Keynote by Thierry Coupaye at the IEEE International Conference on Cloud Networking, Niagara Falls, Canada, October 2015. Summary: Cloud computing emerged, a decade or so ago, from underused computing and storage ressources in Internet players mega data centres that were thought to be provided "as a service". As a result of this inception, Cloud is often considered as a synonym for massive data center, which somehow fuels a very centralised vision of (cloud) computing and storage provision. However, we might be at a time in which the pendulum begins to swing back. Indeed, several initiatives are emerging around a vision of more geographically distributed clouds where computing and storage resources are made available at the edge of the network, close to users, in complement or replacement of massive remote data centres. This presentation discusses, through some examples, the evolution of cloud architectures towards more distribution, the signs and stakes of these mutations.

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Towards the extinction of
mega data centres?
To which extent should the Cloud be distributed
at the network edge?
Keynote @ 4th IEEE International Conference on Cloud Networking, Niagara Falls, Canada
October 5-7 2015
Thierry Coupaye (PhD)
Head of cloud platforms research, Orange Labs, France

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§ Cloud evolutions, variations and mutations
§ Centralized or distributed: pick one!
§ Centralized or distributed: take both!
§ End of story: natural selection?
Outline

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The speaker
§ Head of cloud platforms research at Orange Labs
§ Orange Expert on Future Network
- Sponsor of the “Programmable and cloud networking” domain
§ Background in distributed systems architecture
- Active database systems at University of Grenoble, France
- Semi-structured data management at European Bioinformatics Institute,
Cambridge, UK
- Large scale software deployment at Dassault Systems, France
- Component-based software architectures at Orange, France
-> autonomic and cloud computing

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Why is Cloud Computing strategic for Orange
’’Cloud Computing is in our DNA’’.
Orange Business Services, Dec.
2009
“Cloud Computing is here to drive
the IT ecosystem so traditional ICT
providers must transform or die.”
Yankee Group, 2008
“Cloud computing holds enormous
potential for telecom service
providers if they get aggressive
about driving technological
innovation there.” Telephony Online,
April 2009.
1. evolution of hosting offers for enterprise market
XaaS for multinational companies, large national accounts,
and medium and small enterprises
2. evolution of mass market services platforms and services
- communications, audiovisual (TV, DVR, music), healthcare,
IoT/M2M, transport, gaming…
- emergence of personal cloud
3. evolution of information systems
– network and service provisioning and management,
customers management, business intelligence, billing…
– human resources, inventory (network, suppliers),
finances…
4. embodiment of Future Internet Architecture
“SDN”, “NFV”, “Network Softwarization”
§ Very few networks/platforms/services inside Orange will not be impacted by
cloud computing
§ The future of Orange depends partly on its capability to master cloud
computing

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cloud evolutions,
variations and
mutations

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Cloud evolution
Cloud is not a synonym for mega data centres!

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Back to the definition of Cloud Computing
(adapted from NIST)
6 characteristics
1. On Demand
2. Self-Service
3. Network Access
4. Resource Pooling (multi-
tenancy)
5. Rapid Elasticity
6. Measured Service,
Scalable pricing
3 delivery models
(markets)
1. Cloud Software
as a Service
(SaaS)
2. Cloud Platform
as a Service
(PaaS)
3. Cloud
Infrastructure as
a Service (IaaS)
4 deployment
models
1. Private cloud
2. Public cloud
3. Hybrid cloud
4. Community
Cloud
Source: Forrester
Location transparency is the point!

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“The future of cloud computing - an army of monkeys?”
Sam Johnston, Sept. 2008
“I don't care if my cloud computing architecture is powered
by a grid, a mainframe, my neighbour's desktop or an
army of monkeys, so long as it's fast, cheap and secure.”
Source: https://groups.google.com/forum/#!topic/cloud-computing/xs2ctSEvMbw

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Trends, variations, mutations towards cloud distribution
Centralized public clouds are in fact generally distributed over
multiple (mega) data centres for availability reasons
Verizon (©)
Orange (©)Microsoft (©)
Amazon (©)
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Verizon Data Centres

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Amazon (AWS) Data Centres

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Microsoft Azure Data Centres

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Orange Data Centres
Sydneycore MPLS POP
other core backbone routes
data centres
core DC interconnections
Atlanta, GA
Normandie
Lodz
Rio de Janeiro
major service centres
Mauritius
Cairo
Delhi
London
San Jose, CA
Sterling, VA
Francfurt
Singapore
Hong Kong
Paris

