Presentation on theme: "1 16/02/2014 Your business technologists. Powering progress A necessary symbiosis; Cloud Computing, IoT, Big Data and Mobile Brussels, 17 th April Josep."— Presentation transcript:
1 16/02/2014 Your business technologists. Powering progress A necessary symbiosis; Cloud Computing, IoT, Big Data and Mobile Brussels, 17 th April Josep Martrat Atos firstname.lastname@example.org
2 16/02/2014 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
3 16/02/2014 The new Atos Reminder Snapshot by Service Line Snapshot by Market 8.6 Bn (proforma 2010) Snapshot by geographies C. 74,000 employees
4 16/02/2014 Main IT trends Important economic and IT trends are shaping a new transformation" Mobility and Internet of Things Social Networks and Media Cloud Computing Big Data Tech drivers Users & applications Main economic trends Economic power changing towards emerging economies The debt crisis leads to cost pressure Teh IPR* are more valuable than ever to keep the competitive advantatge * Intellectual Property Rights
5 16/02/2014 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 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
6 16/02/2014 BIG 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
7 16/02/2014 BD: 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-orientedDocument- oriented data Graph-oriented databases Voldermort (Linkedin), Membase Google BigTable, Cassandra (facebook), Hbase (Yahoo, Microsoft*) MongoDB (10gen), CouchDB Neo4j
10 16/02/2014 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 machine- generated data will represent 42% of all data by 2020 (up from 11% in 2005).
11 16/02/2014 Western Europe Internet Devices Source : IDC Information Society Index Post-PC era
12 16/02/2014 Scenario (example): Smart Stadium 12 Mobile device Access capture personal informations 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 >>> CPU Security Private/Public Cloud Content management Recommendation systems Augnmented reality Media Distribution Media on-venue/internet distribution Bandwidth CDN Crowd uploading content to social networks Encoders Intelligent waste management Public waste baskets monitor their fill level, frequency of use and defectiveness Server Movement/capacity sensors
13 16/02/2014 Scenario (example): Smart Airport Weather sensors 13 802.11 interface Bluetooth Webcam Online storage Server Mobile device & client app Ethernet Operational DB Shopping facilities
14 16/02/2014 Some 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. Dont 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
15 16/02/2014 Some 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.
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