Internet Draft L. Yang
Expiration: May 2003 Intel Labs
File: draft-yang-forces-model-01.txt J. Halpern
Working Group: ForCES
R. Gopal
Nokia
R. Dantu
Univ. of Texas
Nov 2002
ForCES Forwarding Element Functional Model
draft-yang-forces-model-01.txt
Status of this Memo
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Abstract
This document defines a functional model for forwarding elements
(FEs) used in the Forwarding and Control Plane Separation (ForCES)
protocol. This model is used to describe the capabilities and state
of ForCES forwarding elements within the context of the ForCES
protocol, so that ForCES control elements (CEs) can control the FEs
accordingly. The model is to specify what logical functions are
present in the FEs, what capabilities these functions support, and
in what order these functions are or can be performed. The
forwarding element model defined herein is intended to satisfy the
requirements specified in the ForCES requirements draft [FORCES-
REQ]. Using this model, predefined or vendor specific logical
Internet Draft ForCES FE Functional Model Nov 2002
functions can be expressed and configured. However, the definition
of these individual functions are not described and defined in this
document.
Table of Contents
Abstract...........................................................1
1. Definitions.....................................................2
2. Motivation and Requirements of FE model.........................3
3. Capability Model versus State Model.............................3
4. FE Model........................................................6
4.1. FE Blocks..................................................7
4.2. FE Block Library...........................................7
4.2.1. QoS Functions.........................................8
4.2.2. Generic Filtering Functions..........................10
4.2.3. Vendor Specific Functions............................10
4.2.4. Port Functions.......................................10
4.2.5. Forwarding Functions.................................11
4.2.6. High-Touch Functions.................................12
4.2.7. Security Functions...................................12
4.2.8. Off-loaded Functions.................................12
4.3. FE Stage and Directed Graph of FE.........................13
4.3.1. Basic Concepts.......................................13
4.3.2. Topological versus Encoded State Approaches..........13
4.3.3. Cascading FE Blocks..................................16
5. Data Modeling and Representation...............................16
6. Security Considerations........................................17
7. Intellectual Property Right....................................17
8. IANA consideration.............................................18
9. Normative References...........................................18
10. Informative References........................................18
11. Acknowledgments...............................................18
12. Authors' Addresses............................................19
Conventions used in this document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in
this document are to be interpreted as described in [RFC-2119].
1. Definitions
A set of terminology associated with the ForCES requirements is
defined in [FORCES-REQ] and is not copied here. The following list
of terminology is relevant to the FE model defined in this document.
Datapath -- A conceptual path taken by packets within the forwarding
plane, inside an FE. There might exist more than one datapath within
an FE.
Forwarding Element (FE) Block -- An abstraction of the basic packet
processing logical functions in the datapath. It is the building
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block of FE functionality. This concept abstracts away
implementation details from the parameters of interest for
configuration, control and management by CE.
Forwarding Element (FE) Stage -- Representation of an FE block
instance in a FE's datapath. As a packet flows through an FE along a
datapath, it flows through one or multiple distinct stages, with
each stage implementing an instance of a certain logical function
block. There may be multiple instances of the same functional block
in a FE's datapath.
2. Motivation and Requirements of FE model
The ForCES architecture allows Forwarding Elements (FEs) of varying
functionality to participate in a ForCES network element (NE). The
implication of this varying functionality is that CEs can make only
minimal assumptions about the functionality provided by its FEs.
Before CEs can configure and control the forwarding behavior of FEs,
CEs need to query and discover the capabilities and states of their
FEs. [FORCES-REQ] mandates that this capabilities and states
information be expressed in the form of an FE model, and this model
will be used as the basis for CEs to control and manipulate FEs'
behavior via ForCES protocol.
[FORCES-REQ] describes all the requirements placed on the FE model
in detail. We provide a brief summary here to highlight some of the
design issues we face.
. The FE model MUST express what logical functions can be applied
to packets as they pass through an FE.
. The FE model MUST be capable of supporting/allowing variations
in the way logical functions are implemented on an FE.
. The model MUST be capable of describing the order in which
these logical functions are applied in a FE.
. The FE model SHOULD be extendable and should have provision to
express new or vendor specific logical functions.
. The FE model SHOULD be able to support minimal set of logical
functions that are already identified, such as port functions,
forwarding functions, QoS functions, filtering functions, high-
touch functions, security functions, vendor-specific functions
and off-loaded functions.
