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El F enómeno de la N ube

El F enómeno de la N ube. Bernardo A. Huberman HP Labs. La Evoluci ó n de los Datos y Servicios hacia la Nube. The Cloud, Social, Big Data & Mobile. 1990s. Client/Server. 2000s. The Internet. 1970-80s. Mainframe. 2010s. Una explosión de conectividad.

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El F enómeno de la N ube

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  1. El Fenómeno de la Nube Bernardo A. Huberman HP Labs

  2. La Evolución de los Datos y Servicioshacia la Nube The Cloud, Social, Big Data & Mobile 1990s Client/Server 2000s The Internet 1970-80s Mainframe 2010s

  3. Unaexplosión de conectividad Big Data and management challenge 2013 By 2020 Internet of Things 98,000 tweets 30 Billion 23,148 appsdownloaded (1) Devices 400,710 ad requests 40 Trillion GB In 60 sec today (2) DATA 10 Million Mobile Apps 2000 lyrics playedon Tunewiki (3) 1,500pings sent on PingMe 208,333 minutesAngry Birds played … for 8 Billion Pervasive Connectivity Smart Device Expansion Explosion of Information (4) (1) IDC Directions 2013: Why the Datacenter of the Future Will Leverage a Converged Infrastructure, March 2013, Matt Eastwood ; (2) & (3) IDC Predictions 2012: Competing for 2020, Document 231720, December 2011, Frank Gens; (4) http://en.wikipedia.org

  4. UnaExplosión de Datos Cambiostectónicos en la tecnologia 202044 ZB (figure exceeds prior forecasts by 9 ZBs) 2015 2012 2010 2005 0.1 ZB 1.2 ZB 2.8 ZB (1) 8.5 ZB El volumen de datosesdemasiadogrande para podermoverlo de un servidor a otro (1) IDC "The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things" April 2014

  5. Una generación tan masiva de contenido Requiere nuevas herramientas para poder navegar ese torrente de datos

  6. EvoluciónhaciaunaMultiplicidad de Nubes Mainframe >_ >_ The Mainframe era >_ The PC era The Cloud era Multi-cloud

  7. Problemas 1. Nuevosmodeloseconómicos 2. Como navegar y utilizar el torrente de datosque la nube genera sobresupropiofuncionamiento.

  8. Hybrid Clouds

  9. Opciones para el Exceso de Consumo Permitentransitardesde la nube local a la publica

  10. El Problema de lasReservas A resource is to be used in period 2 if reserved in period 1, the user pays 1 if bought in period 2, the user pays C >1 each user “knows” the probability, p, that he will need the resource in the next period. Question: can we discover p from his choices?

  11. Yes, one can design reservation contracts that are truth-telling.Or equivalently, option prices that reveal the likelihood that a burst will lead to flexing outside the private cloud.Bernardo A. Huberman, Fang Wu and Li Zhang, NetnomicsVol. 7. 27-37, (2005).US Patent No. 8,060,388

  12. Ejemplo

  13. El Problema de la Atención Como prestaratención a lo queesrelevante? E ignorar lo que no esimportante? El dilema del centro versus la periferia.

  14. Interfaces de Hoy Amazon Web Services

  15. Interfaces de Hoy HP Cloud Services

  16. Interfaces de Hoy HP HelionOpenStack Horizon Dashboard

  17. Interfaces de Hoy Rightscale

  18. Interfaces de Hoy Cisco Unified Computing System

  19. Interfaces de Hoy HP Insight Cluster Management Utility

  20. AlgoDiferente

  21. HP Labs Loom Helion management at scale

  22. Dos componentes del sistema: Como decider quemostrar(k out of n items) Como mostrarlo J. Hailpern and B.A. Huberman, Proceedings of the ACM SIGCHI Symposium on EICS (2013) Clevr: ascendiendosobre el mar de datos

  23. Quemostrar • The system has n different items • But can only present up to k items at any given time • The system is in a “state” given by a set of properties for each item • (history, importance, etc) • It is possible to compute an index to each item state. • The top list has those items with the largest indices. • Bernardo A. Huberman and Fang Wu, Advances in Complex Systems, Vol. 11,487-496 (2008)

  24. Como mostrarloClevr: a window into your data http://socialresearchers.hpl.hp.com/video/CLEVR_HP_JoshBernardo_720p.mp4

  25. Mas datos http://www.hpl.hp.com/research/scl

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