101 AF21 Profile3 Re-mark non-conforming to DSCP 001000,
tail-drop when full.
other BE none Apply RED-like dropping.
This SLS specifies that the customer may submit packets marked for
DSCP 001001 which will get EF treatment so long as they remain
conforming to Profile4, which will be discarded if they exceed this
profile. The discarded packets are counted in this example, perhaps
for use by the provider's sales department in convincing the customer
to buy a larger SLS. Packets marked for DSCP 001100 will be shaped
to Profile5 before forwarding. Packets marked for DSCP 001101 will
be metered to Profile3 with non-conforming packets "downgraded" by
being re-marked with a DSCP of 001000. It is implicit in this
agreement that conforming packets are given the PHB originally
indicated by the packets' DSCP field.
Figures 6 and 7 illustrates a TCB that might be used to handle this
SLS at an ingress interface at the customer/provider boundary.
The Classification stage of this example consists of a single BA
classifier. The BA classifier is used to separate traffic based on
the Diffserv service level requested by the customer (as indicated by
the DSCP in each submitted packet's IP header). We illustrate three
DSCP filter values: A, B, and C. The 'X' in the BA classifier is a
wildcard filter that matches every packet not otherwise matched.
The path for DSCP 001100 proceeds directly to Dropper1 whilst the
paths for DSCP 001001 and 001101 include a metering stage. All other
traffic is passed directly on to Dropper3. There is a separate meter
for each set of packets corresponding to classifier outputs A and C.
Each meter uses a specific profile, as specified in the TCS, for the
corresponding Diffserv service level. The meters in this example
each indicate one of two conformance levels: conforming or non-
conforming.
Following the Metering stage is an Action stage in some of the
branches. Packets submitted for DSCP 001001 (Classifier output A)
that are deemed non-conforming by Meter1 are counted and discarded
while packets that are conforming are passed on to Queue1. Packets
submitted for DSCP 001101 (Classifier output C) that are deemed non-
conforming by Meter2 are re-marked and then both conforming and non-
conforming packets are multiplexed together before being passed on to
Dropper2/Queue3.
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The Algorithmic Dropping, Queuing and Scheduling stages are realized
as follows, illustrated in figure 7. Note that the figure does not
show any of the implicit control linkages between elements that allow
e.g., an Algorithmic Dropper to sense the current state of a
succeeding Queue.
+-----+
| A|---------------------------> to Queue1
+->| |
| | B|--+ +-----+ +-----+
| +-----+ | | | | |
| Meter1 +->| |--->| |
| | | | |
| +-----+ +-----+
| Counter1 Absolute
submitted +-----+ | Dropper1
traffic | A|-----+
--------->| B|--------------------------------------> to AlgDropper1
| C|-----+
| X|--+ |
+-----+ | | +-----+ +-----+
Classifier1| | | A|--------------->|A |
(BA) | +->| | | |--> to AlgDrop2
| | B|--+ +-----+ +->|B |
| +-----+ | | | | +-----+
| Meter2 +->| |-+ Mux1
| | |
| +-----+
| Marker1
+-----------------------------------> to AlgDropper3
Figure 6: An Example Traffic Conditioning Block (Part 1)
Conforming DSCP 001001 packets from Meter1 are passed directly to
Queue1: there is no way, with configuration of the following
Scheduler to match the metering, for these packets to overflow the
depth of Queue1, so there is no requirement for dropping at this
point. Packets marked for DSCP 001100 must be passed through a
tail-dropper, AlgDropper1, which serves to limit the depth of the
following queue, Queue2: packets that arrive to a full queue will be
discarded. This is likely to be an error case: the customer is
obviously not sticking to its agreed profile. Similarly, all packets
from the original DSCP 001101 stream (some may have been re-marked by
this stage) are passed to AlgDropper2 and Queue3. Packets marked for
all other DSCPs are passed to AlgDropper3 which is a RED-like
Algorithmic Dropper: based on feedback of the current depth of
Queue4, this dropper is supposed to discard enough packets from its
input stream to keep the queue depth under control.