3. Capability Model versus State Model
Since the motivation of an FE model is to allow the CEs later to
control and configure the FEs' behavior via ForCES protocol, it
becomes essential to examine and understand what kind of control and
configuration the CEs might do to the FEs. It is also equally
essential to understand how configurable or programmable FEs are
today and will be in the near future. To understand the issue
better, it is helpful to make a distinction between two different
kinds of FE models û the FE state model and FE capability model.
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The FE state model describes the current state of the FE, that is,
the instantaneous values or operational behavior of the FE. The FE
state model presents the snapshot view of the FE to the CE. On the
other hand, the FE capability model describes the configurable
capabilities of an FE in terms of variations of functions supported
or limitations contained. Conceptually FE capability model presents
the many possible states allowed on an FE. The information on the
capabilities of the FE helps the CE to make more intelligent
decision on the configuration it wants to send to the FE. So the
configuration is the desirable state that the FE should be in.
Figure 1 shows the concepts of FE state, capabilities and
configuration in the context of CE-FE communication via ForCES
protocol.
+---------+ +---------+
| | FE state: what it is now. | |
| |<------------------------------------| |
| | | |
| CE | FE capabilities: what it can be. | FE |
| |<------------------------------------| |
| | | |
| | FE configuration: what it should be.| |
| |------------------------------------>| |
+---------+ +---------+
Figure 1. Illustration of FE state, capabilities and configuration
in the context of CE-FE communication via ForCES.
For example, using the FE state model, an FE may be described to its
CE as the following:
- on a given port the packets are classified using a given
classification filter;
- the given classifier results in packets being metered in a certain
way, and then marked in a certain way;
- the packets coming from specific markers are delivered into a
shared queue for handling, while other packets are delivered to a
different queue;
- a specific scheduler with specific behavior and parameters will
service these collected queues.
On the other hand, the capability model may describe the FE at the
coarsest level such as:
- this FE can handle IPv4 and IPv6 forwarding;
- this FE can perform classification on the following fields: source
IP address, destination IP address, source port number, destination
port number, etc;
- this FE can perform metering;
- this FE can handle up to N queues;
- this FE can add and remove encapsulating headers of types
including IPSec, GRE, L2TP.
Where it gets more complicated is for the capability model to cope
with the detailed limits, issues such as how many classifiers the FE
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can handle, how many queues, and how many buffer pools the FE can
support, how many meters the FE can provide. There is also the issue
as how flexibly these various functions can be interconnected within
the FE, in another word, how programmable the FE really can be and
how the FE capability model can reflect that.
While one could try to build an object model for representing
capabilities in full, other efforts have found this to be a
significant undertaking. A middle of the road approach is to define
coarse-grained capabilities and simple capacity measures. Then, if
the CE attempts to instruct the FE to set up some specific behavior
it is not capable of, the FE will return an error indicating the
problem.
It is clear that in the context of ForCES, a state model is
definitely necessary. The question is how much of the capability
model is needed in addition to the state model. A simple state model
without any capability flavor will severely limit ForCESÆs ability
to take advantage of the flexibility offered by programmable FEs. On
the other hand, an all too powerful capability model is difficult to
develop and may impose unnecessary overhead for most of the FEs that
only offer static functionalities.
In order to strike a good balance, it is necessary to examine the
kinds of control and configuration that the CEs may do to the FEs.
The first kind of control and configuration is the simplest of all.
It assumes that the logical functions that an FE supports are
already given and the interconnection of these functions remains
static in its lifetime. Therefore, the CE can only control FEÆs
behavior by manipulating the parameters for each individual
function, but it cannot change either the datapath or the functions
along each datapath. We call this "static FE" control and
configuration. For example, Figure 4 and 6 each show an FE
configuration example by representing the processing steps in a
directed graph interconnecting all the functional stages that
packets can possibly traverse. If such a configuration remains
static during FE's lifetime, then all CE can control is the
parameters associated with each stage in the graph, for example, the
routing table in the LPM forwarder in Figure 4, or the token bucket
parameters associated with meter1 in Figure 6. However, the CE
cannot reconfigure the graph topology dynamically, such as adding
another meter or queue onto the FE in Figure 6 on the fly. For this
kind of static control and configuration purpose, the useful FE
model should describe how the graph is connected and what are the
ôdials and knobsö (i.e., the parameters or attributes) each function
allows CE to manipulate. It should also include the statistics and
events that FEs can collect and report to CEs. Even for such a
ôstatic FEö, some capability model at the individual functions level
may be desirable to convey the flexibility of each function.
