Internet DRAFT - draft-retana-network-complexity-framework
draft-retana-network-complexity-framework
Network Working Group A. Retana
Internet-Draft Cisco Systems
Intended status: Standards Track R. White
Expires: September 15, 2013 Verisign
March 14, 2013
A Framework for Measuring Network Complexity
draft-retana-network-complexity-framework-00.txt
Abstract
Network architecture revolves around the concept of fitting the
design of a network to its purpose; of asking the question, "what
network will best fit these needs?" A part of fitting network design
to requirements is the problem of complexity, an idea often measured
by "seat of pants" methods. When would adding a particular protocol,
policy, or configuration be "too complex?" This document suggests a
series of continuums along which network complexity might be
measured. No suggestions are made in measuring complexity for each
of these continuums are provided; this is left for future documents.
Status of This Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Requirements notation . . . . . . . . . . . . . . . . . . . . 3
3. Control Plane State verses Optimal Forwarding Paths (Stretch) 3
4. Configuration State verses Failure Domain Separation . . . . 4
5. Policy Centralization verses Optimal Policy Application . . . 6
6. Configuration State verses Per Hop Forwarding Optimization . 7
7. Reactivity verses Stability . . . . . . . . . . . . . . . . . 7
8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 9
9. Security Considerations . . . . . . . . . . . . . . . . . . . 9
10. Normative References . . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
Network complexity is a systemic, rather than component level,
problem; complexity must be measured in terms of the multiple moving
parts of a system, and complexity may be more than the complexity of
the individual pieces, examined individually, might suggest. There
are two basic ways in which systemic level problems might be
addressed: interfaces and continuums. In addressing a systemic
problem through interfaces, we seek to treat each piece of the system
as a "black box," and develop a complete understanding of the
interfaces between these black boxes. In address a systemic problem
as a continuum, we seek to understand the impact of a single change
or element to the entire system as a set of tradeoffs. While network
complexity can profitably be approached from either of these
perspectives, the authors of this document have chosen to approach
the systemic impacts of network complexity from the perspective of
continuums of tradeoffs. In theory, modifying the network to resolve
one particular problem (or class of problems) will add complexity
which results in the increased liklihood (or appearance) of another
class of problems. Discovering these continuums of tradeoffs, and
then determining how to measure each one, become the key steps in
understanding and measuring systemic complexity in this view.
This document proposes five such continuums; more may be possible.
Others may be added into this document in future revisions, or
documented in other drafts, as circumstances dictate.
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o Control Plane State verses Optimal Forwarding Paths (or it's
opposite measure, stretch)
o Configuration State verses Failure Domain Separation
o Policy Centralization verses Optimal Policy Application
o Configuration State verses Per Hop Forwarding Optimization
o Reactivity verses Stability
Each of these continuums is described in a separate section of this
draft.
2. Requirements notation
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", "OPTIONAL" in this
document are to be interpreted as in [RFC2119].
3. Control Plane State verses Optimal Forwarding Paths (Stretch)
Control plane state is the aggregate amount of information carried by
the control plane through the network in order to produce the
forwarding table at each device. Each additional piece of
information added to the control plane --such as more specific
reachability information, policy information, additional control
planes for virtualization and tunneling, or more precise topology
information-- adds to the complexity of the control plane. This
added complexity, in turn, adds to the burden of monitoring,
understanding, troubleshooting, and managing the network. Removing
control plane state, however, is not always a net positive gain for
the network as a system; removing control plane state almost always
results in decreased optimality in the forwarding and handing of
packets travelling through the network. This decreased optimality
can be termed stretch, which is defined as the difference between the
absolute shortest (or best) path traffic could take through the
network and the path the traffic actually takes through the network.
Stretch is expressed as the difference between the optimal and actual
path. The figure below provides and example of this tradeoff.
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R1-------+
| |
R2 R3
| |
R4-------R5
|
R6
Assume each link is of equal cost in this figure, and:
o R4 is advertising 192.0.2.1/32 as a reachable destination not
shown on the diagram
o R5 is advertising 192.0.2.2/32 as a reachable destination not
shown on the diagram
o R6 is advertising 192.0.2.3/32 as a reachable destination not
shown on the diagram
For R1, the shortest path to 192.0.2.3/32, advertised by R6, is along
the path [R1,R2,R4,R6]. Assume, however, the network administrator
decides to aggregate reachability information at R2 and R3,
advertising 192.0.2.0/24 towards R1 from both of these points. This
reduces the overall complexity of the control plane by reducing the
amount of information carried past these two routers (at R1 only in
this case). Aggregating reachability information at R2 and R3,
however, has the impact of making both routes towards 192.168.2.3/32
appear as equal cost paths to R1; there is no particular reason R1
should choose the shortest path through R2 over the longer path
through R3. This, in effect, increases the stretch of the network.
