Internet DRAFT - draft-behringer-complexity-framework
draft-behringer-complexity-framework
Internet Research Task Force M. Behringer
Internet-Draft Cisco
Intended status: Informational G. Huston
Expires: April 18, 2013 Asia Pacific Network Information
Centre
October 15, 2012
A Framework for Defining Network Complexity
draft-behringer-complexity-framework-00.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, but most of them 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
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at http://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on April 18, 2013.
Copyright Notice
Copyright (c) 2012 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(http://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
Behringer & Huston Expires April 18, 2013 [Page 1]
Internet-Draft Complexity Framework October 2012
carefully, as they describe your rights and restrictions with respect
to this document.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Current Understanding of Network Complexity . . . . . . . . . . 3
2.1. The Behavior of a Complex Network . . . . . . . . . . . . . 3
2.2. Robust Yet Fragile . . . . . . . . . . . . . . . . . . . . 4
2.3. The Complexity Cube . . . . . . . . . . . . . . . . . . . . 4
3. Towards Defining Network Complexity . . . . . . . . . . . . . . 4
3.1. General Observations . . . . . . . . . . . . . . . . . . . 4
3.2. The Problem Space . . . . . . . . . . . . . . . . . . . . . 4
3.3. Technical Debt . . . . . . . . . . . . . . . . . . . . . . 5
4. Possible Directions of Research . . . . . . . . . . . . . . . . 5
4.1. Definitions and Metrics . . . . . . . . . . . . . . . . . . 6
4.2. Comparative Analysis . . . . . . . . . . . . . . . . . . . 6
4.3. Containment, Control or Reduction of Complexity . . . . . . 6
4.4. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . 6
5. Security Considerations . . . . . . . . . . . . . . . . . . . . 7
6. Informative References . . . . . . . . . . . . . . . . . . . . 7
Appendix A. Acknowledgements . . . . . . . . . . . . . . . . . . . 7
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 7
Behringer & Huston Expires April 18, 2013 [Page 2]
Internet-Draft Complexity Framework October 2012
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]. That
wiki also contains background information on previous meetings on the
subject, previous research, etc.
2. Current Understanding of Network Complexity
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.
o Fragility: A small local input can break the entire system.
Behringer & Huston Expires April 18, 2013 [Page 3]
Internet-Draft Complexity Framework October 2012
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.
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.
3. Towards Defining Network Complexity
3.1. General Observations
Any analysis of practical network complexity must take a wide range
of parameters into account, also parameters which are hard to
measure, for example the human element. Human error constitutes in
most cases of critical outages the trigger condition; therefore any
analysis ignoring the human factor cannot address the full picture.
[insert a reference that 70%(?) of critical outages have a human
origin]
3.2. The Problem Space
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
Behringer & Huston Expires April 18, 2013 [Page 4]
Internet-Draft Complexity Framework October 2012
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.
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.
3.3. 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
that also technical debt must be repaid one day by cleaning up the
network or software.
4. Possible Directions of Research
The problem space of network complexity is very large, as many
influencing factors contribute to the overall complexity of a
network. The following sections outline areas for research.
Behringer & Huston Expires April 18, 2013 [Page 5]
Internet-Draft Complexity Framework October 2012
4.1. Definitions and Metrics
In the context of general network operations, as well as in the
context of standardisation of protocols a common definition of the
term "network complexity" would be useful. It would also be useful
to have a metric for the complexity of a protocol or network design,
such that two candidate proposals can be objectively compared.This
could happen in a bottom-up approach, where metrics for parts of a
network are combined to an overall metric; or in a top-down approach
where a global metric or vector of metrics is broken down into the
components of a network.
4.2. Comparative Analysis
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, if two network architectures are compared against each
other, it may be possible to ignore the network layout and device
hardware if those are the same in both cases. In such specific
comparisons it should be considerably easier to find valid metrics,
and to compare the approaches objectively.
4.3. Containment, Control or Reduction of Complexity
In some disciplines such as software engineering, complexity is
relatively well understood, as well as metrics and methods to reduce
it. Such approaches can be applied in the networking industry to
achieve the same result.
4.4. Use Cases
While it is hard to define a universal set of metrics for network
complexity, special use cases should be documented to serve as
examples, and to stimulate discussion. Such use cases could come out
of different areas:
o Documented examples of "catastrophic failure": While the cause of
complexity is hard to understand, the result may be a catastropic
outage, which can be reverse-engineered to understand the root
causes. The knowledge from this process may give insight into
root causes of complexity.
o A detailed complexity analysis of a particular network or
protocol. Even if this analysis may not be complete or fully
objective, it would be useful to learn about different approaches.
Behringer & Huston Expires April 18, 2013 [Page 6]
Internet-Draft Complexity Framework October 2012
o Analysis of existing networks, protocols or components from an
insider point of view, discussing in detail where the perceived
complexity in the set-up is, and how this could be changed to
reduce complexity.
o Work in related areas, for example a detailled analysis of the
total cost of ownership, and how this could be mapped into a
complexity metric.
5. Security Considerations
This document does not discuss any specific security considerations.
6. Informative References
[Behringer]
Behringer, M., "Classifying Network Complexity",
Proceedings of the ACM Re-Arch'09, December 2009.
[Doyle] Doyle, J., "The 'robust yet fragile' nature of the
Internet", PNAS vol. 102 no. 41 14497-14502, October 2005.
[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/>.
Appendix A. Acknowledgements
This document 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.
Behringer & Huston Expires April 18, 2013 [Page 7]
Internet-Draft Complexity Framework October 2012
Authors' Addresses
Michael H. Behringer
Cisco
Email: mbehring@cisco.com
Geoff Huston
Asia Pacific Network Information Centre
Email: gih@apnic.net
Behringer & Huston Expires April 18, 2013 [Page 8]