However, a lot of other information may not be necessary, like the
packet formats supported between meter1 and counter1 in Figure 6 as
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an example, because such information is only useful when the graph
can be re-configured dynamically on the fly.
The second kind of control and configuration builds on top of the
first kind. Using Figure 6 as an example, instead of presenting the
static FE graph to the CE, the FE can convey its capabilities to the
CE by telling "this FE can support one classifier with up to N
filters. This FE can also support up to M meters, X queues, etc." We
call this dynamic FE control and configuration. For such dynamic
control and configuration, a more powerful and flexible FE
capability model is required. For example, it becomes necessary to
model not only the capability of the building blocks like
classifiers, filters, meters etc., but also the linkage flexibility
and constraints between the blocks, so that CE can have the
intelligence to build a dynamic FE graph that makes sense.
The third level of control and configuration is even more powerful
and future looking. In addition to dynamic configuration, CEs might
even be allowed to download a given functionality onto FEs at run
time. This is similar to the active network concept and so we call
it active FE control and configuration. Like active network, this is
still considered a research area and is not being considered here.
The FE model proposed in this document intends to fully support the
static FE control and configuration at the minimum. It is also our
intention to allow dynamic FE control and configuration to a certain
degree when it makes sense. This FE model currently makes no attempt
to address issues beyond the first two kinds of control and
configuration scenarios.
4. FE Model
This section proposes a ForCES FE model to satisfy all the
requirements in [FORCES-REQ] for FE control and configuration. The
approach taken is to model the FE datapath(s) and its packet
treatment behavior via a directional graph where each node in the
graph is an instance of a well-defined logical function block.
The FE model defines a generic FE block akin to an abstract base
class in object-oriented terminology. The generic FE block contains
basic information like block type and textual description of the
block function. Based on this generic FE block, a set of well-known
FE logical functions are defined with additional state and
capability information pertinent to each specific function. A name
space is used to associate a unique name or ID with each type of FE
block. New logical functions can also be added later to accommodate
future innovation in the forwarding plane, as long as the new
functions are modeled as an FE block. With such a set of basic
building blocks defined, any FE can be modeled by a directional
graph where each node is an instance of an FE block, representing a
processing stage in the packet datapath. Each node contains
information like block name or ID (indicating the block type), stage
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ID (local to FE), number of downstream blocks and a list of the
stage IDs of those downstream blocks.
The rest of this section is devoted to describe the informal data
model of FE. The description here is intended to be abstract and
conceptual, and examples are used for illustration purpose only.
Separate document(s) will serve as specifications by using a formal
data modeling language and those specifications should be consistent
with the conceptual model described here.
4.1. FE Blocks
The generic FE block is the basic building block of the FE model,
like an abstract base class in object-oriented terminology. Actual
FE logical functions like classifiers, IPv4 forwarders and meters
are examples of real FE blocks derived from the generic FE block
concept.
A well-defined block has a well-defined packet processing behavior
and a well-defined set of state and capabilities that CE can
potentially configure or control via ForCES. A namespace is needed
to specify different types of blocks. The namespace assigns either a
unique ID or label to each distinct block type. Such a namespace
must be extensible so that new functions can be easily added later.
Therefore, the following defines a generic FE Block:
- block ID or label which uniquely identifies the block type;
- textual description of block function.
4.2. FE Block Library
We expect a small set of well-understood FE functional blocks to be
defined initially. Such a set of blocks can be viewed as a FE block
library. The minimum set of FE functions required in [FORCES-REQ]
must be part of this library. It is expected that new FE blocks
would be defined and added into this library over time.
The actual model for each functional block may differ and contains
information pertinent to the semantics of the function itself.
However, some general guideline is still useful. For example,
typically it is important to specify information such as:
- how many inputs it takes and what kinds of packets and meta data
it takes for each input;
- how many outputs it produces and what kind of packets and meta
data it emits for each output;
- the packet processing (such as modification) behavior;
- what information is programmed into it (e.g., LPM list, next hop
list, WRED parameters, etc.) and what parameters among them are
configurable;
- what statistics it keeps (e.g., drop count, CRC error count,
etc.);
- what events it can throw (e.g., table miss, port down, etc.).
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This document only intends to describe the conceptual FE model and
illustrate it with some examples. However, it is not the intention
of this document to define any specific block or the library itself.
