1

What Happens WhenWhat Happens WhenProcessingProcessing

StorageStorageBandwidth Bandwidth

are Free and Infinite?are Free and Infinite?

Jim Gray

Microsoft Research

2

OutlineOutline Hardware CyberBricks

– all nodes are very intelligent Software CyberBricks

– standard way to interconnect intelligent nodes What next?

– Processing migrates to where the power is• Disk, network, display controllers have full-blown OS

• Send RPCs to them (SQL, Java, HTTP, DCOM, CORBA) to them

• Computer is a federated distributed system.

3

A Hypothetical QuestionA Hypothetical QuestionTaking things to the limitTaking things to the limit

Moore’s law 100x per decade:– Exa-instructions per second in 30 years

– Exa-bit memory chips

– Exa-byte disks Gilder’s Law of the Telecosom

3x/year more bandwidth60,000x per decade!

– 40 Gbps per fiber today

4

Grove’s LawGrove’s Law

Link Bandwidth doubles every 100 years! Not much has happened to telephones lately Still twisted pair

5

Gilder’s Telecosom Law: Gilder’s Telecosom Law: 3x bandwidth/year for 25 more years3x bandwidth/year for 25 more years

Today: – 10 Gbps per channel– 4 channels per fiber: 40 Gbps– 32 fibers/bundle = 1.2 Tbps/bundle

In lab 3 Tbps/fiber (400 x WDM) In theory 25 Tbps per fiber 1 Tbps = USA 1996 WAN bisection bandwidth

1 fiber = 25 Tbps

6

ThesisThesisMany little beat few bigMany little beat few big

Smoking, hairy golf ballSmoking, hairy golf ball How to connect the many little parts?How to connect the many little parts? How to program the many little parts?How to program the many little parts? Fault tolerance?Fault tolerance?

$1 $1 millionmillion $100 K$100 K $10 K$10 K

MainframeMainframe MiniMiniMicroMicro NanoNano

14"14"9"9"

5.25"5.25" 3.5"3.5" 2.5"2.5" 1.8"1.8"1 M SPEC marks, 1TFLOP1 M SPEC marks, 1TFLOP

101066 clocks to bulk ram clocks to bulk ram

Event-horizon on chipEvent-horizon on chip

VM reincarnatedVM reincarnated

Multi-program cache,Multi-program cache,On-Chip SMPOn-Chip SMP

10 microsecond ram

10 millisecond disc

10 second tape archive

10 nano-second ram

Pico Processor

10 pico-second ram

1 MM 3

100 TB

1 TB

10 GB

1 MB

100 MB

7

Year 2000 Year 2000 4B4B Machine Machine The Year 2000 commodity PC

Billion Instructions/Sec

.1 Billion Bytes RAM

Billion Bits/s Net

10 B Bytes Disk

Billion Pixel display– 3000 x 3000 x 24

1,000 $

10 GB byte Disk

.1 B byte RAM

1 Bips Processor

1 B

bits

/sec

LA

N/W

AN

8

4 B PC’s: 4 B PC’s: The Bricks of CyberspaceThe Bricks of Cyberspace Cost 1,000 $ Come with

– OS (NT, POSIX,..)

– DBMS

– High speed Net

– System management

– GUI / OOUI

– Tools

Compatible with everyone else CyberBricks

9

Super Server: 4T MachineSuper Server: 4T Machine Array of 1,000 4B machinesArray of 1,000 4B machines

1 b ips processors1 b ips processors1 B B DRAM 1 B B DRAM 10 B B disks 10 B B disks 1 Bbps comm lines1 Bbps comm lines1 TB tape robot1 TB tape robot

A few megabucksA few megabucks Challenge:Challenge:

ManageabilityManageabilityProgrammabilityProgrammabilitySecuritySecurityAvailabilityAvailabilityScaleabilityScaleabilityAffordabilityAffordability

As easy as a single systemAs easy as a single systemFuture servers are CLUSTERSFuture servers are CLUSTERSof processors, discsof processors, discs

Distributed database techniquesDistributed database techniquesmake clusters workmake clusters work

