wdiff draft-mcbride-edge-data-discovery-overview-01.txt draft-mcbride-edge-data-discovery-overview-02.txt

T2TRG
COINRG M. McBride
Internet-Draft Huawei Futurewei
Intended status: Standards Track D. Kutscher
Expires: September 11, 2019 January 8, 2020 Emden University
E. Schooler
Intel
CJ. Bernardos
UC3M
March 10,
July 07, 2019

Overview of

Edge Data Discovery
draft-mcbride-edge-data-discovery-overview-01 for COIN
draft-mcbride-edge-data-discovery-overview-02

Abstract

This document describes the problem of distributed data discovery in
edge computing. Increasing numbers of IoT devices computing, and sensors are
generating a torrent in particular for computing-in-the-network
(COIN), which may require both the marshalling of data that originates at the very edges outset
of the
network and that flows upstream, if it flows at all. Sometimes that
data must be processed or transformed (transcoded, subsampled,
compressed, analyzed, annotated, combined, aggregated, etc.) on edge
equipment, particularly in places where multiple high bandwidth
streams converge a computation and where resources are limited. Support for edge
data analysis is critical to make local, low-latency decisions (e.g.,
regarding predictive maintenance, the dispatch persistence of emergency services,
identity, authorization, etc.). In addition, (transformed) the resultant data may
be cached, copied and/or stored at multiple locations in after the network
on route to its final destination.
computation. Although the data might originate at the network edge, for example in factories, automobiles, video cameras,
wind farms, etc.,
as more and more distributed data is created,
processed processed, and stored,
it becomes increasingly dispersed throughout the network. There
needs to be a standard way to find it. New and existing protocols
will need to be identified/developed/enhanced for developed to support distributed data discovery at
the network edge and beyond.

Status of This Memo

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provisions of BCP 78 and BCP 79.

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Table of Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Edge Data . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Background . . . . . . . . . . . . . . . . . . . . . . . 3
1.3. Requirements Language . . . . . . . . . . . . . . . . . . 4
1.4. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4
2. The Edge Data Discovery Problem Scope . . . . . . . . . . . . 5 . . 4
2.1. A Cloud-Edge Continuum . . . . . . . . . . . . . . . . . 5
2.2. Types of Edge Data . . . . . . . . . . . . . . . . . . . 6
3. Scenarios for Discovering Requiring Discovery of Edge Data Resources . . . . . . . . 8 7
4. Edge Data Discovery . . . . . . . . . . . . . . . . . . . . . 8 7
4.1. Types of Discovery . . . . . . . . . . . . . . . . . . . 9 7
4.2. Naming the Data . . . . . . . . . . . . . . . . . . . . . 9 8
5. Use Cases of edge data discovery . . . . . . . . . . . . . . 10 9
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10 12
7. Security Considerations . . . . . . . . . . . . . . . . . . . 10 12
8. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 10 12
9. Normative References . . . . . . . . . . . . . . . . . . . . 11 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11 13

1. Introduction

Edge computing is an architectural shift that migrates Cloud
functionality (compute, storage, networking, control, data
management, etc.) out of the back-end data center to be more
proximate to the IoT data being generated and analyzed at the edges
of the network. Edge computing provides local compute, storage and
connectivity services, often required for latency- and bandwidth-
sensitive applications. Thus, Edge Computing plays a key role in
verticals such as Energy, Manufacturing, Automotive, Video Analytics,
Surveillance, Retail, Gaming, Healthcare, Mining, Buildings and Smart
Cities.

1.1. Edge Data

Edge computing is motivated at least in part by the sheer volume of
data that is being created by IoT endpoint devices (sensors, cameras,
lights, vehicles, drones, wearables, etc.) at the very network edge
and that flows upstream, in a direction for which the network was not
originally provisioned. designed. In fact, in dense IoT deployments (e.g., many
video cameras are streaming high definition video), where multiple
data flows collect or converge at edge nodes, data is likely to need
transformation (transcoded, subsampled, compressed, analyzed,
annotated, combined, aggregated, etc.) to fit over the next hop link,
or even to fit in memory or storage. Note also that the act of
performing compute on the data creates yet another new data stream!
Preservation of the original data streams are needed sometimes but
not always.