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These four Queue elements are then serviced by a Scheduler element
Scheduler1: this must be configured to give each of its inputs an
appropriate priority and/or bandwidth share. Inputs A and C are
given guarantees of bandwidth, as appropriate for the contracted
profiles. Input B is given a limit on the bandwidth it can use
(i.e., a non-work-conserving discipline) in order to achieve the
desired shaping of this stream. Input D is given no limits or
guarantees but a lower priority than the other queues, appropriate
for its best-effort status. Traffic then exits the Scheduler in a
single orderly stream.
The interconnections of the TCB elements illustrated in Figures 6 and
7 can be represented textually as follows:
TCB1:
Classifier1:
FilterA: Meter1
FilterB: Dropper1
FilterC: Meter2
Default: Dropper3
from Meter1 +-----+
------------------------------->| |----+
| | |
+-----+ |
Queue1 |
| +-----+
from Classifier1 +-----+ +-----+ +->|A |
---------------->| |------->| |------>|B |------->
| | | | +--->|C | exiting
+-----+ +-----+ | +->|D | traffic
AlgDropper1 Queue2 | | +-----+
| | Scheduler1
from Mux1 +-----+ +-----+ | |
---------------->| |------->| |--+ |
| | | | |
+-----+ +-----+ |
AlgDropper2 Queue3 |
|
from Classifier1 +-----+ +-----+ |
---------------->| |------->| |----+
| | | |
+-----+ +-----+
AlgDropper3 Queue4
Figure 7: An Example Traffic Conditioning Block (Part 2)
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Meter1:
Type: AverageRate
Profile: Profile4
ConformingOutput: Queue1
NonConformingOutput: Counter1
Counter1:
Output: AbsoluteDropper1
Meter2:
Type: AverageRate
Profile: Profile3
ConformingOutput: Mux1.InputA
NonConformingOutput: Marker1
Marker1:
Type: DSCPMarker
Mark: 001000
Output: Mux1.InputB
Mux1:
Output: Dropper2
AlgDropper1:
Type: AlgorithmicDropper
Discipline: Drop-on-threshold
Trigger: Queue2.Depth > 10kbyte
Output: Queue2
AlgDropper2:
Type: AlgorithmicDropper
Discipline: Drop-on-threshold
Trigger: Queue3.Depth > 20kbyte
Output: Queue3
AlgDropper3:
Type: AlgorithmicDropper
Discipline: RED93
Trigger: Internal
Output: Queue3
MinThresh: Queue3.Depth > 20 kbyte
MaxThresh: Queue3.Depth > 40 kbyte
<other RED parms too>
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Queue1:
Type: FIFO
Output: Scheduler1.InputA
Queue2:
Type: FIFO
Output: Scheduler1.InputB
Queue3:
Type: FIFO
Output: Scheduler1.InputC
Queue4:
Type: FIFO
Output: Scheduler1.InputD
Scheduler1:
Type: Scheduler4Input
InputA:
MaxRateProfile: none
MinRateProfile: Profile4
Priority: 20
InputB:
MaxRateProfile: Profile5
MinRateProfile: none
Priority: 40
InputC:
MaxRateProfile: none
MinRateProfile: Profile3
Priority: 20
InputD:
MaxRateProfile: none
MinRateProfile: none
Priority: 10
8.3. An Example TCB to Support Multiple Customers
The TCB described above can be installed on an ingress interface to
implement a provider/customer TCS if the interface is dedicated to
the customer. However, if a single interface is shared between
multiple customers, then the TCB above will not suffice, since it
does not differentiate among traffic from different customers. Its
classification stage uses only BA classifiers.
The configuration is readily modified to support the case of multiple
customers per interface, as follows. First, a TCB is defined for
each customer to reflect the TCS with that customer: TCB1, defined
above is the TCB for customer 1. Similar elements are created for
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TCB2 and for TCB3 which reflect the agreements with customers 2 and 3
respectively. These 3 TCBs may or may not contain similar elements
and parameters.
Finally, a classifier is added to the front end to separate the
traffic from the three different customers. This forms a new TCB,
TCB4, which is illustrated in Figure 8.
