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Network Working Group J. Schoenwaelder
Internet-Draft Jacobs University Bremen
Intended status: Informational September 25, 2007
Expires: March 28, 2008
Protocol Independent Network Management Data Modeling Languages -
Lessons Learned from the SMIng Project
draft-schoenw-sming-lessons-01.txt
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Abstract
A data modeling language for network management protocols called
SMIng was developed within the Network Management Research Group of
the Internet Research Task Force over a period of several years.
This memo documents some of the lessons learned during the project
for consideration by designers of future data modeling languages for
network management protocols.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. SMIng Goals and History . . . . . . . . . . . . . . . . . . . 4
3. Lessons Learned . . . . . . . . . . . . . . . . . . . . . . . 5
3.1. Readers, Writers, Tool Developers . . . . . . . . . . . . 5
3.2. Data vs. Command vs. Object vs. Document . . . . . . . . . 5
3.3. Naming Independence . . . . . . . . . . . . . . . . . . . 7
3.4. Errors and Exceptions . . . . . . . . . . . . . . . . . . 7
3.5. Configuration Storage Models . . . . . . . . . . . . . . . 8
3.6. Selection of Language Features . . . . . . . . . . . . . . 8
3.7. Syntax versus Semantics . . . . . . . . . . . . . . . . . 8
3.8. Reuse of Definitions . . . . . . . . . . . . . . . . . . . 9
3.9. Versioning and Meta Information . . . . . . . . . . . . . 9
3.10. Extensibility . . . . . . . . . . . . . . . . . . . . . . 9
3.11. Language Independence . . . . . . . . . . . . . . . . . . 10
3.12. Compliance and Conformance . . . . . . . . . . . . . . . . 10
4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 11
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
6. Security Considerations . . . . . . . . . . . . . . . . . . . 11
7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 11
8. Informative References . . . . . . . . . . . . . . . . . . . . 11
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . . 12
Intellectual Property and Copyright Statements . . . . . . . . . . 14
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1. Introduction
Network management is often performed using a collection of different
management protocols. Many operators use several different
management protocols to access a single managed device for different
purposes, such as device configuration, event and notification
delivery, and collection of statistics. While some operators manage
to settle on a single protocol for a given management task and/or
domain, it is not uncommon that different operators select different
competing management protocols for the same management task and/or
domain.
As a result, device vendors are forced to support multiple management
interfaces to satisfy their customers. This increases software
complexity and costs. It is therefore understandable that device
vendors are interested to reduce the complexity and efforts
associated with supporting multiple management interfaces. One
obvious step in this direction could be to have a common data model
which is underlying all management protocol interfaces instead of the
current situation where every management protocol interface has its
own slightly different data model. As a consequence, it is
attractive to design data modeling languages that are protocol
independent and allow for a "late binding" to concrete management
protocols.
The Network Management Research Group (NMRG) of the Internet Research
Task Force (IRTF) has developed a new protocol independent data
modeling language for network management protocols called SMIng
[RFC3780]. Work on SMIng started in 1999 and concluded with the
publication of the core language [RFC3780] and an extension providing
mappings to the Simple Network Management Protocol (SNMP) [RFC3781]
in 2004.
This memo documents some of the lessons learned during the project
for consideration by engineers of future data modeling languages for
network management protocols. Our focus is on technical design
aspects that have turned out to be more difficult than expected as
well as aspects that required significant discussions in order to
understand trade-offs.
The rest of this document is structured as follows. Section 2
provides a short review of the goals and the history of the SMIng
project. Section 3 discusses twelve lessons learned. The document
concludes in Section 4.
