1. Scaleable Computing Jim Gray Microsoft Corporation Gray@Microsoft.com™

2. Thesis: Scaleable Servers
Commodity hardware allows new applications
New applications need huge servers
Clients and servers are built of the same “stuff”
Commodity software and
Commodity hardware
Servers should be able to
Scale up (grow node by adding CPUs, disks, networks)
Scale out (grow by adding nodes)
Scale down (can start small)
Key software technologies
Objects, Transactions, Clusters, Parallelism

3. 1987: 256 tps Benchmark 14 M$ computer (Tandem) A dozen people
False floor, 2 rooms of machines
Admin expert
Hardware experts
A 32 node processor array
Auditor
Network expert
Simulate 25,600 clients
Manager
Performance expert
OS expert
DB expert
A 40 GB disk array (80 drives)

4. 1988: DB2 + CICS Mainframe 65 tps
IBM 4391
Simulated network of 800 clients
2m$ computer
Staff of 6 to do benchmark
2 x 3725
network controllers
Refrigerator-sized
CPU
16 GB disk farm
4 x 8 x .5GB

5. 1997: 10 years later 1 Person and 1 box = 1250 tps
1 Breadbox ~ 5x 1987 machine room
23 GB is hand-held
One person does all the work
Cost/tps is 1,000x less 25 micro dollars per transaction
4x200 Mhz cpu
1/2 GB DRAM
12 x 4GB disk
Hardware expert
OS expert
Net expert
DB expert
App expert
3 x7 x 4GB
disk arrays

6. What Happened?
Moore’s law: Things get 4x better every 3 years (applies to computers, storage, and networks)
New Economics: Commodity class price/mips software $/mips k$/year mainframe , minicomputer microcomputer
GUI: Human - computer tradeoff optimize for people, not computers
mainframe
mini
micro
time
price

7. What Happens Next ? Last 10 years: 1000x improvement
1985
2005
1995
performance ?
Last 10 years: x improvement
Next 10 years: ????
Today: text and image servers are free 25 m$/hit => advertising pays for them
Future: video, audio, … servers are free “You ain’t seen nothing yet!”

8. Kinds Of Information Processing
Point-to-point
Broadcast
Lecture
Concert
Conversation
Money
Network
Immediate
Time- shifted
Mail
Book
Newspaper
Database
It’s ALL going electronic
Immediate is being stored for analysis (so ALL database)
Analysis and automatic processing are being added

9. Why Put Everything In Cyberspace?
Point-to-point OR
broadcast
Low rent -
min $/byte
Shrinks time -
now or later
Shrinks space -
here or there
Automate processing -
knowbots
Network
Immediate OR time-delayed
Locate
Process
Analyze
Summarize
Database

10. Magnetic Storage Cheaper Than Paper
File cabinet: cabinet (four drawer) 250$ paper (24,000 sheets) 250$ space 10$/ft2) 180$ total 700$ 3¢/sheet
Disk: disk (4 GB =) 800$ ASCII: 2 mil pages ¢/sheet (80x cheaper)
Image: 200,000 pages ¢/sheet (8x cheaper)
Store everything on disk

11. Databases Information at Your Fingertips™ Information Network™ Knowledge Navigator™
All information will be in an online database (somewhere)
You might record everything you
Read: 10MB/day, 400 GB/lifetime (eight tapes today)
Hear: 400MB/day, 16 TB/lifetime (three tapes/year today)
See: 1MB/s, 40GB/day, 1.6 PB/lifetime (maybe someday)

12. Database Store ALL Data Types
The old world:
Millions of objects
100-byte objects
The new world:
Billions of objects
Big objects (1 MB)
Objects have behavior (methods)
People
Name
Address
Mike
Won
David NY
Berk
Austin
Paperless office
Library of Congress online
All information online
Entertainment
Publishing
Business
WWW and Internet
People
Name
Address
Papers
Picture
Voice
David NY
Mike
Berk
Won
Austin

13. Billions Of Clients Every device will be “intelligent”
Doors, rooms, cars…
Computing will be ubiquitous

14. Billions Of Clients Need Millions Of Servers
All clients networked to servers
May be nomadic or on-demand
Fast clients want faster servers
Servers provide
Shared Data
Control
Coordination
Communication
Clients
Mobile clients
Fixed clients
Servers
Server
Super
server