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Hybrid and community clouds are by nature distributed over
multiple data centres/clouds2
© Avaeglo
© Orange
Trends, variations, mutations towards cloud distribution

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Networks are getting « softwarized » and are converging with a
distributed vision of cloud computing.
3 examples:
§ Virtual CDN (vCDN)
§ Cloud RAN (C-RAN)
§ Mobile Edge Computing (MEC)
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Trends, variations, mutations towards cloud distribution

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Virtual Content Delivery Networks (vCDN)
§ vCDN is a step towards CDN “cloudification”
§ vCDN is a step towards cloud distribution at the edge of the network
CDN providers would like to deploy their functions inside mobile networks
© Akamai
© Amazon
§ CDN
- Caching servers placed closed to users
- Used for video streaming and also web
accelaration, device management,
application delivery, virtual desktops…
- Less effective in varying conditions (e.g.
flash crowds)
§ vCDN – virtualization-based CDN
- Dynamicity of ressource (caches)
location
- Isolation in multi-providers scenarios

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Cloud Radio Access Networks (C-RAN)
§ C-RAN is a step towards RAN “cloudification”
§ C-RAN is a step towards cloud distribution at the edge of the network
in the access network because RRH and BBU distance cannot exceed ~20-40kms
§ Traditional/All-in-One/Macro-Base architecture
- Co-location of radio and baseband processing
§ Distributed Base Station
- Separation of radio (RRH) and baseband processing (BBU)
- More convenient place for BBU (maintenance…)
- Still static assignment of BBU to RRH
§ Cloud-RAN
- BBU virtualization and pooling
Dynamicity/flexibility in RRH-BBU assignment, better
performance, energy and cost savings, easier maintenance
and evolution
Source: “Cloud RAN for Mobile Networks – A Technology
Overview”. A. Checko and al. IEEE Communication
Surveys & Tutorials, 7(1), 2015.

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Mobile Edge Computing (MEC)
§ MEC is a step towards cloud distribution at the edge of the network
§ MEC targets geo-distributed applications
§ ETSI ISG targetting:
- a standard service environment open
to 3d party service developpers and
content providers
- on top of a standard hosting
infrastructure
- located at the RAN Edge
§ Benefits: low latency, high
bandwidth, access to radio network
data
§ Use cases: video analytics, location-
based services, IoT, augmented
reality, caching
© ETSI

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Centralized or
distributed: pick
one!

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Economical issues
Criteria Centralized Cloud/Mega DC Distributed Cloud/Micro DC
Construction cost
(terrain, building)
Complex to choose a site, to
get authorisations.
Smaller and more discrete
buildings. Existing buildings
can be reused.
Extension cost Very low up to the DC
physical limit.
Very high beyond: need to
build new mega DC.
Small and smooth. Very low
until the DC physical limit.
Easy beyond to rent or build
a new small building or a
« DC container »
Construction
security cost
Strong, active, humanized
security. A mega-DC is an
industrial site…
Can be passive and light
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© Orange
© Sun Microsystems

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Environmental issues
Criteria Centralized Cloud/Mega DC Distributed Cloud/Micro DC
Energy cost Good price because of high
volumes but heat reselling
may be difficult for isolated
DC
Less or no need for cooling.
Higher capacity to use
locally produced clean
energy (solar, wind…).
Easyness to resell heat to
surrounding buildings
Energetic
connectivity
DC needs to be close to
massive electricity
production. Redondance of
energy producers may be
difficult (e.g. in France)
No special
approvisionnement.
Easyness to get multiple
producers
Network
connectivity
Large DC can be located
close to big peering points
Most micro DC are not
always close to major
peering points
DC workers
transportation cost
Workers generally not in
close proximity
May need transportation but
workers live not far away
Risks (industrial,
natural,
economic…)
Large size implies higher
risks and associated cost
Lower risks. Lower cost.
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© Qarnot Computing

