Internet DRAFT - draft-irtf-ncrg-complexity-framework
draft-irtf-ncrg-complexity-framework
Internet Research Task Force M. Behringer
Internet-Draft Cisco
Intended status: Informational G. Huston
Expires: May 07, 2014 Asia Pacific Network Information Centre
November 03, 2013
A Framework for Defining Network Complexity
draft-irtf-ncrg-complexity-framework-01.txt
Abstract
Complexity is a widely used parameter in network design, yet there is
no generally accepted definition of the term. Complexity metrics
exist in a wide range of research papers, but most of these address
only a particular aspect of a network, for example the complexity of
a graph or software. There is a desire to define the complexity of a
network as a whole, as deployed today to provide Internet services.
This document provides a framework to guide research on the topic of
network complexity.
Status of This Memo
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Copyright Notice
Copyright (c) 2013 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. General Considerations . . . . . . . . . . . . . . . . . . . 3
2.1. The Behavior of a Complex Network . . . . . . . . . . . . 3
2.2. Robust Yet Fragile . . . . . . . . . . . . . . . . . . . 4
2.3. The Complexity Cube . . . . . . . . . . . . . . . . . . . 4
2.4. Related Concepts . . . . . . . . . . . . . . . . . . . . 4
2.5. Technical Debt . . . . . . . . . . . . . . . . . . . . . 5
2.6. Layering considerations . . . . . . . . . . . . . . . . . 6
3. Tradeoffs . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4. Structural Complexity . . . . . . . . . . . . . . . . . . . . 7
5. Components of Complexity . . . . . . . . . . . . . . . . . . 7
5.1. The Physical Network (Hardware) . . . . . . . . . . . . . 7
5.2. State in the Network . . . . . . . . . . . . . . . . . . 7
5.3. Churn . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.4. Algorithms . . . . . . . . . . . . . . . . . . . . . . . 8
6. Location of Complexity . . . . . . . . . . . . . . . . . . . 8
6.1. Topological Location . . . . . . . . . . . . . . . . . . 8
6.2. Logical Location . . . . . . . . . . . . . . . . . . . . 8
6.3. Layering Considerations . . . . . . . . . . . . . . . . . 8
7. Dependencies . . . . . . . . . . . . . . . . . . . . . . . . 8
7.1. Local Dependencies . . . . . . . . . . . . . . . . . . . 9
7.2. Network Wide Dependencies . . . . . . . . . . . . . . . . 9
7.3. Network External Dependencies . . . . . . . . . . . . . . 9
8. Management Interactions . . . . . . . . . . . . . . . . . . . 9
8.1. Configuration Complexity . . . . . . . . . . . . . . . . 9
8.2. Troubleshooting Complexity . . . . . . . . . . . . . . . 9
8.3. Monitoring Complexity . . . . . . . . . . . . . . . . . . 9
8.4. Complexity of System Integration . . . . . . . . . . . . 9
9. External Interactions . . . . . . . . . . . . . . . . . . . . 10
9.1. User Interactions . . . . . . . . . . . . . . . . . . . . 10
9.2. Interactions on End Systems . . . . . . . . . . . . . . . 10
9.3. Inter-Network Interactions . . . . . . . . . . . . . . . 10
10. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . 10
11. Security Considerations . . . . . . . . . . . . . . . . . . . 10
12. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 11
13. Informative References . . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
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1. Introduction
During the design phase of a network complexity plays a key role.
Network designers generally seek to find the simplest design that
fulfils a set of requirements. As no objective definition of network
complexity exists, subjective measures are used to come to a
conclusion. The resulting diverging views on what constitutes
complexity subsequently lead to conflicts in design teams. While
most people would agree that complexity is an important factor in
network design, today's design decisions are made based on a rough
estimation of the network's complexity, rather than a solid
understanding.
The goal of this document is to define a framework for network
complexity research. This framework describes related research and
current understanding of the topic, as well as outlining some ways
research could be taken forward. Specifically, contributions are
invited in all of the areas mentioned.
Many references to existing research in the area of network
complexity are listed on the Network Complexity Wiki [wiki]. This
wiki also contains background information on previous meetings on the
subject, previous research, etc.