In addition, data may be cached, copied and/or stored at multiple
locations in the network on route to its final destination. With an
increasing percentage of devices connecting to the Internet being
mobile, support for in-the-network caching and replication is
critical for continuous data availability, not to mention efficient
network and battery usage for endpoint devices.

Additionally, as mobile devices' memory/storage fill up, in an edge
context they may have the ability to offload their data to other
proximate devices or resources, leaving a bread crumb trail of data
in their wakes. Therefore, although data might originate at edge
devices, as more and more data is continuously created, processed and
stored, it becomes increasingly dispersed throughout the physical
world (outside of or scattered across managed local data centers),
increasingly isolated in separate local edge clouds or data silos.
Thus there needs to be a standard way to find it. New and existing
protocols will need to be identified/developed/enhanced for these
purposes. Being able to discover distributed data at the edge or in
the middle of the network - will be an important component of Edge
computing.

1.2. Background

Several IETF T2T RG Edge discussion was Computing discussions have been held and over
the last couple years, a comparative study on the definition of Edge
computing was presented in multiple sessions in T2T RG in 2018. 2018 and an
Edge Computing I-D was submitted early 2019. An IETF BEC (beyond
edge computing) effort has been evaluating potential gaps in existing
edge computing architectures. Edge Data Discovery is one potential
gap that was identified and that needs evaluation and a solution.

Businesses, such as industrial companies, are starting to understand
how valuable
The newly proposed COIN RG highlights the data is that they've kept need for computations in silos. Once this data
is made accessible on edge computing platforms, they may
the network to be able to
monetize the value of the data. But this will happen only if marshal potentially distributed input data
can be discovered
and searched among heterogeneous equipment in a
standard way. Discovering the to handle resultant output data, that i.e., its most useful to a given
market segment, will be extremely useful in building business
revenues. Having a mechanism to provide this granular discovery is
the problem that needs solving either with existing, or new,
protocols. placement, storage
and/or possible migration strategy.

1.3. Requirements Language

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].

1.4. Terminology

o Edge: The edge encompasses all entities not in the back-end cloud.
The device edge is represents the boundary between digital very leaves of the network and physical
encompasses the entities found in the last mile network. Sensors,
gateways, compute nodes are included. Because the things that
populate the IoT can be both physical and/or cyber, in some
solutions, particularly in software-defined or digital-twin
contexts, the device edge can include logical (vs physical)
entities. The infrastructure edge includes equipment on the
network operator side of the last mile network including cell
towers, edge data centers, cable headends, POPs, etc. See
Figure 1 for other possible tiers of edge clouds between the
device edge and the back-end cloud data center.

o Edge Computing: Distributed computation that is performed near the
network edge, where nearness is determined by the system
requirements. This includes high performance compute, storage and
network equipment on either the device or infrastructure edge.

o Edge Data Discovery: The process of finding required data from
edge entities, i.e., from databases, files systems, device memory
that might be physically distributed in the network, and
consolidating it or by providing access to it logically as if it were
a single unified source, perhaps through its namespace, that can
be evaluated or searched.

o NDN: Named Data ICN: Information Centric Networking. NDN An ICN-enabled network
routes data by name (vs address), caches content natively in the
network, and employs data-centric security. Data discovery may
require that data be associated with a name or names, a series of
descriptive attributes, and/or a unique identifier.

2. The Edge Data Discovery Problem Scope

Our focus is on how to define and scope the edge data discovery
problem. This requires some discussion of the evolving definition of
the edge as part of a cloud-to-edge continuum and in turn what is
meant by edge data as well as the meta-data surrounding the edge
data.

2.1. A Cloud-Edge Continuum

Although Edge Computing data typically originates at edge devices,
there is nothing that precludes edge data from being created anywhere
in the cloud-to-edge computing continuum (Figure 1). New edge data
may result as a byproduct of computation being performed on the data
stream anywhere along its path in the network. For example,
infrastructure
example,infrastructure edges may create new edge data when multiple
data streams converge upon this aggregation point and require
transformation (e.g., to fit within the available resources. Edge data also
may be sent resources, to
smooth raw measurements to eliminate high-frequency noise, to
obfuscate data for privacy).

