Internet DRAFT - draft-irtf-nmrg-autonomic-network-definitions
draft-irtf-nmrg-autonomic-network-definitions
Internet Research Task Force M. Behringer
Internet-Draft M. Pritikin
Intended status: Informational S. Bjarnason
Expires: September 24, 2015 A. Clemm
Cisco Systems
B. Carpenter
Univ. of Auckland
S. Jiang
Huawei Technologies Co., Ltd
L. Ciavaglia
Alcatel Lucent
March 23, 2015
Autonomic Networking - Definitions and Design Goals
draft-irtf-nmrg-autonomic-network-definitions-07.txt
Abstract
Autonomic systems were first described in 2001. The fundamental goal
is self-management, including self-configuration, self-optimization,
self-healing and self-protection. This is achieved by an autonomic
function having minimal dependencies on human administrators or
centralized management systems. It usually implies distribution
across network elements.
This document defines common language, and outlines design goals and
non-design goals for autonomic functions. A high level reference
model illustrates how functional elements in an autonomic network
interact.
Status of This Memo
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This Internet-Draft will expire on September 24, 2015.
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Table of Contents
1. Introduction to Autonomic Networking . . . . . . . . . . . . 2
2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Design Goals . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1. Self-Management . . . . . . . . . . . . . . . . . . . . . 5
3.2. Co-Existence with Traditional Management . . . . . . . . 6
3.3. By Default Secure . . . . . . . . . . . . . . . . . . . . 7
3.4. Decentralisation and Distribution . . . . . . . . . . . . 8
3.5. Simplification of Autonomic Node Northbound Interfaces . 8
3.6. Abstraction . . . . . . . . . . . . . . . . . . . . . . . 8
3.7. Autonomic Reporting . . . . . . . . . . . . . . . . . . . 9
3.8. Common Autonomic Networking Infrastructure . . . . . . . 9
3.9. Independence of Function and Layer . . . . . . . . . . . 10
3.10. Full Life Cycle Support . . . . . . . . . . . . . . . . . 10
4. Non Design Goals . . . . . . . . . . . . . . . . . . . . . . 10
4.1. Eliminate human operators . . . . . . . . . . . . . . . . 10
4.2. Eliminate emergency fixes . . . . . . . . . . . . . . . . 11
4.3. Eliminate central control . . . . . . . . . . . . . . . . 11
5. An Autonomic Reference Model . . . . . . . . . . . . . . . . 11
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 13
7. Security Considerations . . . . . . . . . . . . . . . . . . . 13
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 13
9. Informative References . . . . . . . . . . . . . . . . . . . 14
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 15
1. Introduction to Autonomic Networking
Autonomic systems were first described in a manifesto by IBM in 2001
[Kephart]. The fundamental concept involves eliminating external
systems from a system's control loops and closing of control loops
within the autonomic system itself, with the goal of providing the
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system with self-management capabilities, including self-
configuration, self-optimization, self-healing and self-protection.
IP networking was initially designed with similar properties in mind.
An IP network should be distributed and redundant to withstand
outages in any part of the network. Routing protocols such as OSPF
or ISIS exhibit properties of self-management, and can thus be
considered autonomic in the definition of this document.
However, as IP networking evolved, the ever increasing intelligence
of network elements was often not put into protocols to follow this
paradigm, but external configuration systems. This configuration
made network elements dependent on some process that manages them,
either a human, or a network management system.
Autonomic functions can be defined in two ways:
o On a node level: Nodes interact with each other to form feedback
loops.
o On a system level: Feedback loops include central elements as
well.
System level autonomy is implicitly or explicitly the subject in many
IETF working groups, where interactions with controllers or network
management systems are discussed.
This work specifically focuses on node level autonomic functions. It
focuses on intelligence of algorithms at the node level, to minimize
dependency on human administrators and central management systems.
Some network deployments benefit from a fully autonomic approach, for
example networks with a large number of relatively simple devices.
Most of currently deployed networks however will require a mixed
approach, where some functions are autonomic and others are centrally
managed. Central management of networking functions clearly has
advantages and will be chosen for many networking functions. This
document does not discuss which functions should be centralised or
follow an autonomic approach. Instead, it should help make the
decision which is the best approach for a given situation.