A representation of this multi-customer TCB might be:
TCB4:
Classifier4:
Filter1: to TCB1
Filter2: to TCB2
Filter3: to TCB3
No Match: AbsoluteDropper4
AbsoluteDropper4:
Type: AbsoluteDropper
TCB1:
(as defined above)
TCB2:
(similar to TCB1, perhaps with different
elements or numeric parameters)
TCB3:
(similar to TCB1, perhaps with different
elements or numeric parameters)
and the filters, based on each customer's source MAC address, could
be defined as follows:
Filter1:
submitted +-----+
traffic | A|--------> TCB1
--------->| B|--------> TCB2
| C|--------> TCB3
| X|------+ +-----+
+-----+ +-->| |
Classifier4 +-----+
AbsoluteDrop4
Figure 8: An Example of a Multi-Customer TCB
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Type: MacAddress
SrcValue: 01-02-03-04-05-06 (source MAC address of customer 1)
SrcMask: FF-FF-FF-FF-FF-FF
DestValue: 00-00-00-00-00-00
DestMask: 00-00-00-00-00-00
Filter2:
(similar to Filter1 but with customer 2's source MAC address as
SrcValue)
Filter3:
(similar to Filter1 but with customer 3's source MAC address as
SrcValue)
In this example, Classifier4 separates traffic submitted from
different customers based on the source MAC address in submitted
packets. Those packets with recognized source MAC addresses are
passed to the TCB implementing the TCS with the corresponding
customer. Those packets with unrecognized source MAC addresses are
passed to a dropper.
TCB4 has a Classifier stage and an Action element stage performing
dropping of all unmatched traffic.
8.4. TCBs Supporting Microflow-based Services
The TCB illustrated above describes a configuration that might be
suitable for enforcing a SLS at a router's ingress. It assumes that
the customer marks its own traffic for the appropriate service level.
It then limits the rate of aggregate traffic submitted at each
service level, thereby protecting the resources of the Diffserv
network. It does not provide any isolation between the customer's
individual microflows.
A more complex example might be a TCB configuration that offers
additional functionality to the customer. It recognizes individual
customer microflows and marks each one independently. It also
isolates the customer's individual microflows from each other in
order to prevent a single microflow from seizing an unfair share of
the resources available to the customer at a certain service level.
This is illustrated in Figure 9.
Suppose that the customer has an SLS which specifies 2 service
levels, to be identified to the provider by DSCP A and DSCP B.
Traffic is first directed to a MF classifier which classifies traffic
based on miscellaneous classification criteria, to a granularity
sufficient to identify individual customer microflows. Each
microflow can then be marked for a specific DSCP The metering
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elements limit the contribution of each of the customer's microflows
to the service level for which it was marked. Packets exceeding the
allowable limit for the microflow are dropped.
+-----+ +-----+
Classifier1 | | | |---------------+
(MF) +->| |-->| | +-----+ |
+-----+ | | | | |---->| | |
| A|------ +-----+ +-----+ +-----+ |
-->| B|-----+ Marker1 Meter1 Absolute |
| C|---+ | Dropper1 | +-----+
| X|-+ | | +-----+ +-----+ +-->|A |
+-----+ | | | | | | |------------------>|B |--->
| | +->| |-->| | +-----+ +-->|C | to TCB2
| | | | | |---->| | | +-----+
| | +-----+ +-----+ +-----+ | Mux1
| | Marker2 Meter2 Absolute |
| | Dropper2 |
| | +-----+ +-----+ |
| | | | | |---------------+
| |--->| |-->| | +-----+
| | | | |---->| |
| +-----+ +-----+ +-----+
| Marker3 Meter3 Absolute
| Dropper3
V etc.
Figure 9: An Example of a Marking and Traffic Isolation TCB
This TCB could be formally specified as follows:
TCB1:
Classifier1: (MF)
FilterA: Marker1
FilterB: Marker2
FilterC: Marker3
etc.
Marker1:
Output: Meter1
Marker2:
Output: Meter2
Marker3:
Output: Meter3
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Meter1:
ConformingOutput: Mux1.InputA
NonConformingOutput: AbsoluteDropper1
Meter2:
ConformingOutput: Mux1.InputB
NonConformingOutput: AbsoluteDropper2
Meter3:
ConformingOutput: Mux1.InputC
NonConformingOutput: AbsoluteDropper3
etc.
Mux1:
Output: to TCB2
Note that the detailed traffic element declarations are not shown
here. Traffic is either dropped by TCB1 or emerges marked for one of
two DSCPs. This traffic is then passed to TCB2 which is illustrated
in Figure 10.