An assumption is that all data will have associated policies
(default, inherited or configured) that describe access control
permissions. Consequently, the back-end cloud as needed. Discovering discoverability of data which will be a
function of who or what has requested access. In other words, the
discoverable view into the available data will be sent limited to the cloud those
who are authorized. Discovering edge data that is exclusively
private is out of scope of this document, the assumption being that the cloud boundary is one
there will be some edge clouds that does do not expose or publish the
availability of its their data. Although edge data may be sent to the
back-end cloud as needed, there is nothing that precludes it from
being discoverable if the cloud offers it as public.

Initially our focus is on discovery of edge data that resides at the
Device Edge and the Infrastructure Edge.

+-------------------------------+
| Back-end Cloud Data Center |
+-------------------------------+
*** Cloud
* * Interconnect
***
+-------------------------------+
| Core Data Center |
+-------------------------------+
*** Backbone
* * Network
***
+-------------------------------+
| Regional Data Center |
+-------------------------------+
*** Metropolitan
* * Network
***
+-------------------------------+
| Infrastructure Edge |
+-------------------------------+
*** Access
* * Network
***
+-------------------------------+
| Device Edge |
+-------------------------------+

Figure 1: Cloud-to-edge computing continuum

Initially our focus is on discovery of edge data that resides at the
Device Edge and the Infrastructure Edge.

2.2. Types of Edge Data

Besides classically constrained IoT device sensor and measurement
data accumulating throughout the edge computing infrastructure, edge
data may also take the form of higher frequency and higher volume
streaming data (from a continuous sensor or from a camera), meta data
(about the data), control data (regarding an event that was
triggered), and/or an executable that embodies a function, service,
or any other piece of code or algorithm. Edge data also could be
created after multiple streams converge at the an edge node and are
processed, transformed, or aggregated together in some manner.

SFC Data and meta-data discovery

Service function chaining (SFC) allows the instantiation

Regardless of an
ordered set of service functions and subsequent "steering" of traffic
through them. Service functions provide edge data type, a specific treatment of
received packets, therefore they need to be known so they can be used key problem in a given service composition via SFC. So far, how the SFs are
discovered and composed has been out of the scope of discussions in
IETF. While there are some mechanisms Cloud-Edge
continuum is that can be used and/or
extended to provide this functionality, work needs to be done. An
example of this can be found data is often kept in [I-D.bernardos-sfc-discovery].

In an SFC environment deployed at the edge, the discovery protocol
may also need to make available the following meta-data information
per SF:

o Service Function Type, identifying the category of SF provided.

o SFC-aware: Yes/No. Indicates if the SF silos. Meaning, data is SFC-aware.

o Route Distinguisher (RD): IP address indicating the location of
the SF(I).

o Pricing/costs details.

o Migration capabilities of the SF: whether a given function can be
moved to another provider (potentially including information about
compatible providers topologically close).

o Mobility of the device hosting the SF, with e.g. the following
sub-options:

Level: no, low, high; or a corresponding scale (e.g., 1 to 10).

Current geographical area (e.g., GPS coordinates, post code).

Target moving area (e.g., GPS coordinates, post code).

o Power source of the device hosting the SF, with e.g. the following
sub-options:

Battery: Yes/No. If Yes, the following sub-options could be
defined:

Capacity of
often sequestored within the battery (e.g., mmWh).

Charge status (e.g., %).

Lifetime (e.g., minutes).

Discovery Edge where it was created. A goal of resources in an NFV environment: virtualized resources
do not need to be limited
this discussion is to those available in traditional data
centers, where consider the infrastructure is stable, static, typically
homogeneous and managed by a single admin entity. Computational
capabilities are becoming more and more ubiquitous, with terminal
devices getting extremely powerful, as well as other prospect that different types of devices
that are close to the end users at the
edge (e.g., vehicular onboard
devices for infotainment, micro data centers deployed at the edge,
etc.). It is envisioned that these devices would be able to offer
storage, computing and networking resources to nearby network
infrastructure, devices and things (the fog paradigm). These
resources can will be used to host functions, made accessible across disparate edges, for example
to offload/
complement other resources available at traditional enable richer multi-modal analytics. But this will happen only if
data centers, but
also can be described, searched and discovered across heterogeneous
edges in a standard way. Having a mechanism to reduce enable granular edge
data discovery is the end-to- end latency problem that needs solving either with existing
or new protocols. The mechanisms shouldn't care to provide access to
specialized information (e.g., context available at the edge) which flavor
cloud or
hardware. Similar to edge the request for data discovery of functions, while there are
mechanisms that can be reused/extended, there is no complete solution
yet defined. An example of work in this area is
[I-D.bernardos-intarea-vim-discovery]." made.

