1 16/02/2014 Your business technologists. Powering progress A necessary symbiosis; Cloud Computing, IoT, Big Data and Mobile Brussels, 17 th April Josep.

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10/04/23

Your business technologists. Powering progress

A necessary symbiosis;Cloud Computing, IoT, Big Data and Mobile

Brussels, 17th April

Josep MartratAtos

[email protected]

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10/04/23

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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▶ Main IT trends

Important economic and IT trends are shaping a “new transformation"

Tech driversTech drivers

Users & applications

Users & applications

▶ Main economic trends

* Intellectual Property Rights

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Enterprise roadblocks to move to Cloud

Many customers are still on the edge of their journey to the Cloud

Weight of legacy and fear of migration complexity

Complex Cloud market, Complex billing and

management

Localization of data and privacy to comply with

regulations

Enterprise-grade availability & Security missing in many

offers

Reluctance to become prisoner of another

technology silo

▶ Increase productivity

▶ Higher flexibility

▶ Elastic access to infrastructure resources

Promise and value proposition is clear

▶ Reduce costs

▶ … and it works!

▶ Accelerate the response to demands

▶ Agility and virtual teams

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10/04/23BIG DATA adoption: Drivers and Barriers

▶ Efficiency benefits

▶ Better services

▶ Innovation possibilities

▶ Others are using it (successful cases)

▶ Decrease of adoption cost

•

• Immature technology

• Adoption cost (storage

outsourcing)

• Expertise and tech skills

required to optimal operate

• Understand value (Data

analyst and BI)

• Security and concerns on

privacy

• Migration to cloud

• Regulatory aspects

DRIVERS BARRIERS

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10/04/23BD: Data Storage & Processing

Storage: (NoSQL concept elasticity and fault tolerance )

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!

Key-value stores

Column-oriented Document-oriented data

Graph-oriented databases

Voldermort (Linkedin), Membase

Google BigTable, Cassandra (facebook), Hbase (Yahoo, Microsoft*)

MongoDB (10gen), CouchDB

Neo4j

04/10/23© 2012 IDC 8

Internet of Things Part 1Internet of Things Part 1

Units Installed Worldwide (millions)

Source: IDC Everything Network

04/10/23© 2012 IDC 9

Internet of Things Part 2Internet of Things Part 2

Units Installed Worldwide (millions)

Source: IDC Everything Network

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10/04/23Information 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 machine-generated data will represent 42% of all data by 2020 (up from 11% in 2005).

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10/04/23Western Europe Internet Devices

Source : IDC Information Society Index

Post-PC era

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10/04/23Scenario (example): Smart Stadium

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Mobile device

Access capture personalinformations and perform a

verification in real time of that person against

personal RFID badge of the enterprise.

802.11 interface

Radio F

Fingerprint capture

Tetra

IP cameras

Crowd enters the stadium Security agents use a Public Security network

Sportmen recognition and 3D tracking>>> CPUSecurity

Private/Public Cloud

Content managementRecommendation systems

Augnmented reality

Media DistributionMedia on-venue/internet distribution

Bandwidth

CDN

Crowd uploading content to social networks

Encoders

Intelligent waste managementPublic waste baskets monitor their fill

level, frequency of use and defectiveness

Server

Movement/capacity sensors

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10/04/23Scenario (example): Smart Airport

Weather sensors

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802.11 interface

Bluetooth

Webcam

Online storage

Server

Mobile device & client app

Ethernet

Operational DB Shopping facilities

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10/04/23Some hints when analysing the symbiosis (IoT, BD, Mobile, Clouds)

▶ 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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10/04/23Some challenges

• 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.

10/04/23

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