15. Thesis Many little beat few big
$1 million
1 MM 3
$100 K
$10 K
Pico Processor
Micro
Nano
1 MB
10 pico-second ram
Mainframe
Mini
10 microsecond ram
10 millisecond disc
10 second tape archive
10 nano-second ram 10 MB 1
0 GB 1 TB 1
00 TB
2.5"
1.8"
3.5"
5.25"
1 M SPECmarks, 1TFLOP
106 clocks to bulk ram
Event-horizon on chip
VM reincarnated
Multiprogram cache,
On-Chip SMP
9"
14"
Smoking, hairy golf ball
How to connect the many little parts?
How to program the many little parts?
Fault tolerance?

16. Future Super Server: 4T Machine
Array of 1,000 4B machines
1 bps processors
1 BB DRAM
10 BB disks
1 Bbps comm lines
1 TB tape robot
A few megabucks
Challenge:
Manageability
Programmability
Security
Availability
Scaleability
Affordability
As easy as a single system
CPU
50 GB Disc
5 GB RAM
Cyber Brick
a 4B machine
Future servers are CLUSTERS
of processors, discs
Distributed database techniques
make clusters work

17. Performance = Storage Accesses not Instructions Executed
In the “old days” we counted instructions and IO’s
Now we count memory references
Processors wait most of the time
Where the time goes:
clock ticks used by AlphaSort Components
Disc Wait
Sort
70 MIPS
“real” apps have worse Icache misses so run at 60 MIPS
if well tuned, 20 MIPS if not
Sort OS
Disc Wait
Memory Wait
I-Cache
Miss
B-Cache
D-Cache
Data Miss
Miss

18. Storage Latency: How Far Away is the Data?
Andromeda 9 10
Tape /Optical
2,000 Years
Robot 6
Pluto 10
Disk
2 Years
Clock Ticks
Sacramento
1.5 hr
100
Memory
This Campus 10
On Board Cache
10 min 2
On Chip Cache
This Room 1
Registers
My Head
1 min

19. The Hardware Is In Place… And then a miracle occurs?
SNAP: scaleable network and platforms
Commodity-distributed OS built on:
Commodity platforms
Commodity network interconnect
Enables parallel applications

20. Thesis: Scaleable Servers
Commodity hardware allows new applications
New applications need huge servers
Clients and servers are built of the same “stuff”
Commodity software and
Commodity hardware
Servers should be able to
Scale up (grow node by adding CPUs, disks, networks)
Scale out (grow by adding nodes)
Scale down (can start small)
Key software technologies
Objects, Transactions, Clusters, Parallelism

21. Scaleable Servers BOTH SMP And Cluster
Grow up with SMP; 4xP6 is now standard
Grow out with cluster
Cluster has inexpensive parts
SMP super server
Departmental server
Personal system
Cluster of PCs

22. SMPs Have Advantages
Single system image easier to manage, easier to program threads in shared memory, disk, Net
4x SMP is commodity
Software capable of 16x
Problems:
>4 not commodity
Scale-down problem (starter systems expensive)
There is a BIGGEST one
SMP super server
Departmental server
Personal system

23. Building the Largest Node
There is a biggest node (size grows over time)
Today, with NT, it is probably 1TB
We are building it (with help from DEC and SPIN2)
1 TB GeoSpatial SQL Server database
(1.4 TB of disks = 320 drives).
30K BTU, 8 KVA, 1.5 metric tons.
Will put it on the Web as a demo app.
10 meter image of the ENTIRE PLANET.
2 meter image of interesting parts (2% of land) One pixel per meter = 500 TB uncompressed.
Better resolution in US (courtesy of USGS).
Support files
1-TB SQL Server DB Satellite and aerial
photos
Todo loo da loo-rah, ta da ta-la la la
1-TB home page TM

24. What’s TeraByte? 1 Terabyte: Library of Congress (in ASCII) is 25 TB
1,000,000,000 business letters 150 miles of book shelf
100,000,000 book pages miles of book shelf
50,000,000 FAX images miles of book shelf
10,000,000 TV pictures (mpeg) days of video 4,000 LandSat images earth images (100m)
100,000,000 web page copies of the web HTML
Library of Congress (in ASCII) is 25 TB
1980: $200 million of disc ,000 discs
$5 million of tape silo ,000 tapes
1997: $200 k$ of magnetic disc discs
$30 k$ nearline tape tapes
Terror Byte !