Separate document(s) would be written to do that. The minimum set of
FE functions required in [FORCES-REQ] is listed and discussed
briefly in the following subsections. The IETF DiffServ
(Differentiated Services) and RAP (Resource Allocation Protocols)
working groups have done some relevant work in modeling the
provisioning policy data for QoS functions and filtering functions.
Therefore, we will start our discussion from these related models.
4.2.1. QoS Functions
The IETF community has already done some work in modeling the QoS
functions in the datapath. The IETF DiffServ working group has
defined an informal data model [RFC3290] for QoS-related functions
like classification, metering, marking, actions of marking,
dropping, counting and multiplexing, queueing, etc. The latest work
on DiffServ PIB (Policy Information Base) [DS-PIB] defines a set of
provisioning classes to provide policy control of resources
implementing the Diferentiated Services Architecture. DiffServ PIB
also has an element of capability flavor in it that can potentially
enable more dynamic and intelligent configuration of individual
functions and the interconnection of the functions. The IETF Policy
Framework working group is also defining an informational model
[QDDIM] to describe the QoS mechanisms inherent in different network
devices, including hosts. This model is intended to be used with the
QoS Policy Information Model [QPIM] to model how policies can be
defined to manage and configure the QoS mechanisms present in the
datapath of devices.
Unclassified classified
traffic traffic
+------------+
| |--> match Filter1 --> OutputA
------->| classifier |--> match Filter2 --> OutputB
| |--> no match --> OutputC
+------------+
Figure 2. An Example Classifier Using DiffServ Model
We use the classifier defined in [RFC3290] as an example to
illustrate the DiffServ model. "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." To further define filters: "A filter
consists of a set of conditions on the component values of a
packet's classification key (the header values, contents, and
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attributes relevant for classification)." Figure 2 illustrates an
example classifier.
Based on this conceptual model, [DS-PIB] specifies a classifier of
1:N by N classifier elements. Each classifier element specifies the
following:
- element ID which identifies the particular output out of N;
- classifier instance ID which identifies the classifier instance
(all the N classifier elements belong to the same classifier have
the same classifier instance ID);
- precedence which is an unsigned integer value to represent the
relative order in which classifier elements are applied (the
classifier element with the highest precedence will be matched
first);
- next datapath element which provides a pointer to the next
function along this branch out of N fan-out;
- filter ID which points to the filter used for this branch (Note
that filter is defined independent of the classifier and used here
as a parameter to the classifier).
It is clear from the example above that DiffServ model uses a
topological approach to capture the multiple datapath a packet can
potentially take. Graphically, a classifier of 1:N has N output
branches leading to the next N datapath elements. This has
significant implication when we consider the interconnected graph of
the functions on FE (see Section 4.3). The alternative is to use an
encoded state approach where each packet gets some state information
associated with it that indicates the datapath it takes next. For
example, using the encoded state approach, a classifier of 1:N may
be represented by just one output branch, if all N of the next
datapath elements are of the same block function, say, shaper.
+----------------+
| Meter-A |
| |
----->| In -|-----PM-1--->
| |
| Out -|-----PM-2--->
+----------------+
Figure 3: Meter Followed by Two Preamble Markers
The QDDIM model uses the alternative encoded state approach so that
information about the treatment that a packet received on an ingress
interface is allowed to be communicated along with the packet to the
egress interface (see [QDDIM] Section 3.8.3). QDDIM model represents
this information transfer in terms of a packet preamble. Figure 3
shows the same example used in [QDDIM] (section 3.8.3) in which
meter results are captured in a packet preamble. ôPreamberMarker PM-
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1 adds to the packet preamble an indication that the packet exited
Meter A as conforming traffic. Similarly, PreambleMarker PM-2 adds
to the preambles of packets that come through it indications that
they exited Meter A as nonconforming traffic. A PreambleMarker
appends its information to whatever is already present in a packet
preamble, as opposed to overwriting what is already there.ö ôTo
foster interoperability, the basic format of the information
captured by a PreambleMarker is specified.ö ôOnce a meter result has
been stored in a packet preamble, it is available for any subsequent
Classifier to use.ö
Section 4.3 has more discussion on the difference between the
topological approach (as used by DiffServ model) and the encoded
state approach (as used by QDDIM).
[DS-PIB] also defines a capability model for classifiers by
specifying a bit set to indicate the ability to classify based on IP
source address, IP destination address, IP protocol numbers, IP DSCP
field, layer 4 port number for UDP and TCP, and Ipv6 flow ID. The
capability is thus made known by simply setting the bits
accordingly. Similar technique is also used to indicate capabilities
of other functions like meters, droppers, etc.