Autonomic function cannot always discover all required information;
for example, policy related information requires human input, because
policy is by its nature derived and specified by humans. Where input
from some central intelligence is required, it is provided in a
highly abstract, network wide form.
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Autonomic Computing in general and Autonomic Networking in particular
have been the subject of academic study for many years. There is a
large literature, including several useful overview papers (e.g.,
[Samaan], [Movahedi], and [Dobson]). In the present document we
focus on concepts and definitions that seem sufficiently mature to
become the basis for interoperable specifications in the near future.
In particular, such specifications will need to co-exist with
traditional methods of network configuration and management, rather
than realising an exclusively autonomic system with all the
properties that it would require.
There is an important difference between "automatic" and "autonomic".
"Automatic" refers to a pre-defined process, such as a script.
"Autonomic" is used in the context of self-management. It includes
feedback loops between elements as well as northbound to central
elements. See also the definitions in the next section. Generally,
an automatic process works in a given environment, but has to be
adapted if the environment changes. An autonomic process can adapt
to changing environments.
This document provides the definitions and design goals for Autonomic
Networking in the IETF and IRTF.
2. Definitions
We make the following definitions:
Autonomic: Self-managing (self-configuring, self-protecting, self-
healing, self-optimizing); however, allowing high-level guidance by a
central entity, through Intent (see below). An autonomic function
adapts on its own to a changing environment.
Automatic: A process that occurs without human intervention, with
step-by-step execution of rules. However it relies on humans
defining the sequence of rules, so is not Autonomic in the full
sense. For example, a start-up script is automatic but not
autonomic. An automatic function may need manual adjustments if the
environment changes.
Intent: An abstract, high level policy used to operate the network.
Its scope is an autonomic domain, such as an enterprise network. It
does not contain configuration or information for a specific node
(see Section 3.2 on how Intent co-exists with alternative management
paradigms). It may contain information pertaining to nodes with a
specific role, for example an edge switch, or a node running a
specific function. Intent is typically defined and provided by a
central entity.
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Autonomic Domain: A collection of autonomic nodes that instantiate
the same Intent.
Autonomic Function: A feature or function which requires no
configuration, and can derive all required information either through
self-knowledge, discovery or through Intent.
Autonomic Service Agent: An agent implemented on an autonomic node
which implements an autonomic function, either in part (in the case
of a distributed function) or whole.
Autonomic Node: A node which employs exclusively autonomic functions.
It requires (!) no configuration. (Note that configuration can be
used to override an autonomic function. See Section 3.2 for more
details.) An Autonomic Node may operate on any layer of the
networking stack. Examples are routers, switches, personal
computers, call managers, etc.
Autonomic Network: A network containing exclusively autonomic nodes.
It may contain one or several autonomic domains.
3. Design Goals
This section explains the high level goals of Autonomic Networking,
independent of any specific solutions.
3.1. Self-Management
The original design goals of autonomic systems as described in
[Kephart] also apply to Autonomic Networks. The over-arching goal is
self-management, which is comprised of several self-* properties.
The most commonly cited are:
o Self-configuration: Functions do not require to be configured,
neither by an administrator nor a management system. They
configure themselves, based on self-knowledge, discovery, and
Intent. Discovery is the default way for an autonomic function to
receive the information it needs to operate.
o Self-healing: Autonomic functions adapt on their own to changes in
the environment, and heal problems automatically.
o Self-optimising: Autonomic functions automatically determine ways
to optimise their behaviour against a set of well-defined goals.
o Self-protection: Autonomic functions automatically secure
themselves against potential attacks.
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Almost any network can be described as "self-managing", as long as
the definition of "self" is large enough. For example, a well-
defined SDN system, including the controller elements, can be
described over all as "autonomic", if the controller provides an
interface to the administrator which has the same properties as
mentioned above (high level, network-wide, etc).
For the work in the IETF and IRTF we define the "self" properties on
the node level. It is the design goal to make functions on network
nodes self- managing, in other words, minimally dependent on
management systems or controllers, as well as human operators. Self-
managing functions on a node might need to exchange information with
other nodes in order to achieve this design goal.