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2. SMIng Goals and History
The SMIng project started in 1999 as a research project to address
some drawbacks of SMIv2 [RFC2578] [RFC2579] [RFC2580], the current
standard data modeling language for management information base
modules in the IETF [DSOM99]. Primarily, SMIv2's partial dependence
on ASN.1 and a number of exception rules were considered to be
problematic. In 2000, the work was handed over to the IRTF Network
Management Research Group where SMIng was significantly detailed. In
1999/2000, the work of the Resource Allocation Protocol (RAP) working
group on the Common Open Policy Service (COPS) usage for Policy
Provisioning (COPS-PR) [RFC3084] lead to the creation of the
Structure of Policy Provisioning Information (SPPI) [RFC3159]. As a
consequence, SMIng was split into two parts: a core protocol
independent data definition language and protocol specific mappings
to allow the access of core definitions through (potentially)
multiple management protocols. The replacement of SMIv2 and SPPI by
a single merged data definition language was also a primary goal of
the IETF SMING working group chartered at the end of 2000. The SMING
working group, after carefully documenting the objectives [RFC3216],
could not reach agreement on a common solution, resulting in the
working group being closed down in April 2003. The NMRG then
published the SMIng specifications in 2004 in [RFC3780] and [RFC3781]
in order to document the results achieved in the NMRG.
The published version of SMIng aimed at being a data modeling
language that can be adapted for different management protocols and
thus be closer to an information modeling language. See RFC 3444
[RFC3444] for a more detailed discussion of the terms data modeling
language and information modeling language.
The problem which drove the development of SMIng, namely duplicated
data modeling efforts and often also duplicated instrumentation code,
is still widely unsolved and it even may have become more pressing.
A standard data modeling language capable to drive tools that can
automate the implementation of command line interfaces, monitoring
interfaces, and configuration interfaces from a single specification
surely would have significant value for the networking industry and
standardization bodies.
During the SMIng project, people have looked at various issues that
have to be dealt with in this context. The purpose of this memo is
to document these issues and some of the alternatives that have been
explored to tackle them so that future language designers can learn
from the SMIng experiment.
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3. Lessons Learned
This section summarizes the lessons learned from the SMIng project.
Designers of future data modeling languages for network management
protocols are encouraged to study these lessons to avoid potential
pitfalls.
3.1. Readers, Writers, Tool Developers
Network management data modeling languages are used by engineers to
develop and specify data models for (sub-components of) managed
systems. The data models are later read by implementers with the
goal to create interoperable implementations. Network operators
often read data models while managing networks and services. A data
modeling language is therefore used by both data model writers and
data model readers. In most cases, the number of data model readers
is much larger than the number of data model writers. Hence, when
taking design decisions, it may be necessary to consider to what
extent the design choice simplifies the task of data model readers
and the task of data model writers. All other things being equal,
preference should be given to solutions that simplify the task of
data model readers.
The third group to be considered in the data modeling language design
process are tool developers creating parser libraries or compilers.
In general, language features should be simple and straight forward
to implement in order to achieve wide spread tool support. However,
when considering alternatives, it is important to realize that in
most cases the number of data model writers is much larger than the
number of developers of tools (e.g., parsers, compilers) supporting a
data modeling language.
As a consequence, preference should be give to solutions that
simplify the task of readers. Reduction of the efforts required by
writers is of secondary importance and the reduction of the efforts
required by tool developers is of least important preference. While
this perhaps seems obvious, it is at times difficult to take
according design decisions, especially if the group working on a new
data modeling language is dominated by tool developers or data model
writers.
3.2. Data vs. Command vs. Object vs. Document
There are different approaches to model network management interfaces
and to design supporting protocols:
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o The data-oriented approach models all management aspects through
data objects that can be read, written, created, and destroyed.