2. General Considerations
2.1. The Behavior of a Complex Network
While there is no generally accepted definition of network
complexity, there is some understanding of the behavior of a complex
network. It has some or all of the following properties:
o Self-Organization: A network runs some protocols and processes
without external control; for example a routing process, failover
mechanisms, etc. The interaction of those mechanisms can lead to
a complex behaviour.
o Un-predictability: In a complex network, the effect of a local
change on the behaviour of the global network may be
unpredictable.
o Emergence: A network has an emergent property if a small local
change produces a large scale, seemingly unrelated state or
result.
o Non-linearity: An input into the network produces a non-linear
result.
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o Fragility: A small local input can break the entire system.
2.2. Robust Yet Fragile
Networks typically follow the "robust yet fragile" paradigm: They are
designed to be robust against a set of failures, yet they are very
vulnerable to other failures. Doyle [Doyle] explains the concept
with an example: The Internet is robust against single component
failure, but fragile to targeted attacks. The "robust yet fragile"
property also touches on the fact that all network designs are
necessarily making trade-offs between different design goals. The
simplest one is articulated in "The Twelve Networking Truths" RFC1925
[RFC1925]: "Good, Fast, Cheap: Pick any two (you can't have all
three)." In real network design, trade-offs between many aspects
have to be made, including, for example, issues of scope, time and
cost in the network cycle of planning, design, implementation and
management of a network platform. Tradeoff between varoius
parameters are discussed in section 3.
2.3. The Complexity Cube
Complex tasks on a network can be done in different components of the
network. For example, routing can be controlled by central
algorithms, and the result distributed (e.g., OpenFlow model); the
routing algorithm can also run completely distributed (e.g., routing
protocols such as OSPF or ISIS), or a human operator could calculate
routing tables and statically configure routing. Behringer
[Behringer] defines these three axes of complexity as a "complexity
cube" with three axes: Network elements, central systems, and human
operators. While different functions can be shifted between these
axes of the network, the overall complexity may change.
2.4. Related Concepts
When discussing network complexity, a large number of influencing
factors have to be taken into account to arrive at a full picture,
for example:
o State in the network: Contains the network elements, such as
routers, switches (with their OS, including protocols), lines,
central systems, etc. The number and algorithmical complexity of
the protocols on network devices for example.
o Human operators: Complexity manifests itself often by a network
that is not completely understood by human operators. Human error
is a primary source for catastrophic failures, and therefore must
be taken into account.
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o Classes / templates: Rather than counting the number of lines in a
configuration, or the number of hardware elements, more important
is the number of classes from which those can be derived. In
other words, it is probably less complex to have 1000 interfaces
which are identically configured than 5 that are completely
different configured.
o Dependencies and interactions: The number of dependencies between
elements, as well as the interactions between them has influence
on the complexity of the network.
o TCO (Total cost of ownership): TCO could be a good metric for
network complexity, if the TCO calculation takes into accont all
influencing factors, for example training time for staff to be
able to maintain a network.
o Benchmark Unit Cost is a related metric that indicates the cost of
operating a certain component. If calculated well, it reflects at
least parts of the complexity of this component. Therefore, the
way TCO or BUC are calculated can help to derive a complexity
metric.
o Churn / rate of change: The change rate in a network itself can
contribute to complexity, especially if a number of components of
the overall network interact.
Networks differ in terms of their intended purpose (such as is found
in differences between enterprise and public carriage network
platforms, and in their intended role (such as is found in the
diferences between so-called "access" networks and "core" transit
networks). The differences in terms of role and purpose can often
lead to differences in the tolerance for, and even the metrics of,
complexity within such different network scenarios. This is not
necessarily a space where a single methodology for measuring
complexity, and defining a single threshold value of acceptability of
complexity, is appropriate.
2.5. Technical Debt
Many changes in a network are made with a dependency on the existing
network. Often, a suboptimal decision is made because the optimal
decision is hard or impossible to realise at the time. Over time,
the number of suboptimal changes in themselves cause significant
complexity, which would not have been there had the optimal solution
been implemented.