As mentioned in the Introduction, closed-loop control is an important
aspect of self-managing systems. This implies peer-to-peer dialogues
between the parties that make up the closed loop. Such dialogues
require two-way "discussion" or "negotiation" between each pair or
groups of peers involved in the loop, so they cannot readily use
typical top-down command-response protocols. Also, a discovery phase
is unavoidable before such closed-loop control can take place.
Multi-party protocols are also possible but can be significantly more
complex.
3.2. Co-Existence with Traditional Management
For the foreseeable future, autonomic nodes and networks will be the
exception; autonomic behaviour will initially be defined function by
function. Therefore, co-existence with other network management
paradigms has to be considered. Examples are management by command
line, SNMP, SDN (with related APIs), NETCONF, etc.
Conflict resolution between autonomic default behaviour and Intent on
one side, and other methods on the other is therefore required. This
is achieved through prioritisation. Generally, autonomic mechanisms
define a network wide behaviour, whereas the alternative methods are
typically on a node by node basis. Node based management concepts
take a higher priority over autonomic methods. This is in line with
current examples of autonomic functions, for example routing: A
(statically configured) route has priority over the routing
algorithm. In short:
o lowest priority: autonomic default behaviour
o medium priority: autonomic Intent
o highest priority: node specific network management concepts, such
as command line, SNMP, SDN, NETCONF, etc. How these concepts are
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prioritised between themselves is outside the scope of this
document.
The above priorisation essentially results in the actions of the
human administrator always being able to over-rule autonomic
behaviour. This is generally the expectation of network operators
today, and remains therefore a design principle here. In critical
systems, such as atomic power plants, sometimes the opposite
philosophy is used: The expectation is that a well defined algorithm
is more reliable than a human operator, especially in rare exception
cases. Networking generally does not follow this philosophy yet.
Warnings however should be issued if node specific overrides may
conflict with autonomic behaviour.
In other fields, autonomic mechanisms disengage automatically if
certain conditions occur: The auto-pilot in a plane switches off if
the plane is outside a pre-defined envelope of flight parameters.
The assumption is that the algorithms only work correctly if the
input values are in expected ranges. Some opinions however suggest
that exactly in exceptional conditions is the worst moment to switch
off autonomic behaviour, since the pilots have no full understanding
of the situation at this point, and may be under high levels of
stress. For this reason we suggest here to NOT generally disable
autonomic functions if they encounter unexpected conditions, because
it is expected that this adds another level of unpredictability in
networks, when the situation may already be hard to understand.
3.3. By Default Secure
All autonomic interactions should be by default secure. This
requires that any member of an autonomic domain can assert its
membership using a domain identity, for example a certificate issued
by a domain certification authority. This domain identity is used
for nodes to learn about their neighbouring nodes, to determine the
boundaries of the domain, and to cryptographically secure
interactions within the domain. Nodes from different domains can
also mutually verify their identity and secure interactions as long
as they have a mutually respected trust anchor.
A strong, cryptographically verifiable domain identity is a
fundamental cornerstone in Autonomic Networking. It can be leveraged
to secure all communications, and allows thus automatic security
without traditional configuration, for example pre-shared keys.
Autonomic functions must be able to adapt their behaviour depending
on the domain of the node they are interacting with.
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3.4. Decentralisation and Distribution
The goal of Autonomic Networking is to minimise dependencies on
central elements; therefore, de-centralisation and distribution are
fundamental to the concept. If a problem can be solved in a
distributed manner, it should not be centralised.
In certain cases it is today operationally preferable to keep a
central repository of information, for example a user database on a
AAA server. An autonomic network should be able to use such central
systems, in order to be deployable. It is possible to distribute
such databases as well, and such efforts should be at least
considered. Depending on the case, distribution may not be simple
replication, but involve more complex interactions and organisation.
3.5. Simplification of Autonomic Node Northbound Interfaces
Even in a decentralised solution, certain information flows with
central entities are required. Examples are high level service
definitions, as well as network status requests, audit information,
logging and aggregated reporting.