The SNMP technology is an extreme example of this approach where
even create and destroy operations are handled as side effects of
write operations. While the data-oriented approach is
conceptually simple, it is usually difficult to use for modeling
complex operations, for example due to atomicity issues.
o The command-oriented approach does not model data objects but
instead treats the state maintained by a managed element as a
black box and provides specific commands to cause state changes or
to retrieve/modify some selected internal state information. The
command-oriented approach is widely used by command line
interfaces.
o The object-oriented approach can be seen as a combination of the
data-oriented and the command-oriented approach where commands are
bound to data objects and the black-box state is limited to the
internal state of objects. The object-oriented approach is being
used for example by the Common Information Model.
o The document-oriented approach represents the state of a device
and its configuration as a structured document. As a consequence,
primitive operations are the retrieval of (parts of) a document
and the application of changes to move from one document version
to the next. Since a configuration is treated as a complete
document, it becomes more natural to support coarse grained atomic
operations. The document-oriented approach is used by the Network
Configuration (NETCONF) protocol [RFC4741] recently published by
the IETF.
Note that the choice of the data modeling approach has impact on the
features required by a data modeling language. Trying to be protocol
neutral may imply to support multiple of these approaches, which
clearly increases the complexity of the language design. A data
modeling language aiming at supporting a document-oriented view for
configuration, a data-oriented view for monitoring, and a command-
oriented view for ad-hoc operations on a device must be able to link
say a command to the data objects that may be manipulated to achieve
the behaviour defined for that command. This quickly becomes
difficult and it might be better to avoid mixing different data
modeling approaches.
Unfortunately, in many real-world implementations, new management
protocols will be used together with existing interfaces or
databases, which already follow one of the approaches. This will
force the designers of new data modeling languages to accommodate
features of multiple approaches, even if this makes the result more
complicated.
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3.3. Naming Independence
One of the goals of the SMIng project was to provide a data
definition language where the data definitions are not tied to a
specific management protocol. Since protocols typically use
different approaches to name instances of managed objects, it is
required to support multiple instance naming systems. While this may
sound easy to do, it turns out that trying to achieve naming
independence has many implications:
o There is in general a need to model relationships. A common
approach is to refer to other definitions using names as pointers.
Being naming system independent, such a pointer needs to be
abstracted and the protocol mappings then need to explain how the
pointer works for a given protocol. This quickly becomes
difficult since management protocols tend to impose protocol
specific constraints, resulting from the different naming systems.
o Many relationships between data model components are typically
explained in description clauses. Being protocol neutral,
description clauses have to be worded carefully so that they make
sense with the various protocol mappings. This significantly
increases the efforts to write data models and to review them.
o When thinking about relationships, it is crucial to think about
fate sharing of data objects, namely does the removal of an object
imply the removal of other objects? Being naming system
independent causes some additional challenges here since some fate
sharing properties may be implicit in the naming system used in
one protocol mapping while they must be explicit in other protocol
mappings.
3.4. Errors and Exceptions
Well written data definitions include clear definitions how
implementations should react in various error situations or during
run-time exceptions. It is often a mistake if a data model writer
assumes that implementers will choose a particular error code in a
given error situation. For interoperability reasons, it is far
better to spell out the precise behaviour in error situations and
run-time exceptions. Unfortunately, it becomes quite difficult to be
precise about errors and exceptions in a protocol and naming system
neutral data modeling framework such as SMIng since all error and
exception reporting mechanisms of the underlying protocols need to be
abstracted because otherwise description clauses become protocol
specific. Furthermore, due to the varying features of the underlying
protocols, some errors and exceptions may only exist in some of the
mappings and must therefore be dealt with when the generic data model
is mapped to a concrete protocol.
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3.5. Configuration Storage Models
Management protocols use different mechanisms to deal with
persistence of configuration state. The SNMP protocol uses
StorageType [RFC2579] columns in conceptual tables to indicate which
rows are persistent. The COPS-PR [RFC3084] protocol binds
provisioned information to the existence of the COPS-PR session which
provisioned policy information. The NETCONF protocol [RFC4741] has a
concept of different data stores (running, startup, candidate) that
imply whether configuration state is persistent or not.
Given these fundamentally different approaches, it is difficult to
write protocol neutral configuration data models that work equally
well with the various management protocols. While it is possible to
introduce say StorageType columns in protocol mappings, it is
sometimes awkward to define the semantics of data objects if the
persistence model is left to the protocol mappings.