3. Scenarios for Discovering Requiring Discovery of Edge Data Resources

Mainly two types of situations need to be covered:

1. A set of data resources appears (e.g., a mobile node hosting data
joins a network) and they want to be discovered by an existing
but possibly virtualized and/or ephemeral data directory
infrastructure.

2. A device wants to discover data resources available at or near
its current location. As some of these resources may be mobile,
the available set of edge data may vary over time.

3. A device wants to discover to where best in the edge
infrastructure to opportunistically upload its data, for example
if a mobile device wants to offload its data to the
infrastructure (for greater data availability, battery savings,
etc.).

4. Edge Data Discovery

How can we discover data on the edge and make use of it? There are
proprietary implementations that collect data from various databases
and consolidate it for evaluation. We need a standard protocol set
for doing this data discovery, on the device or infrastructure edge,
in order to meet the requirements of many use cases. We will have
terabytes of data on the edge and need a way to identify its
existence and find the desired data. A user requires the need to
search for specific data in a data set and evaluate it using their
own tools. The tools are outside the scope of this document, but the
discovery of that data is in scope.

4.1. Types of Discovery

There are many aspects of discovery and many different protocols that
address each aspect.

Discovery of new devices added to an environment. Discovery of their
capabilities/services in client/server environments. Discovery of
these new devices automatically. Discovering a device and then
synchronizing the device inventory and configuration for edge
services. There are many existing protocols to help in this
discovery: UPnP, mDNS, DNS-SD, SSDP, NFC, XMPP, W3C network service
discovery, etc.

Edge devices discover each other in a standard way. We can use DHCP,
SNMP, SMS, COAP, LLDP, and routing protocols such as OSPF for devices
to discovery one another.

Discovery of link state and traffic engineering data/services by
external devices. BGP-LS is one solution.

The question is if one or more of these protocols might be a suitable
contender to extend to support edge data discovery?

4.2. Naming the Data

Named Data

Information-Centric Networking (NDN) (ICN) RFC 7927 [RFC7927] is one a class of five research projects funded
by the U.S. National Science Foundation under its Future Internet
Architecture Program. NDN has its roots
architectures and protocols that provide "access to named data" as a
first-order network service. Instead of host-to-host communication
as in an earlier project,
Content-Centric Networking (CCN), which Van Jacobson started at Xerox
PARC around IP networks, ICNs often use location-independent names to
identify data objects, and the time network provides the services of his Google talk,
processing (answering) requests for named data with the objective to
finally deliver the requested data objects to turn his architecture
vision into a running prototype (see requesting consumer.

Such an approach has profound effects on various aspects of a
networking system, including security (by enabling object-based
security on a message/packet level), forwarding behavior (name-based
forwarding, caching), but also his CoNEXT 2009 paper and
especially Jacobsons ACM Queue interview). on more operational aspects such as
bootstrapping, discovery etc.

The motivation CCNx and NDN (https://named-data.net) variants of ICN are based
on a request/response abstraction where consumers (hosts, application
requesting named data) send INTEREST messages into the network that
are forwarded by network elements to a destination that can provide
the requested named data object. Corresponding responses are sent as
so-called DATA messages that follow the reverse INTEREST path.

Each unique data object is named unambiguously in a hierarchical
naming scheme and is authenticated through Public-Key cryptography
(data objects can also optionally be encrypted in different ways).
The naming concept and the object-based security approach lay the
mis-match
foundation for location independent operation. The network can
generally operate without any notion of todays Internet architecture location, and its usage. Today we
build, support, nodes
(consumers, forwarders) can forward requests for named data objects
directly, i.e., without any additional address resolution. Location
independence also enables additional features, for example the
possibility to replicate and use Internet applications cache named data objects. On-patch
caching is a standard feature in many ICN systems -- typically for
enhancing reliability and services on top performance.