Therefore, also nodes in an autonomic network require a northbound
interface. However, the design goal is to maintain this interface as
simple and high level as possible.
3.6. Abstraction
An administrator or autonomic management system interacts with an
autonomic network on a high level of abstraction. Intent is defined
at a level of abstraction that is much higher than that of typical
configuration parameters, for example, "optimize my network for
energy efficiency". Intent must not be used to convey low-level
commands or concepts, since those are on a different abstraction
level.
For example, the administrator should not be exposed to the version
of the IP protocol running in the network.
Also on the reporting and feedback side an autonomic network
abstracts information and provides high-level messages such as "the
link between node x and y is down" (possibly with an identifier for
the specific link, in case of multiple links).
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3.7. Autonomic Reporting
An autonomic network, while minimizing the need for user
intervention, still needs to provide users with visibility like in
traditional networks. However, in an autonomic network, reporting
should happen on a network wide basis. Information about the network
should be collected and aggregated by the network itself, presented
in consolidated fashion to the administrator.
The layers of abstraction that are provided via Intent need to be
supported for reporting functions as well, in order to give users an
indication about the effectiveness of their intent. For example, in
order to assess how effective the network performs with regards to
the Intent "optimize my network for energy efficiency", the network
should provide aggregate information about the number of ports that
were able to be shut down, and the corresponding energy savings,
while validating current service levels are on aggregate still met.
Autonomic network events should concern the autonomic network as a
whole, not individual systems in isolation. For example, the same
failure symptom should not be reported from every system that
observes it, but only once for the autonomic network as a whole.
Ultimately, the autonomic network should support exception based
management, in which only events that truly require user attention
are actually notified. This requires capabilities that allow systems
within the network to compare information and apply specific
algorithms to determine what should be reported.
3.8. Common Autonomic Networking Infrastructure
[I-D.irtf-nmrg-an-gap-analysis] points out that there are already a
number of autonomic functions available today. However, these are
largely independent, and each has its own methods and protocols to
communicate, discover, define and distribute policy, etc.
The goal of the work on Autonomic Networking in the IETF is therefore
not just to create autonomic functions, but to define a common
infrastructure that autonomic functions can use. This Autonomic
Networking Infrastructure may contain common control and management
functions such as messaging, service discovery, negotiation, Intent
distribution, self-monitoring and diagnostics, etc. A common
approach to define and manage Intent is also required.
Refer to the reference model below: All the components around the
"autonomic service agents" should be common components, such that the
autonomic service agents do not have to replicate common tasks
individually.
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3.9. Independence of Function and Layer
Autonomic functions may reside on any layer in the networking stack.
For example, layer 2 switching today is already relatively autonomic
in many environments, since most switches can be plugged together in
many ways and will automatically build a simple layer 2 topology.
Routing functions run on a higher layer and can be autonomic on layer
3. Even application layer functionality such as unified
communications can be autonomic.
"Autonomic" in the context of this framework is a property of a
function which is implemented on a node. Autonomic functions can be
implemented on any node type, for example a switch, router, server,
or call manager.
An Autonomic Network requires an overall control plane for autonomic
nodes to communicate. As in general IP networking, IP is the
spanning layer that binds all those elements together; autonomic
functions in the context of this framework should therefore operate
at the IP layer. This concerns neighbour discovery protocols and
other Autonomic Control Plane functions.
3.10. Full Life Cycle Support
An autonomic function does not depend on external input to operate;
it needs to understand its current situation and surrounding, and
operate according to its current state. Therefore, an autonomic
function must understand the full life cycle of the device it runs
on, from first manufacturing testing through deployment, testing,
troubleshooting, up to decommissioning.
The state of the life-cycle of an autonomic node is reflected in a
state model. The behaviour of an autonomic function may be different
for different deployment states.
4. Non Design Goals
This section identifies various items that are explicitly not design
goals in the IETF/IRTF for autonomic networks, which are mentioned to
avoid misunderstandings of the general intention. They address some
commonly expressed concerns from network administrators and
architects.