3.6. Selection of Language Features
A data modeling language, like any good language, should be based on
a small orthogonal set of language features to control the overall
complexity of the language, both in terms of usage and
implementation. While it is relatively easy to invent and propose
new features, it seems much harder to find a minimal and orthogonal
set of basic features that addresses the requirements.
During the design of the SMIng language, it was found useful to
repeatedly ask the question why certain features are needed or
whether there are any language rules without a strong and convincing
justification. The general principle stated by Antoine de Saint-
Exupery was followed:
Perfection [in design] is achieved, not when there is nothing more
to add, but when there is nothing left to take away.
Language features should be selected by considering what tools can
actually do with the information formalized by a language feature.
In general, language features should raise automation and improve
clarity. Features without a clear justification that they actually
reduce implementation efforts or significantly improve the clarity of
a data model specification should not be accepted.
3.7. Syntax versus Semantics
During the design of a data modeling language, it is most important
to focus on a clear semantic of all language constructs. Syntactical
issues are of secondary importance. While this may sound obvious, it
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is important, especially in open consensus driven processes, to keep
contributors reminded that semantics of language constructs come
first while the syntactic aspects are of secondary importance.
3.8. Reuse of Definitions
A good data modeling language should allow data model writers to
produce reusable definitions. However, not all definitions are
equally easy to reuse. Experience has shown that data types derived
from a small set of base types are usually very easy to reuse;
structured data types are often getting slightly more complex to
reuse. Reusing collections of structured data types with non-trivial
interrelationships are often difficult to reuse. The low hanging
fruits are therefor the simple type definitions.
Sub-typing is frequently used to add specific semantics to a more
generic type and to constrain the set of possible values. While
constraining values is often a good thing, it should be noted that
too strict constraints can turn out to be painful in the future.
There are many examples where data types proved too constrained to
represent protocols that followed a natural evolution.
3.9. Versioning and Meta Information
Changes to data models are inevitable. Even the most careful design
and review process will not be able to predict how technologies
evolve in the future. It is therefore crucial that the data modeling
language supports a suitable versioning model and that it establishes
clear rules which changes are possible without having to change a
version number or (module) name. Supporting complex collections of
meta data (e.g., a granular revision log) as part of the data
modeling language may be less of an issue since such information can
often be maintained in revision control systems and automatically
placed into comments where needed.
3.10. Extensibility
It has proven to be useful to support language extensibility features
that avoid backward compatibility problems with existing parsers when
new language features are introduced. If a data modeling language
supports language extensions, it should be possible to gradually
introduce new language features.
Supporting language extensions essentially requires to be very
consistent with the syntactic structure so that deployed
implementations can skip over new language constructs. In addition,
it is necessary to precisely identify language extensions so that
implementations supporting extensions know where to apply the
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appropriate additional processing.
3.11. Language Independence
One way to define a new data modeling language is to start from an
existing language and to extend it and/or subset it as needed.
However, there are some dangers associated with this approach.
Since languages that are in actual use are not static, it is
important to deal with conflicts that can arise if the base language
is revised such that the extensions or subsets do not work anymore.
This issue becomes particularly important if different organizations
have change control since this makes coordination relatively complex.
Things get even worse if the data modeling extension constitutes not
a significant usage of the core language.
Another risk associated with sub-setting an existing language is that
generic tools are likely not aware of the subset allowed in data
models and hence such tools will not help to restrict the features of
the general language to the subset adequate to define management data
models.
The alternative is to provide a complete and self-contained
definition for the data modeling language. This protects against
change control issues and also makes it simpler for implementers or
data model writers to find all language rules in one place.
As a general rule, there is hardly a reason to import language
definitions from other organizations. If you like to import
something from a source that is likely to change, do not import. If,
however, the source is assumed to be stable, then you can just import
by value and explain the relationship to the original source.