The shortest path from R1 to R6 is 3 hops, a path that will always be
chosen before aggregation is configured. Assuming half of the
traffic will be forwarded along the path through R2 (3 hops), and
half through R3 (4 hops), the network is stretched by ((3+4)/2) - 3),
or .5, a "half a hop."
Traffic engineering through various tunneling mechanisms is, at a
broad level, adding control plane state to provide more optimal
forwarding (or network utlization). Optimizing network utilization
may require detuning stretch (intentionally increasing stretch) to
increase overall network utilization and efficiency; this is simply
an alternate instance of control plane state (and hence complexity)
weighed against optimal forwarding through the network.
4. Configuration State verses Failure Domain Separation
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A failure domain, within the context of a network control plane, can
be defined as the set of devices impacted by a change in the network
topology or configuration. A network with larger failure domains is
more prone to cascading failures, so smaller failure domains are
normally preferred over larger ones. The primary manes used to limit
the size of a failure domain within a network's control plane is
information hiding; the two primary types of information hidden in a
network control plane are reachability information and topology
information. An example of aggregating reachability information is
summarizing the routes 192.0.2.1/32, 192.0.2.2/32, and 192.0.2.3/32
into the single route 192.0.2.0/24, along with the aggregation of the
metric information associated with each of the component routes.
Note that aggregation is a "natural" part of IP networks, starting
with the aggregation of individual hosts into a subnet at the network
edge. An example of topology aggregation is the summarization of
routes at a link state flooding domain boundary, or the complete
failure to advertise topology information in a distance-vector
protocol.
While limiting the size of failure domains appears to be an absolute
good in terms of network complexity, there is a definite tradeoff in
configuration complexity. The more failure domain edges created in a
network, the more complex configuration will become. This is
particularly true is redistribution of routing information between
multiple control plane processes is used to create failure domain
boundaries; moving between different types of control planes causes a
loss of the consistent metrics most control planes rely on to build
loop free paths. Redistribution, in particular, opens the door to
very destructive positive feedback looks within the control plane.
Examples of control plane complexity caused by the creation of
failure domain boundaries include route filters, routing aggregation
configuration, and metric modifications to engineer traffic across
failure domain boundaries.
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Returning to the network described in the previous section,
aggregating routing information at R2 and R3 will divide the network
into two failure domains: (R1,R2,R3), and (R2,R3,R4,R5). A failure
at R5 should have no impact on the forwarding information at R1. A
false failure domain separation occurs, however, when the metric of
the aggregate route advertised by R2 and R3 is dependent on one of
the routes within the aggregate. For instance, if the metric of the
192.0.2.0/24 aggregate is taken from the metric of the component
192.0.2.1/32, then a failure of this one component will cause changes
in the forwarding table at R1 --in this case, the control plane has
not truly been separated into two distinct failure domains. The
added complexity in the illustration network would be the management
of the configuration required to aggregate the contorl plane
information, and the management of the metrics to ensure the control
plane is truly separated into two distinct failure domains.
Replacing aggregation with redistribution adds the complexity of
managing the feedback of routing information redistributed between
the failure domains. For instance, if R1, R2, and R3 were configured
to run one routing protocol, while R2, R3, R4, R5, and R6 were
configured to run another protocol, R2 and R3 could be configured to
redistribute reachability information between these two control
planes. This can split the control plane into multiple failure
domains (depending on how, specifically, redistribution is
configured), but at the cost of creating and managing the
redistribution configuration. Futher, R3 must be configured to block
routing information redistributed at R2 towards R1 from being
redistributined (again) towards R4 and R5.
5. Policy Centralization verses Optimal Policy Application
Another broad area where control plane complexity interacts with
optimal network utilization is Quality of Service (QoS). Two
specific actions are required to optimize the flow of traffic through
a network: marking and Per Hop Behaviors (PHBs). Rather than
examining each packet at each forwarding device in a network, packets
are often marked, or classified, in some way (typically through Type
of Service bits) so they can be handled consistently at all
forwarding devices. Packet marking policies must be configured on
specific forwarding devices throughout the network. Distributing
marking closer to the edge of the network necessarily means
configuring and managing more devices, but produces optimal
forwarding at a larger number of network devices. Moving marking
towards the network core means packets are marked for proper handling
across a smaller number of devices. In the same way, each device
through which a packet passes with the correct PHBs configured
represents an increase in the consistency in packet handling through
the network as well as an increase in the number of devices which
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must be configured and managed for the correct PHBs. The network
below is used for an illustration of this concept.