TCB2 could then be specified as follows:
Classifier2: (BA)
FilterA: Meter5
FilterB: Meter6
+-----+
| |---------------> to Queue1
+->| | +-----+
+-----+ | | |---->| |
| A|---+ +-----+ +-----+
->| | Meter5 AbsoluteDropper4
| B|---+ +-----+
+-----+ | | |---------------> to Queue2
Classifier2 +->| | +-----+
(BA) | |---->| |
+-----+ +-----+
Meter6 AbsoluteDropper5
Figure 10: Additional Example: TCB2
Meter5:
ConformingOutput: Queue1
NonConformingOutput: AbsoluteDropper4
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Meter6:
ConformingOutput: Queue2
NonConformingOutput: AbsoluteDropper5
8.5. Cascaded TCBs
Nothing in this model prevents more complex scenarios in which one
microflow TCB precedes another (e.g., for TCBs implementing separate
TCSs for the source and for a set of destinations).
9. Security Considerations
Security vulnerabilities of Diffserv network operation are discussed
in [DSARCH]. This document describes an abstract functional model of
Diffserv router elements. Certain denial-of-service attacks such as
those resulting from resource starvation may be mitigated by
appropriate configuration of these router elements; for example, by
rate limiting certain traffic streams or by authenticating traffic
marked for higher quality-of-service.
There may be theft-of-service scenarios where a malicious host can
exploit a loose token bucket policer to obtain slightly better QoS
than that committed in the TCS.
10. Acknowledgments
Concepts, terminology, and text have been borrowed liberally from
[POLTERM], as well as from other IETF work on MIBs and policy-
management. We wish to thank the authors of some of those documents:
Fred Baker, Michael Fine, Keith McCloghrie, John Seligson, Kwok Chan,
Scott Hahn, and Andrea Westerinen for their contributions.
This document has benefited from the comments and suggestions of
several participants of the Diffserv working group, particularly
Shahram Davari, John Strassner, and Walter Weiss. This document
could never have reached this level of rough consensus without the
relentless pressure of the co-chairs Brian Carpenter and Kathie
Nichols, for which the authors are grateful.
11. References
[AF-PHB] Heinanen, J., Baker, F., Weiss, W. and J. Wroclawski,
"Assured Forwarding PHB Group", RFC 2597, June 1999.
[DSARCH] Carlson, M., Weiss, W., Blake, S., Wang, Z., Black, D.
and E. Davies, "An Architecture for Differentiated
Services", RFC 2475, December 1998.
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[DSFIELD] Nichols, K., Blake, S., Baker, F. and D. Black,
"Definition of the Differentiated Services Field (DS
Field) in the IPv4 and IPv6 Headers", RFC 2474, December
1998.
[DSMIB] Baker, F., Smith, A., and K. Chan, "Management
Information Base for the Differentiated Services
Architecture", RFC 3289, May 2002.
[E2E] Bernet, Y., Yavatkar, R., Ford, P., Baker, F., Zhang, L.,
Speer, M., Nichols, K., Braden, R., Davie, B.,
Wroclawski, J. and E. Felstaine, "A Framework for
Integrated Services Operation over Diffserv Networks",
RFC 2998, November 2000.
[EF-PHB] Davie, B., Charny, A., Bennett, J.C.R., Benson, K., Le
Boudec, J.Y., Courtney, W., Davari, S., Firoiu, V. and D.
Stiliadis, "An Expedited Forwarding PHB (Per-Hop
Behavior)", RFC 3246, March 2002.
[FJ95] Floyd, S. and V. Jacobson, "Link Sharing and Resource
Management Models for Packet Networks", IEEE/ACM
Transactions on Networking, Vol. 3 No. 4, August 1995l.
[INTSERV] Braden, R., Clark, D. and S. Shenker, "Integrated
Services in the Internet Architecture: an Overview", RFC
1633, June 1994.
[NEWTERMS] Grossman, D., "New Terminology and Clarifications for
Diffserv", RFC 3260, April, 2002
[PDBDEF] K. Nichols and B. Carpenter, "Definition of
Differentiated Services Per Domain Behaviors and Rules
for Their Specification", RFC 3086, April 2001.
[POLTERM] Westerinen, A., Schnizlein, J., Strassner, J., Scherling,
M., Quinn, B., Herzog, S., Huynh, A., Carlson, M., Perry,
J. and S. Waldbusser, "Policy Terminology", RFC 3198,
November 2001.