CPU

50 GB Disc

5 GB RAM

Cyber BrickCyber Bricka 4B machinea 4B machine

10

Functionally Specialized CardsFunctionally Specialized Cards Storage

Network

Display

M MB DRAM

P mips processor

ASIC

ASIC

ASIC

Today:

P=50 mips

M= 2 MB

In a few years

P= 200 mips

M= 64 MB

11

It’s Already True of PrintersIt’s Already True of PrintersPeripheral = CyberBrickPeripheral = CyberBrick

You buy a printer You get a

– several network interfaces– A Postscript engine

• cpu, • memory, • software,• a spooler (soon)

– and… a print engine.

12

System On A ChipSystem On A Chip Integrate Processing with memory on one chip

– chip is 75% memory now– 1MB cache >> 1960 supercomputers– 256 Mb memory chip is 32 MB!– IRAM, CRAM, PIM,… projects abound

Integrate Networking with processing on one chip– system bus is a kind of network– ATM, FiberChannel, Ethernet,.. Logic on chip.– Direct IO (no intermediate bus)

Functionally specialized cards shrink to a chip.

13

Tera Byte Backplane

TODAY– Disk controller is 10 mips risc engine

with 2MB DRAM– NIC is similar power

SOON– Will become 100 mips systems

with 100 MB DRAM. They are nodes in a federation

(can run Oracle on NT in disk controller).

Advantages– Uniform programming model– Great tools– Security– economics (cyberbricks)– Move computation to data (minimize traffic)

All Device Controllers will be Cray 1’sAll Device Controllers will be Cray 1’s

CentralProcessor &

Memory

14

With Tera Byte InterconnectWith Tera Byte Interconnectand Super Computer Adaptersand Super Computer Adapters

Processing is incidental to – Networking– Storage– UI

Disk Controller/NIC is – faster than device– close to device– Can borrow device

package & power So use idle capacity for computation. Run app in device.

Tera ByteBackplane

15

ImplicationsImplications

Offload device handling to NIC/HBA

higher level protocols: I2O, NASD, VIA…

SMP and Cluster parallelism is important.

Tera Byte Backplane

Move app to NIC/device controller

higher-higher level protocols: CORBA / DCOM.

Cluster parallelism is VERY important.

CentralProcessor &

Memory

Conventional Radical

16

How Do They Talk to Each Other?How Do They Talk to Each Other? Each node has an OS Each node has local resources: A federation. Each node does not completely trust the others. Nodes use RPC to talk to each other CORBA? DCOM? IIOP? RMI? One or all of the above. Huge leverage in high-level interfaces. Same old distributed system story.

Wire(s)VIAL/VIPL

stre

ams

data

gram

s

RP

C?

Applications

VIAL/VIPL

streams

datagrams

RP

C ?

Applications

18

Objects!Objects!

It’s a zoo ORBs, COM, CORBA,.. Object Relationa1 Databases Objects and 3-tier computing

19

So

lari

sU

NIX

Inte

rnat

ion

al

OSFDCE

Op

en s

oft

war

e F

ou

nd

atio

n (

OS

F)

NT

ODBCXA / TX

Ob

ject

M

anag

emen

t G

rou

p (

OM

G)

CORBAOpenGroup

History and Alphabet SoupHistory and Alphabet Soup

1985

1990

1995

X/O

pen

DCE

RPC

GUIDs

IDL

DNS

Kerber

os

COM

Microsoft DCOM based on OSF-DCE TechnologyDCOM and ActiveX extend it

COM

20

The PromiseThe Promise

Objects are Software CyberBricks– productivity breakthrough (plug ins)

– manageability breakthrough (modules) Microsoft Promises Cairo

distributed objects, secure, transparent, fast invocation

IBM/Sun/Oracle/Netscape promise CORBA + Open Doc + Java Beans +

All will deliver Customers can pick the best one

Both campsShare key goals: Encapsulation: hide implementation Polymorphism: generic ops

key to GUI and reuse Uniform Naming Discovery: finding a service Fault handling: transactions Versioning: allow upgrades Transparency: local/remote Security: who has authority Shrink-wrap: minimal inheritance Automation: easy

21

The OLE-COM ExperienceThe OLE-COM Experience Macintosh had Publish & Subscribe PowerPoint needed graphs:

– plugged MS Graph in as an component. Office adopted OLE

– one graph program for all of office Internet arrived

– URLs are object references, – Office is Web Enabled right away!