While the DiffServ and QDDIM models are not designed with the
primary goal of direct machine implementation, we can still use them
as our starting point.
4.2.2. Generic Filtering Functions
The framework PIB ([FRMWK-PIB]) from the IETF RAP (Resource
Allocation Protocol) working group defines four groups of PRCs
(Provisioning Classes) that are expected to be common to all clients
that provision policy using COPS-PR ([RFC3084]). One of the four PRC
groups is classifier group, which contains the Base Filter Class and
the other extended filters including the IP Filter, the IEEE 802
Filter and the Internal Label Filter. Even if SPPI ([RFC3159]) is
not the final chosen data model for our FE model, it may still be
valuable to use the work done here as a starting point for the
generic filter functions modeling.
4.2.3. Vendor Specific Functions
New and currently unknown FE functionality can be derived (i.e.,
extended) based on the generic FE Block. The name space used to
identify the FE block type must be extensible such that new logical
functions can be defined and added later to accommodate future
innovation in forwarding plane, as long as the new functions are
modeled as an FE block.
4.2.4. Port Functions
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Every FE contains a certain number of interfaces (ports), including
both the inter-NE interfaces and intra-NE interfaces. The inter-NE
interfaces are the external interfaces for the NE to receive/forward
packets from/to the external world. The intra-NE interfaces are used
for FE-FE or FE-CE communications.
Certain types of physical ports have sub-interfaces (frame relay
DLCIs, ATM VCs, Ethernet VLans, etc.) as virtual or logical
interfaces. Some implementations treat tunnels (e.g., GRE, L2TP,
IPSec, MPLS, etc.) as interfaces, while others do not. [FORCES-REQ]
treats tunneling as high-touch functions and so FE model does not
model tunneling as part of the port functions. Instead, tunneling is
covered in Section 4.2.6.
Port function expresses:
- the number of ports on the FE;
- the sub-interfaces if any;
- the static attributes of each port (e.g., port type, direction,
link speed);
- the configurable attributes of each port (e.g., IP address,
administrative status);
- the statistics collected on each port (e.g., number of packets
received);
- the current status (up or down).
4.2.5. Forwarding Functions
Support for IPv4 and IPv6 unicast and multicast forwarding functions
must be provided by the model.
Typically, the control plane maintains the Routing Information Base
(RIB), which contains all the routes discovered by all the routing
protocols with all kinds of attributes relevant to the routes. The
forwarding plane uses a different database, the Forwarding
Information Base (FIB), which contains only the active subset of
those routes (only the best routes chosen for forwarding) with
attributes that are only relevant for forwarding. A component in the
control plane, termed Route Table Manager (RTM), is responsible to
manage the RIB in the CE and maintain the FIB used by the FEs.
Therefore, the most important aspect in modeling the forwarding
functions is the data model for the FIB. The model also needs to
support the possibility of multiple paths.
At the very minimum, each route in the FIB needs to contain the
following layer-3 information:
- the prefix of the destination IP address;
- the length of the prefix;
- the number of equal-cost multi-path;
- the next hop IP address and the egress interface for each path.
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Another aspect of the forwarding functions is the method to resolve
a next hop destination IP address into the associated media address.
There are many ways to resolve Layer 3 to Layer 2 address mapping
depending upon link layer. For example, in case of Ethernet links,
the Address Resolution Protocol (ARP, defined in RFC 826) is used
for IPv4 address resolution.
Assuming a separate table is maintained in the FEs for address
resolution, the following information is necessary for each address
resolution entry:
- the next hop IP address;
- the media address.
Different implementation may have different ways to maintain the FIB
and the resolution table. For example, a FIB may consist of two
separate tables, one to match the prefix to the next hop and the
other to match the next hop to the egress interface. Another
implementation may use one table instead. Our model of the
forwarding functions should allow such flexibility.
4.2.6. High-Touch Functions
High-touch functions are those that take action on the contents or
headers of a packet based on content other than what is found in the
IP header. Examples of such functions include NAT, ALG, firewall,
tunneling and L7 content recognition.
The ForCES working group first needs to agree upon a small set of
common high-touch functions with well-defined behavior to be
included in the initial FE block library.
4.2.7. Security Functions
The FE model must be able to describe the types of encryption and/or
decryption functions that an FE supports and the associated
attributes for such functions.