4.1. Eliminate human operators
Section 3.1 states that "It is the design goal to [...] minimally
dependent on [...] human operators". It is however not a design goal
to completely eliminate them. The problem targeted by Autonomic
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Networking is the error-prone and hard to scale model of individual
configuration of network elements, traditionally by manual commands
but today mainly by scripting and/or configuration management
databases. This does not, however, imply the elimination of skilled
human operators, who will still be needed for oversight, policy
management, diagnosis, reaction to help desk tickets, etc. The main
impact on administrators should be less tedious detailed work and
more high-level work. (They should become more like doctors than
hospital orderlies.)
4.2. Eliminate emergency fixes
However good the autonomous mechanisms, sometimes there will be fault
conditions etc. that they cannot deal with correctly. At this point
skilled operator interventions will be needed to correct or work
around the problem. Hopefully this can be done by high-level
mechanisms (adapting the policy database in some way) but in some
cases direct intervention at device level may be unavoidable. This
is obviously the case for hardware failures, even if the autonomic
network has bypassed the fault for the time being. Truck rolls will
not be eliminated when faulty equipment needs to be replaced.
However, this may be less urgent if the autonomic system
automatically reconfigures to minimise the operational impact.
4.3. Eliminate central control
While it is a goal to simplify northbound interfaces (Section 3.5),
it is not a goal to eliminate central control, but to allow it on a
higher abstraction level. Senior management might fear loss of
control of an autonomic network. In fact this is no more likely than
with a traditional network; the emphasis on automatically applying
general policy and security rules might even provide more central
control.
5. An Autonomic Reference Model
An Autonomic Network consists of Autonomic Nodes. Those nodes
communicate with each other through an Autonomic Control Plane which
provides a robust and secure communications overlay. The Autonomic
Control Plane is self-organizing and autonomic itself.
An Autonomic Node contains various elements, such as autonomic
service agents which implement autonomic functions. Figure 1 shows a
reference model of an autonomic node. The elements and their
interaction are:
o Autonomic Service Agents, which implement the autonomic behaviour
of a specific service or function.
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o Self-knowledge: An autonomic node knows its own properties and
capabilities
o Network Knowledge (Discovery): An autonomic service agent may
require various discovery functions in the network, such as
service discovery.
o Intent: Network wide high level policy. Autonomic Service Agents
use an Intent interpretation engine to locally instantiate the
global Intent. This may involve coordination with other Autonomic
Nodes.
o Feedback Loops: Control elements outside the node may interact
with autonomic nodes through feedback loops.
o An Autonomic User Agent, providing a front-end to external users
(administrators and management applications) through which they
can receive reports, and monitor the Autonomic Network.
o Autonomic Control Plane: Allows the node to communicate with other
autonomic nodes. Autonomic functions such as Intent distribution,
feedback loops, discovery mechanisms, etc, use the Autonomic
Control Plane. The Autonomic Control Plane can run inband, over a
configured VPN, over a self-managing overlay network, as described
in [I-D.behringer-autonomic-control-plane], or over a traditional
out of band network. Security is a requirement for the Autonomic
Control Plane, which can be bootstrapped by a mechanism as
described in [I-D.pritikin-bootstrapping-keyinfrastructures].
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+------------------------------------------------------------+
| +------------+ |
| | Feedback | |
| | Loops | |
| +------------+ |
| ^ |
| Autonomic User Agent |
| V |
| +-----------+ +------------+ +------------+ |
| | Self- | | Autonomic | | Network | |
| | knowledge |<------>| Service |<------>| Knowledge | |
| | | | Agents | | (Discovery)| |
| +-----------+ +------------+ +------------+ |
| ^ ^ |
| | | |
| V V |
|------------------------------------------------------------|
| Autonomic Control Plane |
|------------------------------------------------------------|
| Standard Operating System Functions |
+------------------------------------------------------------+
Figure 1: Reference Model for an Autonomic Node
6. IANA Considerations
This draft does not request any IANA action.
7. Security Considerations
This document provides definitions and design goals for Autonomic
Networking. A full threat analysis will be required as part of the
development of solutions, taking account of potential attacks from
within the network as well as from outside.
8. Acknowledgements
Many parts of this work on Autonomic Networking are the result of a
large team project at Cisco Systems. In alphabetical order: Ignas
Bagdonas, Parag Bhide, Balaji BL, Toerless Eckert, Yves Hertoghs,
Bruno Klauser.