The term "technical debt" refers to the accumulated complexity of
sub-optimal changes over time. As with financial debt, the idea is
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that also technical debt must be repaid one day by cleaning up the
network or software.
2.6. Layering considerations
In considering the larger space of applications, transport services,
network services and media services, it is feasible to engineer
responses for certain types of desired applications responses in many
different ways, and involving different layers of the so-called
network protocol stack. For example, quality of Service could be
engineered at any of these layers, or even in a number of
combinations of different layers.
Considerations of complexity arise when mutually incompatible
measures are used in combination (such as error detection and
retransmission at the media layer in conjunction with the use TCP
transport protocol), or when assumptions used in one layer are
violated by another layer. This results in surprising outcomes that
may result in complex interactions. This has lead to the perspective
that increased layering frequently increases complexity [RFC3439].
While this research work is focussed network complexity, the
interactions of the network with the end-to-end transport protocols,
application layer protocols and media properties are relevant
considerations here.
3. Tradeoffs
>[I-D.irtf-ncrg-network-design-complexity] describes a set of trade-
offs in network design to illustrate the practical choices network
operators have to make. The amount of parameters to consider in such
tradeoff scenarios is very large, thus that a complete listing may
not be possible. Also the dependencies between the various metrics
itself is very complex and requires further study. This document
attempts to define a methodology and an overall high level structure.
To analyse tradeoffs it is necessary to formalise them. The list of
parameters for such tradeoffs is long, and the parameters can be
complex in themselves. For example, "cost" can be a simple
unidimensional metric, but "extensibility" or "optimal forwarding
state" are harder to define in detail.
A list of parameters to trade off contains metrics such as:
o Cost: How much does the network cost to build (capex) and run
(opex)
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o Bandwidth / delay / jitter: Traffic characteristics between two
points (average, max, ...)
o Configuration complexity: How hard to configure and maintain the
configuration
o Susceptibility to Denial-of-Service: How easy is it to attack the
service
o Security (confidentiality / integrity): How easy is it to sniff /
modify / insert the data flow
o Scalability: To what size can I grow the network / service
o Extensibility: Can I use the network for other services in the
future?
o Ease of troubleshooting: How hard is it to find and correct
problems?
o Predictability: If I change a parameter, what will happen?
o Clean failure: When a problem arises, does the root cause lead to
deterministic failure
The list of the above criteria can be seen as forming an
n-dimensional design space, where each network is represented in one
intersection of all parameters.
4. Structural Complexity
tbc
5. Components of Complexity
Complexity can be found in various components of a networked system.
For example, the configuration of a network element reflects some of
the complexity contained in this system. Or an algorithm used by a
protocol may be more or less complex. When classifying complexity
the first question to ask is "WHAT is complex?". This section offers
a method to answer this question.
5.1. The Physical Network (Hardware)
tbc
5.2. State in the Network
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tbc
5.3. Churn
The frequency of chance in a network intuitively contributes to its
complexity: A network which is not subjected to change tends to be
more stable [need ref here]. While there is permanently a certain
base complexity in the network, this complexity is "under control"
and does not lead to negative side effects.
[I-D.sircar-complexity-entropy] describes how entropy metrics can be
used to describe changing complexity in a network. The fundamental
thesis is that change itself constitutes complexity. When a network
undergoes change, the network entropy and the complextiy increases.
This is also true when the change has simplification as a goal. The
entropy increases during change, and decreases in periods of
stability. It can therefore be used to measure the impact of change
on complexity.
5.4. Algorithms
tbc
6. Location of Complexity
The previous section discussed in which form complexity may be
perceived. This section focuses on where this complexity is located
in a network. For example, an algorithm can run centrally,
distributed, or even in the head of a network administrator. In
classifying the complexity of a network, the location of a component
may have an impact on overall complexity. This section offers a
methodology to the question "WHERE is the complex component?"
6.1. Topological Location
tbc
6.2. Logical Location
tbc
6.3. Layering Considerations
tbc
7. Dependencies
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Dependencies are generally regarded as related to overall complexity.
A system with less dependencies is generally considered less complex.