4.2.8. Off-loaded Functions
In addition to the packet processing functions that are typical to
find on the FEs, some logical functions may also be executed
asynchronously by some FEs, according to a certain finite-state
machine, triggered not only by packet events, but by timer events as
well. Examples of such functions include finite-state machine
execution required by TCP termination or OSPF Hello processing off-
loaded from the CE. The FE model must be capable of expressing these
asynchronous functions, so that the CE may take advantage of such
off-loaded functions on the FEs.
The ForCES working group first needs to agree upon a small set of
such off-loaded functions with well-understood behavior and
interactions with the control plane.
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4.3. FE Stage and Directed Graph of FE
With a set of basic FE functions defined in the block library, we
are ready to model any FEÆs packet processing behavior by a
directional graph where each node is an instance of an FE block,
representing a processing stage in the packet datapath. This section
describes the details behind such a ôdirected graphö FE model.
4.3.1. Basic Concepts
An FE stage is simply an instance of an FE block within an FE's
datapath. As a packet flows through an FE along a datapath, it flows
through one or multiple distinct stages, with each stage
instantiating a certain FE logical function. Each FE allocates an
FE-unique stage ID to each of its stages and passes the stage ID
along with the corresponding block type as part of the FE stage
information. This allows multiple instances of the same block
present in a FE's datapath. Using NAT as an example, one NAT
function is typically performed before the forwarding stage (packets
arriving externally have their public addresses replaced with
private addresses) and one NAT function is performed after (for
packets exiting the domain, their private addresses are replaced by
public ones). So there are three stages (NAT, forwarding, and NAT
again) in this example datapath, with two NAT instances present in
two different stages.
A static FE can be modeled by a directed graph interconnecting all
the stages present in the FE. Each node in the graph corresponds to
a stage. In order to represent the directed interconnection between
two consecutive stages along a datapath, each stage contains a ônext
stageö pointer that is simply the stage ID of its next stage in the
graph. Therefore, the following defines an FE stage (i.e., a node in
the FE gragh):
- stage identifier which uniquely identifies the node within this FE
graph;
- block type which identifies the block function that this stage is
an instance of;
- number of downstream stages which corresponds to the number of
downstream nodes connected to this stage;
- downstream stage identifiers which corresponds to the set of
downstream nodes connected to this stage.
With such information defined for each FE stage, it is now possible
for CE to query the state of the static FE graph by querying for the
initial (ingress) stages of the graph and then traversing the whole
graph in a node-by-node fashion.
4.3.2. Topological versus Encoded State Approaches
As pointed out in Section 4.2.1, there are potentially two different
approaches to model the nodes and the connections between the nodes
in the FE graph, namely, the topological approach and the encoded
state approach.
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+------------+ +------------+ +------------+
input | Ethernet | | | | Ethernet |output
------->| Ingress |-->| IPv4 L3 LPM|-->| Egress |----->
| Port Mgr | | Forwarder | | Port Mgr |
+------------+ +------------+ +------------+
{stage ID=1, {stage ID=2, {stage ID=3,
type= type= type=
Enet-IngP-Mgr, IPv4-L3-LPM-fwd, Enet-EgP-Mgr,
#downstream=1, #downstream=1, #downstream=1,
downstream={2} downstream={3} downstream=none
} } }
Figure 4. A simple example of an FE graph using encoded state
approach.
Input +------------+ +------------+ output
------->|Ingr-Port #1|-->| |
+------------+ | | +------------+
------->|Ingr-Port #2|-->| |-->|EgressPort#1|----->
+------------+ | | +------------+
------->|Ingr-Port #3|-->|IPv4 L3 LPM |-->|EgressPort#2|----->
+------------+ |Forwarder | +------------+
------->|Ingr-Port #4|-->| |-->|EgressPort#3|----->
+------------+ | | +------------+
------->|Ingr-Port #5|-->| |-->|EgressPort#4|----->
+------------+ | | +------------+
------->|Ingr-Port #6|-->| |
+------------+ +------------+
{stage ID=1 {stage ID=7, {stage ID=8,
type= type= type=
Enet-Ing-port, IPv4-L3-LPM-fwd, Enet-Eg-port,
#downstream=1, #downstream=4, #downstream=1,
downstream={7} downstream= downstream=none
} {8,9,10,11} }
. . . } . . .
{stage ID=6 {stage ID=11,
type= type=
Enet-Ing-port, Enet-Eg-port-Mgr,
#downstream=1, #downstream=1,
downstream={7} downstream=none
} }
Figure 5. The same example as in Figure 4 using topological
approach.
