The document discusses the transition from workgroup-centric computing architectures to network-centric architectures. It describes how Google has developed a network-centric model based on 4 key principles: 1) building a protected information network, 2) offering universal connectivity, 3) maintaining shared data models, and 4) requiring continued upgrading and innovation. The network-centric model moves labor, capital and control to centralized networks rather than individual workgroups and users. This allows for more efficient, scalable and secure systems.

1. A Model for the Systems
Architecture of the Future
Prof. Paul A. Strassmann
George Mason University, December 5, 2005
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2. Data-Centric Era; IBM Dominates
Hundred Sources
1950-1980
Months⇒Weeks
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3. Workgroup-Centric Era; Microsoft, INTEL Dominate
Million Sources
Hundred Sources
1950-1980 1980-2010
Weeks⇒Days
Months⇒Weeks
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4. Network-Centric Era; Google and Cisco?
+Multi-Media
+Text Billions Sources
Data
Million Sources
Hundred Sources
1950-1980 1980-2010 2010-
Days⇒Real-Time
Weeks⇒Days
Months⇒Weeks
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5. Example of a Network-Centric System
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6. Network-Centric Requirements (2010)
• Downtime (< 5 min/yr);
• Display (200 Billion ops/sec);
• Connectivity (> 1 Gigabyte/sec);
• Access (< 0.25 sec);
• Innovation (< 1 day);
• Security (> 8 sigma).
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7. Performance (2005)
• Infrastructure = > 50% of spending;
• Security = ?;
• Integration = > 50% of applications;
• Network downtime = > 1 hour/year;
• Innovation = > 1 year.
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8. Conclusion
• Network-Centric systems cannot be
built on Workgroup-Centric architecture.
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9. Network-Centric Principles (Google)
1. Build & operate protected information
network;
2. Offer universal connectivity for:
– Collection, processing and storing of
information;
– Provide secured communications.
3. Maintain shared data models;
4. Require continued upgrading & innovation.
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10. Google Principle #1
Build & operate protected information network
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11. Standard Google Clusters Operate Net
• 359 racks
• 31,654 machines
• 63,184 CPUs
• 126,368 Ghz of processing power
• 63,184 Gb of RAM
• 2,527 Tb of Hard Drive space
• Appx. 40 million searches/day
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12. Clusters Have Identical Architecture
Index
Servers
Web Web
Switch Servers
Document
Servers
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13. Google Cluster Set-Up = Three Days
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14. Google Infrastructure: Key to Growth (1)
• >200,000 custom-built commodity servers;
• Acting as one parallel supercomputer;
• Fault tolerant hardware.
• Storage capacity >5 petabytes; low response latency
(0.2 sec); >80GB per server, distributed;
• Indexed >8 billion web pages; Indexing is
computationally complex (>500M * > 2B matrix)
• Capital and operating costs at fraction of large scale
commercial servers; traffic growth 20-30%/month; data
centers (>12); in US, Europe and Asia.
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15. Google Infrastructure: Key to Growth (2)
• >200,000 commodity Linux servers built to custom
specifications; distributed cluster architecture; acting as one
parallel supercomputer; scaleable;
• >50,000 requests/sec; fault tolerant (no single point of failure);
diverse hardware; stripped version of Red Hat;
• Storage capacity >5 petabytes;
• >80GB per server;
• Indexed >8 billion web pages;
• Indexing is complex (500M x 2B matrix)
• Capital and operating costs at fraction of large scale commercial
servers; traffic growth 20-30%/month; data centers (>12); in US,
Europe and Asia.
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16. Google Infrastructure: Key to Growth (3)
• >200,000 commodity Linux servers built to custom
specifications; distributed cluster architecture; acting as one
parallel supercomputer; scaleable;
• >50,000 requests/sec; fault tolerant (no single point of failure);
diverse hardware; stripped version of Red Hat;
• Storage capacity >5 petabytes; low response latency (0.2 sec);
>80GB per server, distributed;
• Indexed >8 billion web pages; Indexing is computationally
complex (>500M * > 2B matrix)
• Capital and operating costs a fraction of commercial servers;
• Traffic growth 20-30%/month;
• Data centers (>20), in US, Europe and Asia.
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17. Architecture for Reliability
• Replication (3x+) for redundancy;
• Replication for proximity and response;
• Reliability with software and architecture,
not with hardware.
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18. Indexing for Response
• Dynamic indexing of 8B+ pages;
• Dynamic indexing of 1B+ images;
• Indexing of 1B+ messages;
• Index broken into “shards” and
distributed across data centers.
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19. Query Serving Infrastructure
• Processing a single query may involve
1000+ servers;
• Index Servers access Index Shards;
• Document Servers access Doc Shards;
• Response times monitored to assure
<0.25 sec latency.