[QOSDEVMOD] Strassner, J., Westerinen, A. and B. Moore, "Information
Model for Describing Network Device QoS Mechanisms", Work
in Progress.
Bernet, et. al. Informational [Page 48]
RFC 3290 Diffserv Informal Management Model May 2002
[QUEUEMGMT] Braden, R., Clark, D., Crowcroft, J., Davie, B., Deering,
S., Estrin, D., Floyd, S., Jacobson, V., Minshall, C.,
Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
S., Wroclawski, J. and L. Zhang, "Recommendations on
Queue Management and Congestion Avoidance in the
Internet", RFC 2309, April 1998.
[SRTCM] Heinanen, J. and R. Guerin, "A Single Rate Three Color
Marker", RFC 2697, September 1999.
[TRTCM] Heinanen, J. and R. Guerin, "A Two Rate Three Color
Marker", RFC 2698, September 1999.
[VIC] McCanne, S. and Jacobson, V., "vic: A Flexible Framework
for Packet Video", ACM Multimedia '95, November 1995, San
Francisco, CA, pp. 511-522.
<ftp://ftp.ee.lbl.gov/papers/vic-mm95.ps.Z>
[802.1D] "Information technology - Telecommunications and
information exchange between systems - Local and
metropolitan area networks - Common specifications - Part
3: Media Access Control (MAC) Bridges: Revision. This
is a revision of ISO/IEC 10038: 1993, 802.1j-1992 and
802.6k-1992. It incorporates P802.11c, P802.1p and
P802.12e.", ISO/IEC 15802-3: 1998.
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Appendix A. Discussion of Token Buckets and Leaky Buckets
"Leaky bucket" and/or "Token Bucket" models are used to describe rate
control in several architectures, including Frame Relay, ATM,
Integrated Services and Differentiated Services. Both of these
models are, by definition, theoretical relationships between some
defined burst size, B, a rate, R, and a time interval, t:
R = B/t
Thus, a token bucket or leaky bucket might specify an information
rate of 1.2 Mbps with a burst size of 1500 bytes. In this case, the
token rate is 1,200,000 bits per second, the token burst is 12,000
bits and the token interval is 10 milliseconds. The specification
says that conforming traffic will, in the worst case, come in 100
bursts per second of 1500 bytes each and at an average rate not
exceeding 1.2 Mbps.
A.1 Leaky Buckets
A leaky bucket algorithm is primarily used for shaping traffic as it
leaves an interface onto the network (handled under Queues and
Schedulers in this model). Traffic theoretically departs from an
interface at a rate of one bit every so many time units (in the
example, one bit every 0.83 microseconds) but, in fact, departs in
multi-bit units (packets) at a rate approximating the theoretical, as
measured over a longer interval. In the example, it might send one
1500 byte packet every 10 ms or perhaps one 500 byte packet every 3.3
ms. It is also possible to build multi-rate leaky buckets in which
traffic departs from the interface at varying rates depending on
recent activity or inactivity.
Implementations generally seek as constant a transmission rate as
achievable. In theory, a 10 Mbps shaped transmission stream from an
algorithmic implementation and a stream which is running at 10 Mbps
because its bottleneck link has been a 10 Mbps Ethernet link should
be indistinguishable. Depending on configuration, the approximation
to theoretical smoothness may vary by moving as much as an MTU from
one token interval to another. Traffic may also be jostled by other
traffic competing for the same transmission resources.
A.2 Token Buckets
A token bucket, on the other hand, measures the arrival rate of
traffic from another device. This traffic may originally have been
shaped using a leaky bucket shaper or its equivalent. The token
bucket determines whether the traffic (still) conforms to the
specification. Multi-rate token buckets (e.g., token buckets with
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both a peak rate and a mean rate, and sometimes more) are commonly
used, such as those described in [SRTCM] and [TRTCM]. In this case,
absolute smoothness is not expected, but conformance to one or more
of the specified rates is.
Simplistically, a data stream is said to conform to a simple token
bucket parameterized by a {R, B} if the system receives in any time
interval, t, at most, an amount of data not exceeding (R * t) + B.
For a multi-rate token bucket case, the data stream is said to
conform if, for each of the rates, the stream conforms to the token-
bucket profile appropriate for traffic of that class. For example,
received traffic that arrives pre-classified as one of the "excess"
rates (e.g., AF12 or AF13 traffic for a device implementing the AF1x
PHB) is only compared to the relevant "excess" token bucket profile.