Using the topological approach as exemplified by DiffServ model,
there are N connections between a fan-out node of 1:N (e.g., a
classifier) and its next stages. Using the encoded state approach,
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fewer connections are typically needed between the same fan-out node
and its next stages, because each packet carries some state
information as metadata that the next stage nodes can interpret and
invoke different packet treatment. Pure topological approaches can
be overly complex to represent because they force on to build
elaborate topologies with a lot more connections. An encoded state
approach is nicer in that it allows one to simplify the graph and
represent the functional blocks with more clarity. But it does
require extra metadata to be carried along with the packet, like the
preamble in the QDDIM model.
For example in Figure 4, stage #2 (IPv4 L3 LPM Forwarder) generates
some metadata at its output to carry information on which port the
packets should go to, and #3 (Enet-Egress-port-Manager) uses this
meta data to direct the packets to the right egress port. Figure 5
shows how the FE graph looks like when using the pure topological
approach instead, assuming 6 ingress and 4 egress ports. It is clear
that Figure 5 is unwieldy compared to Figure 4.
Queue1
+---+ +--+
| A|------------------->| |--+
+->| | | | |
| | B|--+ +--+ +--+ +--+ |
| +---+ | | | | | |
| Meter1 +->| |-->| | |
| | | | | |
| +--+ +--+ |
| Counter1 Absolute Queue2| +--+
+---+ | Dropper1 +--+ +--->|A |
| A|---+ | |------>|B |
-------->| B|------------------------------>| | +--->|C |------>
| C|---+ +--+ | +->|D |
| X|-+ | | | +--+
+---+ | | +---+ +---+ Queue3| | Scheduler
Classifier1 | | | A|------------>|A | +--+ | |
| +->| | | |->| |--+ |
| | B|--+ +--+ +->|B | | | |
| +---+ | | | | +---+ +--+ |
| Meter2 +->| |-+ Mux1 |
| | | |
| +--+ Queue4 |
| Marker1 +--+ |
+---------------------------->| |----+
| |
+--+
Figure 6. An FE example with multiple datapath.
Note that the FE graph can represent largely arbitrary topologies of
the stages, regardless which approach (topological or encoded state)
is taken. For example, Figure 6 shows an FE implementing QoS
functions via a combination of logical functions like classifier,
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meter, marker, queue, scheduler, etc. Both approaches are able to
represent such an FE graph. The only restrictions on topology relate
to the source and sink nature of ingress and egress port functions
respectively. For example, egress port functions must not have any
downstream stages whereas no other stage may refer to an ingress
port function as one of its downstream stages.
4.3.3. Cascading FE Blocks
An FE block may contain zero, one or more ingress port stages.
Similarly, an FE block may contain zero, one or more egress port
stages. In another word, not every FE block has to contain any
ingress port or egress port stages. For example, Figure 7 shows two
cascading FE blocks. Block #1 contains one ingress port function but
no egress port function, while block #2 contains one egress port
function but no ingress port function. It is possible to connect
these two FE blocks together to achieve the complete ingress-to-
egress packet processing function. This provides the flexibility to
spread the functions across multiple FEs and interconnect them
together later for certain applications.
-------------------------------------------------------
| +---------+ +------------+ +---------+ |
input| | | | | | output |
---+->| Ingress |-->|Header |-->|IPv4 |---------+--->+
| | port | |Decompressor| |Forwarder| FE | |
| +---------+ +------------+ +---------+ Block #1| |
------------------------------------------------------| V
|
+-----------------------<-----------------------------+
|
| |-----------------------------------------
V | +------------+ +----------+ |
| input | | | | output |
+->--+->|Header |-->| Egress |---------+-->
| |Compressor | | port | FE |
| +------------+ +----------+ Block #2|
-----------------------------------------|
Figure 7. An example of two different FE blocks connected together.
5. Data Modeling and Representation
A formal data modeling language is needed to represent the
conceptual FE model described in this document and a full
specification will be written using such a data modeling language.
It is also necessary to identify a data representation method for
over-the-wire transport of the FE model data.
The following is a list of some potential candidates for
consideration. For the moment, we intend to leave this as an open
issue and much debate is needed in the ForCES WG before a decision
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can be made. Therefore, we only provide the candidate list and some
initial discussion here without drawing a conclusion yet.