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20. Google MapReduce System (1)
• Coordinates servers in real-time;
• Automates distribution of workload;
• Fault tolerance and service reconstitution;
• Systems-wide I/O cluster scheduling;
• Status and performance monitoring.
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21. Google MapReduce System (2)
• Coordination of servers in real-time;
• Automates distribution of workload;
• Fault tolerance & service reconstitution;
• Systems-wide cluster scheduling;
• Status and performance monitoring.
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22. Google Principle #2
Universal connectivity
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23. Multi-Lingual Services
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24. Search in Arabic Media
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25. Video Searches
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26. Google Base - Connecting Diverse Sources
Locate events within
45 miles of New York in
November, 2005
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27. Semantic Parsing
• Tools parse millions of documents;
• Automated learning for related information.
– Query: “Bay Area Cooking Classes”
– Finds: “San Francisco College Classes”;
“The Magic of Thai Cuisine”
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28. Google Principle #3
Shared data models
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29. Data Engineering
• Standard file management: The Google
File System (GFS);
• Standard job scheduling: The Global Work
Queue (GWQ);
• Standard network management: The
Google MapReduce system.
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30. Google File System (GFS)
• Replicated Masters manage MetaData
directories;
• Data transfers directly at the machine
level within 2,000+ clusters;
• File broken into 64 MB chunks for
2000+ MB/second read/write load;
• All file chunks at least triplicate for
safety.
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31. Data Dictionary for Interoperability
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32. Application Interfaces
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33. Google Principle #4
Upgrading & Innovation
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34. Deliver On-Line Services
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35. Shopping Services
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36. Environment for Rapid Innovation
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37. A New Application Launched in 15 Minutes
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38. Occupy the Desktop
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39. Multimedia Services
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40. Workgroup vs. Network Architectures
Comparison Summaries
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41. Workgroup Computing Today:
Millions of Local Applications+Local Data
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42. Work-Groups Vulnerable Today
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43. New Internet: Billions of Browsers,
Secure Shared Applications+Data
Browsers
Application
Application
Application 43
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44. Workgroup vs. Network Architectures (1)
Workgroup Centric Network Centric
Strategy: Capture Desktop Strategy: Occupy Internet
Customer’s labor and capital Labor and capital in network
User-specific infrastructures Infrastructure is universal
Systems controls by user Network controls in network
Operating system dependency Open source browsers
License Software Pay for Use
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45. Workgroup vs. Network Architectures (2)
Workgroup Centric Network Centric
Strategy: Capture Desktop Strategy: Occupy Internet
Customer’s labor and capital Labor and capital in network
User-specific infrastructures Infrastructure is universal
Systems controls by user Network controls in network
Operating system dependency Open source browsers
License Software Pay for Use
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46. Workgroup vs. Network Architectures (3)
Workgroup Centric Network Centric
Strategy: Capture Desktop Strategy: Occupy Internet
Customer’s labor and capital Labor and capital in network
User-specific infrastructures Infrastructure is universal
Systems controls by user Network controls in network
Operating system dependency Open source browsers
License Software Pay for Use
46
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47. Workgroup vs. Network Architectures (4)
Workgroup Centric Network Centric
Strategy: Capture Desktop Strategy: Occupy Internet
Customer’s labor and capital Labor and capital in network
User-specific infrastructures Infrastructure is universal
Systems controls by user Network controls in network
Operating system dependency Open source browsers
License Software Pay for Use
47
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48. Workgroup vs. Network Architectures (5)
Workgroup Centric Network Centric
Strategy: Capture Desktop Strategy: Occupy Internet
Customer’s labor and capital Labor and capital in network
User-specific infrastructures Infrastructure is universal
Systems controls by user Network controls in network
Operating system dependent Open source browsers
License Software Pay for Use
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49. Workgroup vs. Network Architectures (6)
Workgroup Centric Network Centric
Strategy: Capture Desktop Strategy: Occupy Internet
Customer’s labor and capital Labor and capital in network
User-specific infrastructures Infrastructure is universal
Systems controls by user Network controls in network
License Software Pay for Use
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50. Workgroup vs. Network Architectures (7)
Workgroup Centric Network Centric
Strategy: Capture Desktop Strategy: Occupy Internet
Customer’s labor and capital Labor and capital in network
User-specific infrastructures Infrastructure is universal
Systems controls by user Network controls in network
License Software Pay for Use
Data read from files Data assembled in context
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51. The Future
Technology
• All electronic devices on Internet.
• Data, voice, video, sensor inputs accessible.
• Phone, TV and print media displaced.
Services
• Systems respond to questions.
• Information is displayed in context.
• Applications for decision-making.
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52. Relevance for National Security Systems
• Workgroups to Network-Centric services.
• Migrate through displacement.
• Invest savings in innovation.
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