25. TB DB User Interface
Next

26. Tpc-C Web-Based Benchmarks
Client is a Web browser (7,500 of them!)
Submits
Order
Invoice
Query to server via Web page interface
Web server translates to DB
SQL does DB work
Net:
easy to implement
performance is GREAT!
HTTP
IIS
= Web
ODBC
SQL

27. TPC-C Shows How Far SMPs have come
Performance is amazing:
2,000 users is the min!
30,000 users on a 4x12 alpha cluster (Oracle)
Peak Performance: 30,390 $305/tpmC (Oracle/DEC)
Best Price/Perf: 6,712 $65/tpmC (MS SQL/DEC/Intel)
graphs show UNIX high price & diseconomy of scaleup

28. TPC C SMP Performance SMPs do offer speedup
but 4x P6 is better than some 18x MIPSco

29. The TPC-C Revolution Shows How Far NT and SQL Server have Come
Economy of scale on Windows NT
Recent Microsoft SQL Server benchmarks are Web-based
tpmC and $/tpmC MS
SQL Server: Economy of Scale & Low Price
$250
DB2
$200
Informix
$150
Price $/TPM-C
Better
Microsoft
$100
Oracle
$50
Sybase $0
1000
2000
3000
4000
5000
6000
7000
8000
Performance tpmC

30. What Happens To Prices? No expensive UNIX front end (20$/tpmC)
No expensive TP monitor software (10$/tpmC)
=> 65$/tpmC

31. 1 billion transactions per day
Grow UP and OUT
1 Terabyte DB
Cluster:
a collection of nodes
as easy to program and manage as a single node
SMP super server
1 billion transactions per day
Departmental server
Personal system

32. Clusters Have Advantages
Clients and servers made from the same stuff
Inexpensive:
Built with commodity components
Fault tolerance:
Spare modules mask failures
Modular growth
Grow by adding small modules
Unlimited growth: no biggest one

33. Windows NT clusters Key goals:
Easy: to install, manage, program
Reliable: better than a single node
Scaleable: added parts add power
Microsoft & 60 vendors defining NT clusters
Almost all big hardware and software vendors involved
No special hardware needed - but it may help
Enables
Commodity fault-tolerance
Commodity parallelism (data mining, virtual reality…)
Also great for workgroups!
Initial: two-node failover
Beta testing since December96
SAP, Microsoft, Oracle giving demos.
File, print, Internet, mail, DB, other services
Easy to manage
Each node can be 4x (or more) SMP
Next (NT5) “Wolfpack” is modest size cluster
About 16 nodes (so 64 to 128 CPUs)
No hard limit, algorithms designed to go further

34. SQL Server™ Failover Using “Wolfpack” Windows NT Clusters
Each server “owns” half the database
When one fails…
The other server takes over the shared disks
Recovers the database and serves it
Shared SCSI disk strings A B
Private
disks
Clients

35. How Much Is 1 Billion Transactions Per Day?
1 Btpd = 11,574 tps (transactions per second) ~ 700,000 tpm (transactions/minute)
AT&T
185 million calls (peak day worldwide)
Visa ~20 M tpd
400 M customers
250,000 ATMs worldwide
7 billion transactions / year (card+cheque) in 1994
Millions of transactions per day
1,000.
100.
Mtpd
10. 1.
0.1
AT&T
1 Btpd
Visa
BofA
NYSE

36. Billion Transactions per Day Project
Building a 20-node Windows NT Cluster (with help from Intel) > 800 disks
All commodity parts
Using SQL Server & DTC distributed transactions
Each node has 1/20 th of the DB
Each node does 1/20 th of the work
15% of the transactions are “distributed”

37. Parallelism The OTHER aspect of clusters
Clusters of machines allow two kinds of parallelism
Many little jobs: online transaction processing
TPC-A, B, C…
A few big jobs: data search and analysis
TPC-D, DSS, OLAP
Both give automatic parallelism

38. Kinds of Parallel Execution
Any
Any
Sequential
Sequential
Pipeline
Program
Program
Partition
outputs split N ways
inputs merge M ways
Any
Any
Sequential
Sequential
Program
Program
Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey 81