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Technical issues
Criteria Centralized Cloud/Mega DC Distributed Cloud/Micro DC
Manageability
(supersvision)
Centralized management is
easier (homogeneity)
More complex, need more
automation but hardware
management easier.
Security Easy security management. Smaller attack surface (risk
fragmentation). Less damage
in case of attacks.
Availability and
reliability
SPOF risk. Difficult DC
redundancy. Higher
dependability on core
network and Internet traffic
No SPOF. Easier recovery.
Inter-DC fault tolerance.
Local traffic.
Performance Very efficient « inside the
DC » but not necessarily
from user’s point of view.
Lower latencies. Intrinsic
support of user mobility
(follow-me cloud)
Core network
traffic
Heavy Lighter
😕 😏
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© University of Technology Sydney
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Socio-political issues
Criteria Centralized Cloud/Mega DC Distributed Cloud/DC
Proximity and data
protection
Concerns with data location
in remote/foreign mega DC
More trust in local data
centre (e.g. “proxicenter” in
Rennes, France)
Sovereignty and
land-use planning
Huge economic attraction
but limited to happy few and
mega DC generally out of
city centres
Local authorities (e.g. cities)
expectations i) from their own
needs (smart cities) and ii) to
attract business
Legal issues Concerns with data location
(e.g. health, fiscal), lawful
interception
Easier adaption to local
regulations. QoE
improvement through data
location (cf. Net Neutrality)
“Libertarianism” Embodiment of “digital
imperialism”
Contributes to fulfilment of
expectations for
decentralized and open
(source) infrastructures
Proxycenter, Rennes, France © TDF
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Centralized or
distributed: take
both!

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Fog Computing
§ A paradigm from Cisco
“Fog Computing is a highly virtualized
platform that provides compute, storage and
networking services between end devices and
traditional Cloud Computing data centres,
typically, but not exclusively located at the
edge of the network” *
§ Cisco Fog Computing = Cloud + IoT
- Data collection from sensors/things
- Local data processing and actuators
control
- Filtering, aggregation and upload to
remote DC for batch analysis
§ Use cases: connected vehicles, smart
grid, wireless sensors and
actuators… web acceleration
© Cisco
* Source: “Fog Computing and Its Role in the Internet of
Things”. Flavio Bononi and al, Cisco. ACM SIGCOMM
International Conference on Mobile Cloud Computing,
August 2012.

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Some other geo-distributed clouds
§ NTT Edge Computing
- Small servers in vicinity of users
and devices
- Uses cases: smart city/building,
M2M, medical, gaming, speech/
image recognition
- Edge accelerated web platform
research prototype
§ AT&T Cloud 2.0
- Balance between local storage/
computation and remote
offloading
- Favor local storage/computation
by a device or a federation of
devices (~Device-to-Device)
§ SAVI
- Canadian initiative: U. Waterloo,
Toronto, McGill… + IBM, Cisco,
Juniper…
- Testbed
• for accelerated distributed
(possibly short-lived)
applications deployment
• over virtualized small-cell
wireless access network
• Connected through optical
backhaul to multi-tier cloud
including both mega DC and
smart converged edges

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Orange geo-distributed cloud
§ NGPoP
- Virtualized converged access network
- Virtualized computing and storage
- Open to 3d parties
§ Discovery (http://beyondtheclouds.github.io)
- A open initiative lead by Inria with
Orange and Renater
- Targets a Locality-based Utility
Computing platform (“LUC-OS”)
- Hypothesis
• Autonomic and decentralized
management
• OpenStack substrate
§ An ubiquitous cloud platform that leverages a continuum of DC from mega DC
to nano/pico DC (user devices/things) through mini/micro DC (network PoP)
§ Orange as a (geo) distributed open cloud platform operator

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Geo-distributed cloud for new applications
Source: NGMN 5G White Paper
§ “intrinsically local”
§ Crowd/social
§ Connected cars
§ Smart Home
§ Smart City
§ IoT in general
§ Interactive
§ Gaming
§ E-health
§ Augmented reality
§ Virtual reality

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End of story:
natural
selection?

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1. Geo-distributed applications will continue to grow
2. The construction of new massive mega DC might slow down
3. Smaller DC in closer users’ vicinity will complement mega DC
4. User devices are potential cloud platforms too (“nano DC”,
“CloudLets”)
5. Different cloud deployment scenarios will probably co-exist
- which raises many interesting technical/research challenges…
6. This new landscape could change the actors play
webcos <-> cloud providers <-> CDN providers <-> telcos
Final word: natural selection?

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Thank you!
Acknowledgments
This talk contains material from Ivan Meriau, Arnaud Diquélou, Daniel Stern from
Orange Labs, Sylvain Quief and al. from Orange Cloud for Business
Link: Please comment on Orange research blog here:
http://research.orange.com/en/fog-computing-and-geo-distributed-cloud/