In CCNx and NDN, forwarders are stateful, i.e., they keep track of
an extremely capable architecture not designed
forwarded INTEREST to support them. What
if we had an architecture designed later match the received DATA messages.

Stateful forwarding (in conjunction with the general named-based and
location-independent operation) also empowers forwarders to support them? Specifically,
todays IP packets can name only endpoints execute
individual forwarding strategies and perform optimizations such as
in-network retransmissions, multicasting requests (in cases there are
several opportunities for accessing a particular named data object)
etc.

Naming data and application-specific naming conventions are naturally
important aspects in ICN. It is common that applications define
their own naming convention (i.e., semantics of conversations (IP
addresses) at elements in the network layer. What if we generalize this layer to name any information (or content), not just endpoints? We make it
easier to develop, manage, secure,
hierarchy). Such names can often directly derived from application
requirements, for example a name like /my-home/living-
room/light/switch/main could be relevant in a smart home setting, and
corresponding devices and application could use our networks. NDN a corresponding
convention to facilitate controllers finding sensors and actors in
such a system with minimal user configuration.

The aforementioned features make ICN amenable to data discovery.
Because there is no name/address chasm as in IP-based systems, data
can be
applied discovered by sending INTEREST to edge named data objects directly
(assuming a naming convention as described above). Moreover, ICN can
authenticate received data objects directly, for example using local
trust anchors in the network (for example in a home network).

Advanced ICN features for data discovery to make it much easier to extract include the concept of
manifests in CCNx, i.e., ICN objects that describe data collections,
and meta-data by naming it. If data was named we would be able set synchronization protocols in NDN (https://named-
data.net/publications/li2018sync-intro/) that can inform consumers
about the availability of new data in a tree-based data structure
(with automatic retrieval and authentication). Also, ICN is not
limited to
discover accessing static data. Frameworks such as Named Function
Networking (http://www.named-function.net) and RICE can provide the appropriate
general ICN feature for discovery not only for data simply by its name. but also for name
functions (for in-network computing) and for their results.

5. Use Cases of edge data discovery

1. Autonomous Vehicles

Autonomous vehicles rely on the processing of huge amounts of complex
data in real-time for fast and accurate decisions. These vehicles
will rely on high performance compute, storage and network resources
to process the volumes of data they produce in a low latency way.
Various systems will need a standard way to discover the pertinent
data for decision making

2. Video Surveillance
The majority of the video surveillance footage will remain at the
edge infrastructure (not sent to the cloud data center). This
footage is coming from vehicles, factories, hotels, universities,
farms, etc.Much of the video footage will not be interesting to those
evaluating the data. A mechanism, set of protocols perhaps, is
needed to identify the interesting data at the edge. What
constitutes interesting will be context specific, e.g., video frames
with a car in it, a backyard nocturnal creature in it, a person or
bicyclist or etc. Interesting video data may be stored longer in
storage systems at the very edge of the network or in flight in
networking equipment further away from the device edge.

3. Elevator Networks

Elevators are one of many industrial applications of edge computing.
Edge equipment receives data from 100's of elevator sensors. The
data coming into the edge equipment is vibration, temperature, speed,
level, video, etc. We need the ability to identify where the data we
need to evalute is located.

4. Service Function Chaining

Service function chaining (SFC) allows the instantiation of an
ordered set of service functions and the subsequent "steering" of
traffic through them. Service functions provide a specific treatment
of received packets, therefore they need to be known so they can be
used in a given service composition via SFC. So far, how the SFs are
discovered and composed has been out of the scope of discussions in
IETF. While there are some mechanisms that can be used and/or
extended to provide this functionality, work needs to be done. An
example of this can be found in [I-D.bernardos-sfc-discovery].

In an SFC environment deployed at the edge, the discovery protocol
may also need the following kind of meta-data information per SF:

o Service Function Type, identifying the category of SF provided.

o SFC-aware: Yes/No. Indicates if the SF is SFC-aware.

o Route Distinguisher (RD): IP address indicating the location of
the SF(I).

o Pricing/costs details.

o Migration capabilities of the SF: whether a given function can be
moved to another provider (potentially including information about
compatible providers topologically close).

o Mobility of the device hosting the SF, with e.g. the following
sub-options:

Level: no, low, high; or a corresponding scale (e.g., 1 to 10).