39. Data Rivers Split + Merge Streams
N X M Data Streams
M Consumers
N producers
River
Producers add records to the river,
Consumers consume records from the river
Purely sequential programming.
River does flow control and buffering
does partition and merge of data records
River = Split/Merge in Gamma = Exchange operator in Volcano.
Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey 82

40. Partitioned Execution
Spreads computation and IO among processors
Partitioned data gives
NATURAL parallelism
Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey 83

41. N x M way Parallelism N inputs, M outputs, no bottlenecks.
Partitioned Data
Partitioned and Pipelined Data Flows
Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey 84

42. The Parallel Law Of Computing
Grosch's Law:
Parallel Law:
Needs:
Linear speedup and linear scale-up
Not always possible
2x $ is 4x performance
1 MIPS
1 $
1,000 MIPS
32 $
.03$/MIPS
2x $ is 2x performance
1,000 MIPS
1,000 $
1 MIPS 1 $

43. Thesis: Scaleable Servers
Commodity hardware allows new applications
New applications need huge servers
Clients and servers are built of the same “stuff”
Commodity software and
Commodity hardware
Servers should be able to
Scale up (grow node by adding CPUs, disks, networks)
Scale out (grow by adding nodes)
Scale down (can start small)
Key software technologies
Objects, Transactions, Clusters, Parallelism

44. The BIG Picture Components and transactions
Software modules are objects
Object Request Broker (a.k.a., Transaction Processing Monitor) connects objects (clients to servers)
Standard interfaces allow software plug-ins
Transaction ties execution of a “job” into an atomic unit: all-or-nothing, durable, isolated
Object Request Broker

45. ActiveX and COM
COM is Microsoft model, engine inside OLE ALL Microsoft software is based on COM (ActiveX)
CORBA + OpenDoc is equivalent
Heated debate over which is best
Both share same key goals:
Encapsulation: hide implementation
Polymorphism: generic operations key to GUI and reuse
Versioning: allow upgrades
Transparency: local/remote
Security: invocation can be remote
Shrink-wrap: minimal inheritance
Automation: easy
COM now managed by the Open Group

46. Linking And Embedding Objects are data modules; transactions are execution modules
Link: pointer to object somewhere else
Think URL in Internet
Embed: bytes are here
Objects may be active; can callback to subscribers

47. Commodity Software Components Inexpensive OS, DBMS…and plug-ins
Recent TPC-C prices
Oracle on DEC UNIX: 30.4 k 305$/tpmC
Informix on DEC UNIX: k 277$/tpmC
DB2 on Solaris: $/tpmC
SQL Server on Compaq, Windows NT: $/tpmC (using Web, no TP monitor!)
Oracle on Windows NT: $/tpmC
Net: “Open” solutions can do even biggest jobs; thousands of online users per “node” of cluster
ActiveX, VBX, and Java plug-ins
Spreadsheets, GeoQuery, FAX, voice, image libraries, commodity component market

48. Objects Meet Databases The basis for universal data servers, access, & integration
object-oriented (COM oriented) programming interface to data
Breaks DBMS into components
Anything can be a data source
Optimization/navigation “on top of” other data sources
A way to componentized a DBMS
Makes an RDBMS and O-R DBMS (assumes optimizer understands objects)
Database
Spreadsheet
Photos
Mail
Map
Document
DBMS
engine

49. The Pattern: Three Tier Computing
Presentation
Clients do presentation, gather input
Clients do some workflow (Xscript)
Clients send high-level requests to ORB (Object Request Broker)
ORB dispatches workflows and business objects -- proxies for client, orchestrate flows & queues
Server-side workflow scripts call on distributed business objects to execute task
workflow
Business
Objects
Database

50. The Three Tiers DCOM (oleDB, ODBC,...) Object server Pool HTTP+ DCOM
Web Client
HTML
VB or Java
Script Engine
Virt Machine
VBscritpt
JavaScrpt
VB Java
plug-ins
Internet
ORB
HTTP+
DCOM
Object
server
Pool
Middleware
TP Monitor
Web Server...
DCOM (oleDB, ODBC,...)
Object & Data
server.
LU6.2
IBM
Legacy
Gateways

51. Why Did Everyone Go To Three-Tier?
Manageability
Business rules must be with data
Middleware operations tools
Performance (scaleability)
Server resources are precious
ORB dispatches requests to server pools
Technology & Physics
Put UI processing near user
Put shared data processing near shared data
Presentation
workflow
Business
Objects
Database