+----R1----+
| |
+--R2--+ +--R3--+
| | | |
R4 R5 R6 R7
In this network, marking and PHB configuration may be configured on
any device, R1 through R7. Assume marking is configured at the
network edge; in this case, four devices, (R4,R5,R6,R7), must be
configured, including ongoing configuration management, to mark
packets. Moving packet marking to R2 and R3 will halve the number of
devices on which packet marking configuration must be managed, but at
the cost of consistent packet handling at the inbound interfaces of
R2 and R3 themselves. Thus reducing the number of devices which must
have managed configurations for packet marking will reduce optimal
packet flow through the network. Assuming packet marking is actually
configured along the edge of this network, configuring PHBs on
different devices has this same tradeoff of managed configuration
verses optimal traffic flow. If the correct PHBs are configured on
R1, R2, and R3, then packets passing through the network will be
handled correctly at each hop. The cost involved will be the
management of PHB configuration on three devices. Configuring a
single device for the correct PHBs (R1, for instance), will decrease
the amount of configuration management required, at the cost of less
than optimal packet handling along the entire path.
6. Configuration State verses Per Hop Forwarding Optimization
The number of PHBs configured along a forwarding path exhibits the
same complexity verses optimality tradeoff described in the section
above. The more types of service (or queues) traffic is divided
into, the more optimally traffic will be managed as it passes through
the network. At the same time, each class of service must be
managed, both in terms of configuration and in its interaction with
other classes of service configured in the network.
7. Reactivity verses Stability
The speed at which the network's control plane can react to a change
in configuration or topology is an area of widespread study. Control
plane convergence can be broken down into four essential parts:
o Detecting the change
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o Propagating information about the change
o Determining the best path(s) through the network after the change
o Changing the forwarding path at each network element along the
modified paths
Each of these areas can be addressed in an effort to improve network
convergence speeds; some of these improvements come at the cost of
increased complexity.
Changes in network topology can be detected much more quickly through
faster echo (or hello) mechanisms, lower layer physical detection,
and other methods. Each of these mechanisms, however, can only be
used at the cost of evaluating and managing false positives and high
rates of topology change. If the state of a link change can be
detected in 10ms, for instance, the link could theoretically change
state 50 times in a second --it would be impossible to tune a network
control plane to react to topology changes at this rate. Injecting
topology change information into the control plane at this rate can
destabalize the control plane, and hence the network itself. To
counter this, most fast down detection techniques include some form
of dampening mechanism; configuring and managing these dampening
mechanisms represents an added complexity that must be configured and
managed.
Changes in network topology must also be propagated throughout the
network, so each device along the path can compute new forwarding
tables. In high speed network environments, propagation of routing
information changes can take place in tens of milliseconds, opening
the possibility of multiple changes being propagated per second.
Injecting information at this rate into the contral plane creates the
risk of overloading the processes and devices participating in the
control plane, as well as creating destructive positive feedback
loops in the network. To avoid these consequences, most control
plane protocols regulate the speed at which information about network
changes can be transmitted by any individual device. A recent
innovation in this area is using exponential backoff techniques to
manage the rate at which information is injected into the control
plane; the first change is transmitted quickly, while subsequent
changes are transmitted more slowly. These techniques all control
the destabalilzing effects of rapid information flows through the
control plane through the added complexity of configuring and
managing the rate at which the control plane can propagate
information about network changes.
All control planes require some form of algorithmic calculation to
find the best path through the network to any given destination.
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These algorithms are often lightweight, but they still require some
amount of memory and computational power to execute. Rapid changes
in the network can overwhelm the devices on which these algorithms
run, particularly if changes are presented more quickly than the
algorithm can run. Once the devices running these algorithms become
processor or memory bound, it could experience a computational
failure altogether, causing a more general network outage. To
prevent computational overloading, control plane protocols are
designed with timers limiting how often they can compute the best
path through a network; often these timers are exponential in nature,
allowing the first computation to run quickly, while delaying
subsequent computations. Configuring and managing these timers is
another source of complexity within the network.
Another option to improve the speed at which the control plane reacts
to changes in the network is to precompute alternate paths at each
device, and possibly preinstall forwarding information into local
forwarding tables. Additional state is often needed to precompute
alternate paths, and additional algorithms and techniques are often
configured and deployed. This additional state, and these additional
algorithms, add some amount of complexity to the configuration and
management of the network. In some situations (for some topologies),
a tunnel is required to pass traffic around a network failure or
topology change. These tunnels, while not manually configured,
represent additional complexity at the forwarding and control planes.
8. Conclusion
This document describes various areas of network and design where
complexity is traded off against some optimization in the operation
of the network. This is (by it's nature) not an exhaustive list, but
it can serve to guide the measurement of network complexity and the
search for other areas where these tradeoffs exist.
9. Security Considerations
None.
10. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
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Authors' Addresses
Alvaro Retana
Cisco Systems
2610 Wycliff Road
Raleigh, NC 27607
USA
Email: aretana@cisco.com
Russ White
Verisign
12061 Bluemont Way
Reston, VA 20190
USA
Email: russw@riw.us
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