Office97 smaller than Office95 because of shared components

It works!!

22

Linking And EmbeddingLinking And EmbeddingObjects are data modules;Objects are data modules;

transactions are execution modulestransactions are execution modules

Link: pointer to object somewhere else– Think URL in Internet

Embed: bytesare here

Objects may be active; can callback to subscribers

23

Objects Meet DatabasesObjects Meet Databasesbasis for basis for universaluniversal data servers, access, & integration data servers, access, & integration

DBMSDBMSengineengine

Object-oriented (COM oriented) interface to data

Breaks DBMS into components Anything can be

a data source Optimization/navigation

“on top of” other data sources

Makes an RDBMS anO-R DBMS assuming optimizer understands objects

DatabaseDatabase

SpreadsheetSpreadsheet

PhotosPhotos

MailMail

MapMap

DocumentDocument

24

The BIG PictureThe BIG PictureComponents and transactionsComponents 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 ActiveX Components are a 250M$/year business.

Object RequestObject Request BrokerBroker

25

Transaction

Object Request Broker (ORB)Object Request Broker (ORB) Orchestrates RPCOrchestrates RPC

Registers Servers Manages pools of servers Connects clients to servers Does Naming, request-level authorization, Provides transaction coordination Direct and queued invocation Old names:

– Transaction Processing Monitor,

– Web server,

– NetWare

Object-Request Broker

26

The OO Points So FarThe OO Points So Far

Objects are software Cyber Bricks Object interconnect standards are emerging Cyber Bricks become Federated Systems. Next points:

– put processing close to data

– do parallel processing.

27

Three Tier ComputingThree Tier Computing

Clients do presentation, gather input

Clients do some workflow (Xscript)

Clients send high-level requests to ORB

ORB dispatches work-flows and business objects -- proxies for client, orchestrate flows & queues

Server-side workflow scripts call on distributed business objects to execute task

Database

Business Objects

workflow

Presentation

28

The Three The Three TiersTiers

Web Client

HTML

VB or Java Script Engine

VB or Java Virt Machine

VBscritptJavaScrpt

VB Javaplug-ins

InternetORB

HTTP+DCOM

ObjectserverPool

MiddlewareORB

TP MonitorWeb Server...

DCOM (oleDB, ODBC,...)

Object & Dataserver.

LU6.2

IBMLegacy Gateways

29

Transaction Processing Transaction Processing Evolution to Three TierEvolution to Three Tier

Intelligence migrated to clientsIntelligence migrated to clients Mainframe Batch processing

(centralized)

Dumb terminals & Remote Job Entry

Intelligent terminals database backends

Workflow SystemsObject Request BrokersApplication Generators

Mainframe

cards

Active

green screen3270

Server

TP Monitor

ORB

30

Web Evolution to Three TierWeb Evolution to Three TierIntelligence migrated to clients (like TP)Intelligence migrated to clients (like TP)

Character-mode clients, smart servers

GUI Browsers - Web file servers

GUI Plugins - Web dispatchers - CGI

Smart clients - Web dispatcher (ORB)pools of app servers (ISAPI, Viper)workflow scripts at client & server

archie ghophergreen screen

WebServer

Mosaic

WAIS

NS & IE

Active

31

PC Evolution to Three TierPC Evolution to Three Tier Intelligence migrated to serverIntelligence migrated to server

Stand-alone PC (centralized)

PC + File & print servermessage per I/O

PC + Database server message per SQL statement

PC + App server message per transaction

ActiveX Client, ORB ActiveX server, Xscript

disk I/OIO request

reply

SQL Statement

Transaction

32

Why Did Everyone Go To Three-Why Did Everyone Go To Three-Tier?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

– Minimizes data moves

– Encapsulate / modularityDatabase

Business Objects

workflow

Presentation

33

DAD’sRaw Data

Customer comes to storeTakes what he wantsFills out invoiceLeaves money for goods

Easy to buildNo clerks

Why Put Business Objects at Why Put Business Objects at Server?Server?