52. Why Put Business Objects at Server?
Customer comes to store with list
Gives list to clerk
Clerk gets goods, makes invoice
Customer pays clerk, gets goods
Easy to manage
Clerks controls access
Encapsulation
MOM’s Business Objects
DAD’sRaw Data
Customer comes to store
Takes what he wants
Fills out invoice
Leaves money for goods
Easy to build
No clerks

53. What Middleware Does ORB, TP Monitor, Workflow Mgr, Web Server
Registers transaction programs workflow and business objects (DLLs)
Pre-allocates server pools
Provides server execution environment
Dynamically checks authority (request-level security)
Does parameter binding
Dispatches requests to servers
parameter binding
load balancing
Provides Queues
Operator interface

54. Server Side Objects Easy Server-Side Execution
A Server
Give simple execution environment
Object gets
start
invoke
shutdown
Everything else is automatic
Drag & Drop Business Objects
Network
Receiver
Queue
Management
Connections
Context
Security
Configuration
Thread Pool
Service logic
Synchronization
Shared Data

55. A new programming paradigm
Develop object on the desktop
Better yet: download them from the Net
Script work flows as method invocations
All on desktop
Then, move work flows and objects to server(s)
Gives
desktop development
three-tier deployment
Software Cyberbricks

56. Transactions Coordinate Components (ACID)
Transaction properties
Atomic: all or nothing
Consistent: old and new values
Isolated: automatic locking or versioning
Durable: once committed, effects survive
Transactions are built into modern OSs
MVS/TM Tandem TMF, VMS DEC-DTM, NT-DTC

57. Transactions & Objects
Application requests transaction identifier (XID)
XID flows with method invocations
Object Managers join (enlist) in transaction
Distributed Transaction Manager coordinates commit/abort

58. Transactions Coordinate Components (ACID)
Programmer’s view: bracket a collection of actions
A simple failure model
Only two outcomes:
Begin()
action
Commit()
Begin()
action
Rollback()
Begin()
action
Rollback()
Fail !
Success!
Failure!

59. Distributed Transactions Enable Huge Throughput
Each node capable of 7 KtmpC (7,000 active users!)
Can add nodes to cluster (to support 100,000 users)
Transactions coordinate nodes
ORB / TP monitor spreads work among nodes

60. Distributed Transactions Enable Huge DBs
Distributed database technology spreads data among nodes
Transaction processing technology manages nodes

61. Thesis: Scaleable Servers
Scaleable Servers Built from Cyberbricks
Allow new applications
Servers should be able to
Scale up, out, down
Key software technologies
Clusters (ties the hardware together)
Parallelism: (uses the independent cpus, stores, wires
Objects (software CyberBricks)
Transactions: masks errors.

62. Computer Industry Laws (Rules of thumb)
Metcalf’s law
Moore’s first law
Bell’s computer classes (7 price tiers)
Bell’s platform evolution
Bell’s platform economics
Bill’s law
Software economics
Grove’s law
Moore’s second law
Is info-demand infinite?
The death of Grosch’s law

63. Metcalf’s Law Network Utility = Users2
How many connections can it make?
1 user: no utility
100,000 users: a few contacts
1 million users: many on Net
1 billion users: everyone on Net
That is why the Internet is so “hot”
Exponential benefit

64. Moore’s First Law XXX doubles every 18 months 60% increase per year
Micro processor speeds
Chip density
Magnetic disk density
Communications bandwidth WAN bandwidth approaching LANs
Exponential growth:
The past does not matter
10x here, 10x there, soon you’re talking REAL change
PC costs decline faster than any other platform
Volume and learning curves
PCs will be the building bricks of all future systems
128KB
128MB
2000
8KB
1MB
8MB
1GB
1970
1980
1990 1M
16M
bits: 1K 4K
16K
64K
256K 4M
64M
256M
1 chip memory size
( 2 MB to 32 MB)

65. Bumps In The Moore’s Law Road 1
100
10000
1970
1980
1990
2000
$/MB of DRAM
DRAM:
1988: United States anti-dumping rules
: ?price flat
Magnetic disk:
: 10x/decade
: 4x/3year! X/decade
.01 1
100
10,000
1970
1980
1990
2000
$/MB of DISK