3.12. Compliance and Conformance
Any successful data modeling language will at some point have to deal
with compliance and conformance definitions. However, designing
language features to express compliance and conformance statements is
non-trivial. Very fine grained mechanisms to define implementation
requirements for different usage scenarios can lead to very detailed
but also very complex compliance and conformance definitions that are
difficult to understand, review and maintain.
It is therefore important to make a good trade-off between the
required expressiveness and the pragmatic usage of compliance and
conformance definitions. The general considerations for language
features discussed Section 3.6 particularly apply here. One approach
is to first gain experience with the usage of a new data modeling
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language and to support compliance and conformance definitions
through language extensions, improving their expressiveness over time
as needed.
4. Conclusions
This memo documents some of the insights gained in the SMIng project
in order to guide future data modeling language designers by making
them aware of some of the problems encountered in the SMIng project
and some of the design guidelines that have been developed through
the project.
While protocol independent data modeling languages seem like a very
good idea in world which uses several different management protocols
for different tasks and deployments, the complexity associated with
the design and effective usage of protocol independent data modeling
languages should not be underestimated.
5. IANA Considerations
This document has no IANA actions.
6. Security Considerations
This document discusses lessons learned during the design of a
network management data modeling language and they have no impact on
the security of the Internet.
7. Acknowledgements
Many people contributed directly or indirectly to this documented
through their participation in the SMIng project. Martin Bjorklund,
David Harrington, Balazs Lengyel, Frank Strauss and Bert Wijnen
provided valuable feedback on earlier versions of this document.
8. Informative References
[RFC2578] McCloghrie, K., Perkins, D., and J. Schoenwaelder,
"Structure of Management Information Version 2 (SMIv2)",
RFC 2578, STD 58, April 1999.
[RFC2579] McCloghrie, K., Perkins, D., and J. Schoenwaelder,
"Textual Conventions for SMIv2", RFC 2579, STD 58,
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April 1999.
[RFC2580] McCloghrie, K., Perkins, D., and J. Schoenwaelder,
"Conformance Statements for SMIv2", RFC 2580, STD 58,
April 1999.
[RFC3084] Chan, K., Seligson, J., Durham, D., Gai, S., McCloghrie,
K., Herzog, S., Reichmeyer, F., Yavatkar, R., and A.
Smith, "COPS Usage for Policy Provisioning (COPS-PR)",
RFC 3084, March 2001.
[RFC3159] McCloghrie, K., Fine, M., Seligson, J., Chan, K., Hahn,
S., Sahita, R., Smith, A., and F. Reichmeyer, "Structure
of Policy Provisioning Information (SPPI)", RFC 3159,
August 2001.
[RFC3216] Elliot, C., Harrington, D., Jason, J., Schoenwaelder, J.,
Strauss, F., and W. Weiss, "SMIng Objectives", RFC 3216,
December 2001.
[RFC3780] Strauss, F. and J. Schoenwaelder, "SMIng - Next Generation
Structure of Management Information", RFC 3780, May 2004.
[RFC3781] Strauss, F. and J. Schoenwaelder, "Next Generation
Structure of Management Information (SMIng) Mappings to
the Simple Network Management Protocol (SNMP)", RFC 3781,
May 2004.
[RFC3444] Pras, A. and J. Schoenwaelder, "On the Difference between
Information Models and Data Models", RFC 3444,
January 2003.
[RFC4741] Enns, R., "NETCONF Configuration Protocol", RFC 4741,
December 2006.
[DSOM99] Schoenwaelder, J. and F. Strauss, "Next Generation
Structure of Management Information for the Internet",
Springer LNCS 1700, October 1999.
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Author's Address
Juergen Schoenwaelder
Jacobs University Bremen
Campus Ring 1
28725 Bremen
Germany
Phone: +49 421 200-3587
Email: j.schoenwaelder@jacobs-university.de
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