We thank the following people for their input to this document:
Dimitri Papadimitriou, Rene Struik, Kostas Pentikousis, Dave Oran,
and Diego Lopez Garcia.
The ETSI working group AFI (http://portal.etsi.org/afi) defines a
similar framework for Autonomic Networking in the "General Autonomic
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Network Architecture" [GANA]. Many concepts explained in this
document can be mapped to the GANA framework. The mapping is outside
the scope of this document. Special thanks to Ranganai Chaparadza
for his comments and help on this document.
9. Informative References
[Dobson] Dobson et al., S., "A survey of autonomic communications",
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Volume 1 Issue 2, Pages 223-259 , December 2006.
[GANA] ETSI GS AFI 002, , "Autonomic network engineering for the
self-managing Future Internet (AFI): GANA Architectural
Reference Model for Autonomic Networking, Cognitive
Networking and Self-Management.", April 2013,
<http://www.etsi.org/deliver/etsi_gs/
AFI/001_099/002/01.01.01_60/gs_afi002v010101p.pdf>.
[I-D.behringer-autonomic-control-plane]
Behringer, M., Bjarnason, S., BL, B., and T. Eckert, "An
Autonomic Control Plane", draft-behringer-autonomic-
control-plane-00 (work in progress), June 2014.
[I-D.irtf-nmrg-an-gap-analysis]
Jiang, S., Carpenter, B., and M. Behringer, "General Gap
Analysis for Autonomic Networking", draft-irtf-nmrg-an-
gap-analysis-04 (work in progress), March 2015.
[I-D.pritikin-bootstrapping-keyinfrastructures]
Pritikin, M., Behringer, M., and S. Bjarnason,
"Bootstrapping Key Infrastructures", draft-pritikin-
bootstrapping-keyinfrastructures-01 (work in progress),
September 2014.
[Kephart] Kephart, J. and D. Chess, "The Vision of Autonomic
Computing", IEEE Computer vol. 36, no. 1, pp. 41-50,
January 2003, <http://users.soe.ucsc.edu/~griss/agent-
papers/ieee-autonomic.pdf>.
[Movahedi]
Movahedi, Z., Ayari, M., Langar, R., and G. Pujolle, "A
Survey of Autonomic Network Architectures and Evaluation
Criteria", IEEE Communications Surveys & Tutorials Volume:
14 , Issue: 2 DOI: 10.1109/SURV.2011.042711.00078,
Page(s): 464 - 490, 2012.
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[Samaan] Samaan, N. and A. Karmouch, "Towards Autonomic Network
Management: an Analysis of Current and Future Research
Directions", IEEE Communications Surveys and Tutorials
Volume: 11 , Issue: 3; DOI: 10.1109/SURV.2009.090303;
Page(s): 22 - 36, 2009.
Authors' Addresses
Michael Behringer
Cisco Systems
Building D, 45 Allee des Ormes
Mougins 06250
France
Email: mbehring@cisco.com
Max Pritikin
Cisco Systems
5330 Airport Blvd
Boulder, CO 80301
USA
Email: pritikin@cisco.com
Steinthor Bjarnason
Cisco Systems
Mail Stop LYS01/5
Philip Pedersens vei 1
LYSAKER, AKERSHUS 1366
Norway
Email: sbjarnas@cisco.com
Alexander Clemm
Cisco Systems
170 West Tasman Drive
San Jose , California 95134-1706
USA
Email: alex@cisco.com
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Brian Carpenter
Department of Computer Science
University of Auckland
PB 92019
Auckland 1142
New Zealand
Email: brian.e.carpenter@gmail.com
Sheng Jiang
Huawei Technologies Co., Ltd
Q14, Huawei Campus
No.156 Beiqing Road
Hai-Dian District, Beijing 100095
P.R. China
Email: jiangsheng@huawei.com
Laurent Ciavaglia
Alcatel Lucent
Route de Villejust
Nozay 91620
France
Email: laurent.ciavaglia@alcatel-lucent.com
Behringer, et al. Expires September 24, 2015 [Page 16]