66. Gordon Bell’s 1975 VAX Planning Model... He Didn’t Believe It!
System Price = 5 x 3 x .04 x memory size/ 1.26 (t-1972) K$
5x: Memory is 20% of cost 3x: DEC markup .04x: $ per byte
He didn’t believe: the projection $500 machine
He couldn’t comprehend the implications
0.01K$
0.1K$
1.K$
10.K$
100.K$
1,000.K$
10,000.K$
100,000.K$
1960
1970
1980
1990
2000
16 KB
64 KB
256 KB
1 MB
8 MB

67. Gordon Bell’s Processing Memories, And Comm 100 Years
1947
1967
1987
2007
2027
2047
Processing
Pri. Mem
Sec. Mem.
POTS(bps)
Backbone

68. Gordon Bell’s Seven Price Tiers
10$: wrist watch computers
100$: pocket/ palm computers
1,000$: portable computers
10,000$: personal computers (desktop)
100,000$: departmental computers (closet)
1,000,000$: site computers (glass house)
10,000,000$: regional computers (glass castle)
Super server: costs more than $100,000 “Mainframe”: costs more than $1 million
Must be an array of processors, disks, tapes, comm ports

69. Bell’s Evolution Of Computer Classes
Technology enables two evolutionary paths: 1. constant performance, decreasing cost 2. constant price, increasing performance ??
Time
Mainframes (central)
Minis (dep’t.)
PCs (personals)
Log price
WSs
1.26 = 2x/3 yrs x/decade; 1/1.26 = .8
1.6 = 4x/3 yrs --100x/decade; 1/1.6 = .62

70. Gordon Bell’s Platform Economics
Traditional computers: custom or semi-custom, high-tech and high-touch
New computers: high-tech and no-touch
100000
10000
Price (K$)
1000
Volume (K)
100
Application price 10 1
0.1
0.01
Mainframe WS
Browser
Computer type

71. Software Economics An engineer costs about $150,000/year
Microsoft: $9 billion
An engineer costs about $150,000/year
R&D gets [5%…15%] of budget
Need [$3 million… $1 million] revenue per engineer
Profit
24%
R&D
16%
Tax
13%
SG&A
34%
Product and Service
13%
Intel: $16 billion
IBM: $72 billion
Oracle: $3 billion
Profit 6%
Profit
15%
R&D 8%
R&D 9%
Profit
R&D 8%
Tax 5%
22%
Tax 7%
SG&A
11%
SG&A
22%
Tax
SG&A
12%
P&S
47%
P&S
59%
P&S
26%
43%

72. Software Economics: Bill’s Law
Fixed_
Cost
Price
Marginal _Cost = +
Units
Bill Joy’s law (Sun): don’t write software for less than 100,000 platforms @$10 million engineering expense, $1,000 price
Bill Gate’s law: don’t write software for less than 1,000,000 platforms @$10 engineering expense, $100 price
Examples:
UNIX versus Windows NT: $3,500 versus $500
Oracle versus SQL-Server: $100,000 versus $6,000
No spreadsheet or presentation pack on UNIX/VMS/...
Commoditization of base software and hardware

73. Grove’s Law The New Computer Industry
Horizontal integration is new structure
Each layer picks best from lower layer
Desktop (C/S) market
1991: 50%
1995: 75%
Function
Example
Operation
AT&T
Integration
EDS
Applications
SAP
Middleware
Oracle
Baseware
Microsoft
Systems
Compaq
Silicon & Oxide
Intel & Seagate

74. Moore’s Second Law
The cost of fab lines doubles every generation (three years)
Money limit hard to imagine:
$10-billion line
$20-billion line
$40-billion line
Physical limit
Quantum effects at 0.25 micron now 0.05 micron seems hard 12 years, three generations
Lithograph: need Xray below 0.13 micron $1
$10
$100
$1,000
$10,000
1960
1970
1980
1990
2000
Year
$million/ Fab Line

75. Constant Dollars Versus Constant Work
One SuperServer can do all the world’s computations
Constant dollars:
The world spends 10% on information processing
Computers are moving from 5% penetration to 50%
$300 billion to $3 trillion
We have the patent on the byte and algorithm

76. Crossing The Chasm New market Old market Old technology New technology
Product finds
customers
No product
no customers
Hard
Very hard
Old
market
Hard
Boring
competitive
slow growth
Customers
find product
Old
technology
New
technology