Current geographical area (e.g., GPS coordinates, post code).

Target moving area (e.g., GPS coordinates, post code).

o Power source of the device hosting the SF, with e.g. the following
sub-options:

Battery: Yes/No. If Yes, the following sub-options could be
defined:

Capacity of the battery (e.g., mmWh).

Charge status (e.g., %).

Lifetime (e.g., minutes).

Discovery of resources in an NFV environment: virtualized resources
do not need to be limited to those available in traditional data
centers, where the infrastructure is stable, static, typically
homogeneous and managed by a single admin entity. Computational
capabilities are becoming more and more ubiquitous, with terminal
devices getting extremely powerful, as well as other types of devices
that are close to the end users at the edge (e.g., vehicular onboard
devices for infotainment, micro data centers deployed at the edge,
etc.). It is envisioned that these devices would be able to offer
storage, computing and networking resources to nearby network
infrastructure, devices and things (the fog paradigm). These
resources can be used to host functions, for example to offload/
complement other resources available at traditional data centers, but
also to reduce the end-to-end latency or to provide access to
specialized information (e.g., context available at the edge) or
hardware. Similar to the discovery of functions, while there are
mechanisms that can be reused/extended, there is no complete solution
yet defined. An example of work in this area is
[I-D.bernardos-intarea-vim-discovery]." The availability of this
meta-data about the capabilities of nearby physical as well as
virtualized resources can be made discoverable through edge data
discovery mechanisms.

6. IANA Considerations

N/A

7. Security Considerations

Security considerations will be a critical component of edge data
discovery particularly as intelligence is moved to the extreme edge
where data is to be extracted.

8. Acknowledgement

The co-authors thank Dave Oran for his detailed feedback on an
earlier version of this draft.

9. Normative References

[I-D.bernardos-intarea-vim-discovery]
Bernardos, C. and A. Mourad, "IPv6-based discovery and
association of Virtualization Infrastructure Manager (VIM)
and Network Function Virtualization Orchestrator (NFVO)",
draft-bernardos-intarea-vim-discovery-01 (work in
progress), February 2019.

[I-D.bernardos-sfc-discovery]
Bernardos, C. and A. Mourad, "Service Function discovery
in fog environments", draft-bernardos-sfc-discovery-02
(work in progress), March 2019.

[I-D.irtf-icnrg-ccnxmessages]
Mosko, M., Solis, I., and C. Wood, "CCNx Messages in TLV
Format", draft-irtf-icnrg-ccnxmessages-09 (work in
progress), January 2019.

[I-D.irtf-icnrg-ccnxsemantics]
Mosko, M., Solis, I., and C. Wood, "CCNx Semantics",
draft-irtf-icnrg-ccnxsemantics-10 (work in progress),
January 2019.

[I-D.kutscher-icnrg-rice]
Krol, M., Habak, K., Oran, D., Kutscher, D., and I.
Psaras, "Remote Method Invocation in ICN", draft-kutscher-
icnrg-rice-00 (work in progress), October 2018.

[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.

[RFC7927] Kutscher, D., Ed., Eum, S., Pentikousis, K., Psaras, I.,
Corujo, D., Saucez, D., Schmidt, T., and M. Waehlisch,
"Information-Centric Networking (ICN) Research
Challenges", RFC 7927, DOI 10.17487/RFC7927, July 2016,
<https://www.rfc-editor.org/info/rfc7927>.

Authors' Addresses

Mike McBride
Huawei
Futurewei

Email: michael.mcbride@huawei.com michael.mcbride@futurewei.com

Dirk Kutscher
Emden University

Email: ietf@dkutscher.net

Eve Schooler
Intel

Email: eve.m.schooler@intel.com
URI: http://www.eveschooler.com

Carlos J. Bernardos
Universidad Carlos III de Madrid
Av. Universidad, 30
Leganes, Madrid 28911
Spain

Phone: +34 91624 6236
Email: cjbc@it.uc3m.es
URI: http://www.it.uc3m.es/cjbc/