A.3 Some Consequences
The fact that Internet Protocol data is organized into variable
length packets introduces some uncertainty in the conformance
decision made by any downstream Meter that is attempting to determine
conformance to a traffic profile that is theoretically designed for
fixed-length units of data.
When used as a leaky bucket shaper, the above definition interacts
with clock granularity in ways one might not expect. A leaky bucket
releases a packet only when all of its bits would have been allowed:
it does not borrow from future capacity. If the clock is very fine
grain, on the order of the bit rate or faster, this is not an issue.
But if the clock is relatively slow (and millisecond or multi-
millisecond clocks are not unusual in networking equipment), this can
introduce jitter to the shaped stream.
This leaves an implementor of a token bucket Meter with a dilemma.
When the number of bandwidth tokens, b, left in the token bucket is
positive but less than the size of the packet being operated on, L,
one of three actions can be performed:
(1) The whole size of the packet can be subtracted from the
bucket, leaving it negative, remembering that, when new
tokens are next added to the bucket, the new token
allocation, B, must be added to b rather than simply setting
the bucket to "full". This option potentially puts more
than the desired burst size of data into this token bucket
interval and correspondingly less into the next. It does,
however, keep the average amount accepted per token bucket
interval equal to the token burst. This approach accepts
traffic if any one bit in the packet would have been
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accepted and borrows up to one MTU of capacity from one or
more subsequent intervals when necessary. Such a token
bucket meter implementation is said to offer "loose"
conformance to the token bucket.
(2) Alternatively, the packet can be rejected and the amount of
tokens in the bucket left unchanged (and maybe an attempt
could be made to accept the packet under another threshold
in another bucket), remembering that, when new tokens are
next added to the bucket, the new token allocation, B, must
be added to b rather than simply setting the bucket to
"full". This potentially puts less than the permissible
burst size of data into this token bucket interval and
correspondingly more into the next. Like the first option,
it keeps the average amount accepted per token bucket
interval equal to the token burst. This approach accepts
traffic only if every bit in the packet would have been
accepted and borrows up to one MTU of capacity from one or
more previous intervals when necessary. Such a token bucket
meter implementation is said to offer "strict" (or perhaps
"stricter") conformance to the token bucket. This option is
consistent with [SRTCM] and [TRTCM] and is often used in ATM
and frame-relay implementations.
(3) The TB variable can be set to zero to account for the first
part of the packet and the remainder of the packet size can
be taken out of the next-colored bucket. This, of course,
has another bug: the same packet cannot have both
conforming and non-conforming components in the Diffserv
architecture and so is not really appropriate here and we do
not discuss this option further here.
Unfortunately, the thing that cannot be done is exactly to
fit the token burst specification with random sized packets:
therefore token buckets in a variable length packet
environment always have a some variance from theoretical
reality. This has also been observed in the ATM Guaranteed
Frame Rate (GFR) service category specification and Frame
Relay. A number of observations may be made:
o Operationally, a token bucket meter is reasonable for traffic
which has been shaped by a leaky bucket shaper or a serial line.
However, traffic in the Internet is rarely shaped in that way: TCP
applies no shaping to its traffic, but rather depends on longer-
range ACK-clocking behavior to help it approximate a certain rate
and explicitly sends traffic bursts during slow start,
retransmission, and fast recovery. Video-on-IP implementations
such as [VIC] may have a leaky bucket shaper available to them,
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but often do not, and simply enqueue the output of their codec for
transmission on the appropriate interface. As a result, in each
of these cases, a token bucket meter may reject traffic in the
short term (over a single token interval) which it would have
accepted if it had a longer time in view and which it needs to
accept for the application to work properly. To work around this,
the token interval, B/R, must approximate or exceed the RTT of the
session(s) in question and the burst size, B, must accommodate the
largest burst that the originator might send.
o The behavior of a loose token bucket is significantly different
from the token bucket description for ATM and for Frame Relay.
o A loose token bucket does not accept packets while the token count
is negative. This means that, when a large packet has just
borrowed tokens from the future, even a small incoming packet
(e.g., a 40-byte TCP ACK/SYN) will not be accepted. Therefore, if
such a loose token bucket is configured with a burst size close to
the MTU, some discrimination against smaller packets can take
place: use of a larger burst size avoids this problem.
o The converse of the above is that a strict token bucket sometimes
does not accept large packets when a loose one would do so.
