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 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 May 07, 2014. Copyright Notice Copyright (c) 2013 IETF Trust and the persons identified as the document authors. All rights reserved. Behringer & Huston Expires May 07, 2014 [Page 1] Internet-Draft Complexity Framework November 2013 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 carefully, as they describe your rights and restrictions with respect to this document. 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 Behringer & Huston Expires May 07, 2014 [Page 2] Internet-Draft Complexity Framework November 2013 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. Behringer & Huston Expires May 07, 2014 [Page 3] Internet-Draft Complexity Framework November 2013 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. Behringer & Huston Expires May 07, 2014 [Page 4] Internet-Draft Complexity Framework November 2013 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 Behringer & Huston Expires May 07, 2014 [Page 5] Internet-Draft Complexity Framework November 2013 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) Behringer & Huston Expires May 07, 2014 [Page 6] Internet-Draft Complexity Framework November 2013 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 Behringer & Huston Expires May 07, 2014 [Page 7] Internet-Draft Complexity Framework November 2013 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 Behringer & Huston Expires May 07, 2014 [Page 8] Internet-Draft Complexity Framework November 2013 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 Behringer & Huston Expires May 07, 2014 [Page 9] Internet-Draft Complexity Framework November 2013 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. Behringer & Huston Expires May 07, 2014 [Page 10] Internet-Draft Complexity Framework November 2013 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, . [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", , . Behringer & Huston Expires May 07, 2014 [Page 11] Internet-Draft Complexity Framework November 2013 Authors' Addresses Michael H. Behringer Cisco Email: mbehring@cisco.com Geoff Huston Asia Pacific Network Information Centre Email: gih@apnic.net Behringer & Huston Expires May 07, 2014 [Page 12]