Transcript title

17/07/2015
A necessary symbiosis;
Cloud Computing, IoT, Big Data and Mobile
Brussels, 17th April
Josep Martrat
Atos
[email protected]
Your business technologists. Powering progress
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17/07/2015
Agenda
▶ Atos, the worldwide IT partner
▶ Trends and context
▶ Cloud and Big data: advantages & barriers
▶ Big data options: storage & processing
▶ And mobiles and IoT comes into arena
▶ Challenges
▶ Scenarios examples
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Important economic and IT trends are
shaping a “new transformation"
▶ Main IT trends
▶ Main economic trends
Users &
applications
Mobility
and
Internet
of Things
17/07/2015
Economic power changing
towards emerging
economies
Social
Networks
and
Media
The debt crisis leads to
cost pressure
Big Data
Cloud
Computing
Teh IPR* are more valuable
than ever to keep the
competitive advantatge
Tech drivers
* Intellectual Property Rights
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Many customers are still on the edge
of their journey to the Cloud
Promise and value proposition is clear
17/07/2015
Enterprise roadblocks to move to Cloud
Weight of legacy and fear of
migration complexity
▶ Increase productivity
▶ Higher flexibility
▶ Accelerate the response to
demands
▶ Elastic access to infrastructure
resources
▶ Agility and virtual teams
Complex Cloud market,
Complex billing and
management
Localization of data and
privacy to comply with
regulations
▶ Reduce costs
Enterprise-grade availability &
Security missing in many offers
▶ … and it works!
Reluctance to become prisoner
of another technology silo
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BIG DATA adoption: Drivers and
Barriers
17/07/2015
BARRIERS
DRIVERS
• Immature technology
• Adoption cost (storage
▶ Efficiency benefits
outsourcing)
▶ Better services
• Expertise and tech skills
▶ Innovation
possibilities
required to optimal operate
▶ Others are using it
(successful cases)
analyst and BI)
▶ Decrease of adoption
cost
privacy
• Understand value (Data
• Security and concerns on
• Migration to cloud
• Regulatory aspects
•
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BD: Data Storage & Processing
17/07/2015
Storage: (NoSQL concept  elasticity and fault tolerance )
Key-value
stores
Column-oriented
Documentoriented data
Graph-oriented
databases
Voldermort
(Linkedin),
Membase
Google BigTable,
MongoDB
Neo4j
Cassandra (facebook), (10gen),
Hbase (Yahoo,
CouchDB
Microsoft*)
The choice of a solution depends on the strategy for the exploitation of Big Data
chosen. Consistency models: Trade-off between consistency and availability!
Processing: (Map reduce. Hadoop implementation)
- We need an optimal ‘processing’ environment (cloud resources &
configurations in private, public, federated, hybrid modes)
- Reduce data transfer vs remote clouds
- Map reduce designed for batch processes – so not suitable for real
time!
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Internet of Things Part 1
8,000
Units Installed Worldwide (millions)
7,000
6,000
5,000
Computers
Handhelds
Networking
4,000
Industrial/automotive
Embedded
3,000
Household
2,000
1,000
0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2016 2017 2018 2019 2020
Source: IDC Everything Network
© 2012 IDC
Jul-15
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Internet of Things Part 2
1,400,000
Units Installed Worldwide (millions)
1,200,000
1,000,000
800,000
Sensors and Tags
Everything Else
600,000
400,000
200,000
0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2016 2017 2018 2019 2020
Source: IDC Everything Network
© 2012 IDC
Jul-15
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17/07/2015
Information exploition
( immense dataset)
▶ Data generation rate and storage needs is rising faster than net bandwidth.
▶ Video-on-demand services occupied 30% of Internet bandwidth in December
2012.
▶ YouTube received 72 hours of new video every minute, which required 17
petabytes of new storage in 2012.
▶ Mobile devices will both consume and
generate much of this data. By the end of
2012, mobile devices generated 25% of
Internet traffic.
▶ According to Cisco, video will account for
86% of all wireless traffic by 2016.
▶ Mobile devices also generate lots of sensor
data, such as GPS location data. Thus,
they are the primary source of the
machine-to-machine (M2M) traffic that
comprises the Internet of Things.
▶ An IDC report forecasts that machinegenerated data will represent 42% of all
data by 2020 (up from 11% in 2005).
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17/07/2015
Western Europe Internet Devices
600,000,000
500,000,000
400,000,000
PC
300,000,000
Non-PC
200,000,000
100,000,000
Post-PC era
0
2010
2011
2012
2013
Source : IDC Information Society Index
2014
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2015
Scenario
(example): Smart Stadium
Movement/capacity
sensors
802.11 interface
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Crowd uploading content to social networks
Server
Media Distribution
Media on-venue/internet distribution
Bandwidth
Intelligent waste management
Public waste baskets monitor their fill
level, frequency of use and
defectiveness
CDN
Encoders
Fingerprint
capture
Radio F
IP cameras
Sportmen recognition and 3D tracking
>>> CPU
Security
Access capture personal
informations and perform a
verification in real time of that
person against
personal RFID badge of the
enterprise.
Mobile device
Crowd enters the stadium
Private/Public
Cloud
Tetra
Security agents use a Public Security network
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Content management
Recommendation systems
Augnmented reality
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17/07/2015
Scenario (example): Smart Airport
Mobile device & client app
802.11 interface
Bluetooth
Server
Webcam
Ethernet
Weather sensors
Online storage
Operational DB
Shopping facilities
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Some hints when analysing the
symbiosis (IoT, BD, Mobile, Clouds)
17/07/2015
▶ Put business objectives and market cases at front (industry driven).
▶ Most IT organizations like to separate data, and cloud, and even assign
them to different teams. However, it may be more productive to link
them strategically.
▶ Big data and IoT segments will become more tightly coupled with Cloud
as markets continue to progress.
▶ Don’t think that the fundamental technologies will merge at any
point. Instead, look at the clear dependencies that should be
considered when dealing with these technologies independently, and as
a whole.
▶ Solve the lack of comprehensive vision and necessary skills to
understand the interaction, impact and dependences of Big Data, IoT,
Mobile and Clouds, all at the same time
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17/07/2015
Some challenges
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Data scalability problem is not the same that Cloud scalability / elasticity
problem (data assets are not VMs). Both strategies need to be aligned to
deliver performance, reliability, consistency and availability.
IoT related applications have non-virtualised parts (distributed sensors
and agents) and it is necessary to study how to incorporate this in the
Cloud Management layers (generally more centralized approach)
Data management and sharing need better abstractions to be included in
the Cloud programming models
Strategies for the migration of huge volume of data to cloud
Skills gaps in the intersection of Data management & Cloud delivery
models
Real time need vs BidData processing approach has limitations and
impact on strategy for mobile clouds
Hypervisor choice & resource type impacts on application data
performance (not well understood yet). Need clouds specialization.
Mobile access networks and context aware computing as the main mean
to consume data. Offloading and dynamic bursting strategies needed at
the edge of network.
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▶ For more information please contact:
[email protected]
Atos
Av Diagonal 200
08017 Barcelona - Spain
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17/07/2015