Therefore, if such a strict token bucket is configured with a
burst size close to the MTU, some discrimination against larger
packets can take place: use of a larger burst size avoids this
problem.
o In real-world deployments, MTUs are often larger than the burst
size offered by a link-layer network service provider. If so then
it is possible that a strict token bucket meter would find that
traffic never matches the specified profile: this may be avoided
by not allowing such a specification to be used. This situation
cannot arise with a loose token bucket since the smallest burst
size that can be configured is 1 bit, by definition limiting a
loose token bucket to having a burst size of greater than one MTU.
o Both strict token bucket specifications, as specified in [SRTCM]
and [TRTCM], and loose ones, are subject to a persistent under-
run. These accumulate burst capacity over time, up to the maximum
burst size. Suppose that the maximum burst size is exactly the
size of the packets being sent - which one might call the
"strictest" token bucket implementation. In such a case, when one
packet has been accepted, the token depth becomes zero and starts
to accumulate again. If the next packet is received any time
earlier than a token interval later, it will not be accepted. If
the next packet arrives exactly on time, it will be accepted and
the token depth again set to zero. If it arrives later, however,
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accumulation of tokens will have stopped because it is capped by
the maximum burst size: during the interval between the bucket
becoming full and the actual arrival of the packet, no new tokens
are added. As a result, jitter that accumulates across multiple
hops in the network conspires against the algorithm to reduce the
actual acceptance rate. Thus it usually makes sense to set the
maximum token bucket size somewhat greater than the MTU in order
to absorb some of the jitter and allow a practical acceptance rate
more in line with the desired theoretical rate.
A.4 Mathematical Definition of Strict Token Bucket Conformance
The strict token bucket conformance behavior defined in [SRTCM] and
[TRTCM] is not mandatory for compliance with any current Diffserv
standards, but we give here a mathematical definition of two-
parameter token bucket operation which is consistent with those
documents and which can also be used to define a shaping profile.
Define a token bucket with bucket size B, token accumulation rate R
and instantaneous token occupancy b(t). Assume that b(0) = B. Then
after an arbitrary interval with no packet arrivals, b(t) will not
change since the bucket is already full of tokens.
Assume a packet of size L bytes arrives at time t'. The bucket
occupancy is still B. Then, as long as L <= B, the packet conforms
to the meter, and afterwards
b(t') = B - L.
Assume now an interval delta_t = t - t' elapses before the next
packet arrives, of size L' <= B. Just before this, at time t-, the
bucket has accumulated delta_t*R tokens over the interval, up to a
maximum of B tokens so that:
b(t-) = min{ B, b(t') + delta_t*R }
For a strict token bucket, the conformance test is as follows:
if (b(t-) - L' >= 0) {
/* the packet conforms */
b(t) = b(t-) - L';
}
else {
/* the packet does not conform */
b(t) = b(t-);
}
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This function can also be used to define a shaping profile. If a
packet of size L arrives at time t, it will be eligible for
transmission at time te given as follows (we still assume L <= B):
te = max{ t, t" }
where t" = (L - b(t') + t'*R) / R and b(t") = L, the time when L
credits have accumulated in the bucket, and when the packet would
conform if the token bucket were a meter. te != t" only if t > t".
A mathematical definition along these lines for loose token bucket
conformance is left as an exercise for the reader.
Authors' Addresses
Yoram Bernet
Microsoft
One Microsoft Way
Redmond, WA 98052
Phone: +1 425 936 9568
EMail: ybernet@msn.com
Steven Blake
Ericsson
920 Main Campus Drive, Suite 500
Raleigh, NC 27606
Phone: +1 919 472 9913
EMail: steven.blake@ericsson.com
Daniel Grossman
Motorola Inc.
20 Cabot Blvd.
Mansfield, MA 02048
Phone: +1 508 261 5312
EMail: dan@dma.isg.mot.com
Andrew Smith (editor)
Harbour Networks
Jiuling Building
21 North Xisanhuan Ave.
Beijing, 100089
PRC
Fax: +1 415 345 1827
EMail: ah_smith@acm.org
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Full Copyright Statement
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Acknowledgement
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