Deck presented at the 2010 SOA & Cloud Symposium

1. Scale as a Competitive Advantage David Chou david.chou@microsoft.com blogs.msdn.com/dachou

2. The age of “big data” 2009: 600K photos served /sec 2010: ~1PB / 60 minutes (projected) 2008: ~1B views / day Source: Wired Magazine: Issue 16.07, 2008.06.23; illustration by Marian Bantjes http://www.wired.com/science/discoveries/magazine/16-07/pb_intro

3. “More is different” Infinite storage. Clouds of processors. Our ability to capture, warehouse, and understand massive amounts of data is changing science, medicine, business, and technology. As our collection of facts and figures grows, so will the opportunity to find answers to fundamental questions. Because in the era of big data, more isn't just more. More is different. Source: Wired Magazine: Issue 16.07, 2008.06.23 http://www.wired.com/science/discoveries/magazine/16-07/pb_intro

4. “The future belongs to the companies and people that turn data into products” Source: “What is data science?”, An O’Reilly Radar Report, 2010.06.02, Mike Loukides http://radar.oreilly.com/2010/06/what-is-data-science.html

5. Working with data at scale 45M tweets pattern visualization in minutes #justinbieber cluster #teaparty cluster …. “political world has more connective tissue than of-the-moment entertainment” Source: “Data science democratize”, 2010.07.01, Mac Slocum http://radar.oreilly.com/2010/07/data-science-democratized.html

6. Big data needs big processing Facebook (2009) +200B pageviews /month >3.9T feed actions /day +300M active users >1B chat mesgs /day 100M search queries /day >6B minutes spent /day (ranked #2 on Internet) +20B photos, +2B/month growth 600,000 photos served /sec 25TB log data /day processed thru Scribe 120M queries /sec on memcache Twitter (2009) 600 requests /sec avg 200-300 connections /sec; peak at 800 MySQL handles 2,400 requests /sec 30+ processes for handling odd jobs process a request in 200 milliseconds in Rails average time spent in the database is 50-100 milliseconds +16 GB of memcached Google (2007) +20 petabytes of data processed /day by +100K MapReduce jobs 1 petabyte sort took ~6 hours on ~4K servers replicated onto ~48K disks +200 GFS clusters, each at 1-5K nodes, handling +5 petabytes of storage ~40 GB /sec aggregate read/write throughput across the cluster +500 servers for each search query < 500ms >1B views / day on Youtube (2009) Myspace(2007) 115B pageviews /month 5M concurrent users @ peak +3B images, mp3, videos +10M new images/day 160 Gbit/sec peak bandwidth Flickr (2007) +4B queries /day +2B photos served ~35M photos in squid cache ~2M photos in squid’s RAM 38k req/sec to memcached (12M objects) 2 PB raw storage +400K photos added /day Source: multiple articles, High Scalability http://highscalability.com/

7. Bing Maps Big data collection and processing flying planes over nearly every inch of the United States on road photos 45-degree low-altitude aerial photos high altitude plane photos satellite photos 10% done (August 2010) previous “all USA” flight image gathering exercise took 10 years 5PB storage and thousands of servers in one container Source: “Map Wars (visiting Bing’s imaging center)”, 2010.08.10, Robert Scoble http://scobleizer.com/2010/08/10/map-wars-visiting-bings-imaging-center/

8. Cloud computing Characteristics On-demand self-service Broad network access Resource pooling Rapid elasticity Measured service Service models Software as a service Platform as a service Infrastructure as a service Deployment models Private cloud Community cloud Public cloud Hybrid cloud “Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.” Source: The NIST Definition of Cloud Computing, Version 15, 2009.10.07, Peter Mell and Tim Grance http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc

9. Cloud levels the playing field 2007 founded by 6 people 2008 $29M funding from VC 2009 revenue - $270M $180M funding from Digital Sky Technologies 2010 1,200+ employees $300M funding from Google and Softbank Active unique players 215M monthly; 10% of world internet population (updated 2010.10); 60M daily 1M daily 4 days after launch; 10M after 60 days 3B neighborhood connections Cloud infrastructure 12,000 Amazon EC2 nodes Adding 1,000 servers per week (updated 2010.10) Moving 1PB data per day (updated 2010.10) 3 Gigabits/sec of traffic between FarmVille and Facebook (at peak) caching cluster serves another 1.5 Gigabits/sec to the application Source(s): “How FarmVille Scales to Harvest 75 Million Players a Month”, HighScalability.com, 2010.02.08, Tedd Hoff “Zynga Moves 1 Petabyte Of Data Daily; Adds 1,000 Servers A Week”, TechCrunch.com, 2010.09.22, LeenaRao

10. Cloud as a platform Utility computing on-demand infrastructure self-provisioning and servicing rapid elasticity economy of scale operational expenditures Infrastructure-as-a-Service Service delivery model … but cloud computing != cloud hosting

11. Cloud as a platform Native cloud applications horizontal scaling (scale-out) parallelization shared-nothing architecture partitioned data (sharding) multi-tenancy failure resilient (or fail-in-place) service-oriented federated composition Platform-as-a-Service Application development model

12. Service delivery models (On-Premise) Infrastructure (as a Service) Platform (as a Service) Software (as a Service) You manage Applications Applications Applications Applications You manage Data Data Data Data Runtime Runtime Runtime Runtime Managed by vendor Middleware Middleware Middleware Middleware You manage Managed by vendor O/S O/S O/S O/S Managed by vendor Virtualization Virtualization Virtualization Virtualization Servers Servers Servers Servers Storage Storage Storage Storage Networking Networking Networking Networking

16. Flickr (from Cal Henderson, then Director of Engineering at Yahoo, 2007) Web Frontend Apps & Services Distributed Storage Distributed Cache Partitioned Data

17. SlideShare(from John Boutelle, CTO at Slideshare, 2008) Web Frontend Apps & Services Distributed Cache Partitioned Data Distributed Storage

18. Twitter (from John Adams, Ops Engineer at Twitter, 2010) Web Frontend Apps & Services Partitioned Data Queues Async Processes Distributed Cache Distributed Storage

19. Distributed Storage Facebook (from Jeff Rothschild, VP Technology at Facebook, 2009) 2010 stats (Source: http://www.facebook.com/press/info.php?statistics) People +500M active users 50% of active users log on in any given day people spend +700B minutes /month Activity on Facebook +900M objects that people interact with +30B pieces of content shared /month Global Reach +70 translations available on the site ~70% of users outside the US +300K users helped translate the site through the translations application Platform +1M developers from +180 countries +70% of users engage with applications /month +550K active applications +1M websites have integrated with Facebook Platform +150M people engage with Facebook on external websites /month Web Frontend Apps & Services Distributed Cache Parallel Processes Partitioned Data Async Processes

20. Cloud computing as a new paradigm Scale-out architecture + distributed computing small logical units of work loosely-coupled processes stateless event-driven design optimistic concurrency partitioned data redundancy fault-tolerance re-try-based recoverability parallel tasks app server web data store app server web data store web app server data store app server web data store app server web data store app server web data store async tasks

21. Strategic advantages of cloud computing cost reduction cost reduction time to market pay by use ability to scale

22. What’s next? Data data federation data purification data democratization derived intelligence Process Web as a platform federated applications adaptive agents

23. Thank you! David Chou david.chou@microsoft.com blogs.msdn.com/dachou © 2010 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.