This section proposes a way to analyse dependencies in a network.
For example, [Chun] states: "We conjecture that the complexity
particular to networked systems arises from the need to ensure state
is kept in sync with its distributed dependencies."
In this document we distinguish three types of dependencis: Local
dependencies, network wide dependencies, and network external
dependencies.
7.1. Local Dependencies
tbc
7.2. Network Wide Dependencies
tbc
7.3. Network External Dependencies
tbc
8. Management Interactions
A static network generally is relatively stable; conversely, changes
introduce a degree of uncertainty and therefore need to be examined
in detail. Also, the trouble shooting of a network exposes
intuitively the complexity of the network. This section proposes a
methodology to classify management interactions with regard to their
relationship to network complexity.
8.1. Configuration Complexity
tbc
8.2. Troubleshooting Complexity
tbc
8.3. Monitoring Complexity
tbc
8.4. Complexity of System Integration
tbc
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9. External Interactions
The user experience of a network also illustrates a form of
complexity. A network can expose certain tasks to the user, or deal
with them internally, hidden to the user. This section describes how
user interactions can be analysed to expose complexity.
9.1. User Interactions
tbc
9.2. Interactions on End Systems
tbc
9.3. Inter-Network Interactions
tbc
10. Examples
In the foreseeable future it is unlikely to define a single,
objective metric that includes all the relevant aspects of
complexity. In the absence of such a global metric, a comparative
approach could be easier.
For example, it is possible to compare the complexity of a
centralised systems where algorithms run centrally, and the results
are distributed to the network nodes with a distributed algorithm.
The type of algorithm may be similar, but the location is different,
and a different dependency graph would result. The supporting
hardware may be the same, thus could be ignored for this exercise.
Also layering is likely to be the same. The management interactions
though would significantly differ in both cases.
The classification in this document also makes it easier to survey
existing research with regards to which area of complexity is
covered. This could help in identifying open areas for research.
11. Security Considerations
This document does not discuss any specific security considerations.
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12. Acknowledgements
The motivations and framework of this overview of studies into
network complexity is the result of many meetings and discussions,
with too many people to provide a full list here. However, key
contributions have been made by: John Doyle, Jon Crowcroft, Mark
Handley, Fred Baker, Paul Vixie, Lars Eggert, Bob Briscoe, Keith
Jones, Bruno Klauser, Steve Youell, Joel Obstfeld.
The authors would like to acknowledge the contributions of Rana
Sircar, Ken Carlberg and Luca Caviglione in the preparation of this
Research Group document.
13. Informative References
[Behringer]
Behringer, M., "Classifying Network Complexity",
Proceedings of the ACM Re-Arch'09, December 2009.
[Chun] Chun, B-G., Ratnasamy, S., and E. Eddie, "NetComplex: A
Complexity Metric for Networked System Design", 5th Usenix
Symposium on Networked Systems Design and Implementation
NSDI 2008, April 2008,
<http://berkeley.intel-research.net/sylvia/netcomp.pdf>.
[Doyle] Doyle, J., "The 'robust yet fragile' nature of the
Internet", PNAS vol. 102 no. 41 14497-14502, October 2005.
[I-D.irtf-ncrg-network-design-complexity]
Retana, A. and R. White, "Network Design Complexity
Measurement and Tradeoffs", draft-irtf-ncrg-network-
design-complexity-00 (work in progress), August 2013.
[I-D.sircar-complexity-entropy]
Sircar, R. and M. Behringer, "Using Entropy as a Measure
for Changes in Network Complexity", draft-sircar-
complexity-entropy-00 (work in progress), October 2013.
[RFC1925] Callon, R., "The Twelve Networking Truths", RFC 1925,
April 1996.
[RFC3439] Bush, R. and D. Meyer, "Some Internet Architectural
Guidelines and Philosophy", RFC 3439, December 2002.
[wiki] , "Network Complexity Wiki", ,
<http://networkcomplexity.org/>.
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Authors' Addresses
Michael H. Behringer
Cisco
Email: mbehring@cisco.com
Geoff Huston
Asia Pacific Network Information Centre
Email: gih@apnic.net
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