- XML (Extensible Markup Language) Schema
- ASN.1 (Abstract Syntax Notation One)
- SMI (Structure of Management Information) [RFC1155]
- SPPI (Structure of Policy Provisioning Information) [RFC3159]
- UML (Universal Modeling Language)
Most of the candidates here, with the notable exception of UML, are
capable of representing the model in the document and over-the-wire.
Of course, it is also possible to choose one data model language for
specification in the document and later allow several over-the-wire
representations to map the model into different implementations.
XML has the advantage of being human and machine readable with
widely available tools support. However, it is very verbose and
hence less efficient for over-the-wire transport. It also requires
XML parsing functions in both the CE and FE and hence may impose
large footprint esp. for FEs. Currently XML is not yet widely
deployed and used in network elements. XML for network configuration
in general remains an open area that still requires substantial
investigation and experiment in IETF.
ASN.1 format is human readable and widely used in network protocols.
SMI is based on a subset of ASN.1 and used to define Management
Information Base (MIB) for SNMP. SPPI is the adapted subset of SMI
used to define Policy Information Base (PIB) for COPS. Substantial
investment has been made in SMI/MIBs/SNMP by IETF and the Internet
community collectively has had many years of design and operation
experience with SMI/MIBs/SNMP. However, it is also well recognized
that SMI/MIBs/SNMP is not well suited for configuration and so
SPPI/PIBs/COPS-PR attempts to optimize for network provisioning and
configuration.
UML is the software industryÆs standard language for specifying,
visualizing, constructing and documenting the artifacts of software
systems. It is a powerful tool for data modeling. However, it does
not provide a data representation format for over-the-wire
transport.
6. Security Considerations
The FE model just describes the representation and organization of
data sets and attributes in the forwarding plane. The associated
communication protocol (i.e., ForCES protocol) will be defined in
separate documents and so the security issues will be addressed
there.
7. Intellectual Property Right
The authors are not aware of any intellectual property right issues
pertaining to this document.
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8. IANA consideration
A namespace is needed to uniquely identify the FE block type for
each FE logical function.
9. Normative References
[RFC1812] F. Baker, ôRequirements for IP Version 4 Routers", June
1995.
[RFC1155] M. Rose, et. al., ôStructure and Identification of
Management Informationfor TCP/IP-based Internets", May
1990.
[RFC3084] K. Chan, et. al., ôCOPS Usage for Policy Provisioning,ö
March 2001.
[RFC3159] K. McCloghrie, et. al., ôStructure of Policy Provisioning
Information (SPPI)", August 2001.
[RFC3290] Y. Bernet, et. al., ôAn Informal Management Model for
Diffserv Routersö, May 2002.
10. Informative References
[FORCES-REQ] H. Khosravi, et. al., ôRequirements for Separation of
IP Control and Forwarding", work in progress, Oct 2002,
<draft-ietf-forces-requirements-07.txt>.
[DS-PIB] M. Fine, et. al., ôDifferentiated Services Quality of
Service Policy Information Baseö, work in progress, June
2002, <draft-ietf-diffserv-pib-09.txt>.
[FRMWK-PIB] M. Fine, et. al., ôFramework Policy Information Baseö,
work in progress, June 2002, <draft-ietf-rap-
frameworkpib-09.txt>.
[QDDIM] B. Moore, et. al., ôInformation Model for Describing Network
Device QoS Datapath Mechanismsö, work in progress, May
2002, <draft-ietf-policy-qos-device-info-model-08.txt>.
[QPIM] Y. Snir, et. al., ôPolicy Framework QoS Information Modelö,
work in progress, Nov 2001, <draft-ietf-policy-qos-info-
model-04.txtö.
11. Acknowledgments
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The authors would also like to thank the following individuals for
their invaluable technical input: David Putzolu, Hormuzd Khosravi,
Eric Johnson, David Durham, Andrzej Matejko, T. Sridhar.
12. Authors' Addresses
Lily L. Yang
Intel Labs
2111 NE 25th Avenue
Hillsboro, OR 97124 USA
Phone: +1 503 264 8813
Email: lily.l.yang@intel.com
Joel Halpern
P.O.Box 6049
Leesburg, VA 20178
Phone: +1 703 371 3043
Email: jmh@joelhalpern.com
Ram Gopal
Nokia Research Center
5, Wayside Road,
Burlington, MA 01803
Phone: +1 781 993 3685
Email: ram.gopal@nokia.com
Ram Dantu
University of Texas Dallas
2601 North Flyod Road
Richardson Texas 75082
Phone: +1 972 883 4653
Email: ram.dantu@utdallas.edu
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