Customer comes to store with list Gives list to clerk Clerk gets goods, makes invoiceCustomer pays clerk, gets goods

Easy to manageClerks controls accessEncapsulation

MOM’s Business Objects

34

The OO Points So FarThe OO Points So Far

Objects are software Cyber Bricks Object interconnect standards are emerging Cyber Bricks become Federated Systems. Put processing close to data Next point:

– do parallel processing.

35

Parallelism: Parallelism: the OTHER half of Super-Serversthe OTHER half of Super-Servers Clusters of machines allow two kinds of parallelism

– Many little jobs: Online transaction processing

• TPC A, B, C,…

– A few big jobs: data search & analysis• TPC D, DSS, OLAP

Both give automatic Parallelism

36

Why Parallel Access To Data?Why Parallel Access To Data?

1 Terabyte

10 MB/s

At 10 MB/s1.2 days to scan

1 Terabyte

1,000 x parallel100 second SCAN.

Parallelism: divide a big problem into many smaller ones

to be solved in parallel.

BANDWID

TH

37

Kinds of Parallel ExecutionKinds of Parallel Execution

Pipeline

Partition outputs split N ways inputs merge M ways

Any Sequential Program

Any Sequential Program

SequentialSequential

SequentialSequential Any Sequential Program

Any Sequential Program

38

Why are Relational OperatorsWhy are Relational OperatorsSuccessful for Parallelism?Successful for Parallelism?

Relational data model uniform operatorson uniform data streamClosed under composition

Each operator consumes 1 or 2 input streamsEach stream is a uniform collection of dataSequential data in and out: Pure dataflow

partitioning some operators (e.g. aggregates, non-equi-join, sort,..)

requires innovation

AUTOMATIC PARALLELISM

39

Database Systems Database Systems “Hide” Parallelism “Hide” Parallelism

Automate system management via tools– data placement– data organization (indexing)– periodic tasks (dump / recover / reorganize)

Automatic fault tolerance– duplex & failover– transactions

Automatic parallelism– among transactions (locking)– within a transaction (parallel execution)

40

SQL SQL a Non-Procedural a Non-Procedural Programming LanguageProgramming Language

SQL: functional programming language describes answer set.

Optimizer picks best execution plan– Picks data flow web (pipeline), – degree of parallelism (partitioning)– other execution parameters (process placement, memory,...)

GUI

Schema

Plan

Monitor

Optimizer

ExecutionPlanning

Rivers

Executors

41

Automatic Data Automatic Data PartitioningPartitioningSplit a SQL table to subset of nodes & disks

Partition within set:Range Hash Round Robin

Shared disk and memory less sensitive to partitioning, Shared nothing benefits from "good" partitioning

A...E F...J K...N O...S T...Z A...E F...J K...N O...S T...Z A...E F...J K...N O...S T...Z

Good for equijoins, range queriesgroup-by

Good for equijoins Good to spread load

42

N x M way ParallelismN x M way Parallelism

A...E F...J K...N O...S T...Z

Merge

Join

Sort

Join

Sort

Join

Sort

Join

Sort

Join

Sort

Merge Merge

N inputs, M outputs, no bottlenecks.

43

Parallel Objects?Parallel Objects? How does all this DB parallelism connect to

hardware/software Cyber Bricks? To scale to large client sets

– need lots of independent parallel execution.– Comes for from from ORB.

To scale to large data sets– need intra-program parallelism (like parallel DBs)– Requires some invention.

44

OutlineOutline Hardware CyberBricks

– all nodes are very intelligent Software CyberBricks

– standard way to interconnect intelligent nodes What next?

– Processing migrates to where the power is• Disk, network, display controllers have full-blown OS

• Send RPCs to them (SQL, Java, HTTP, DCOM, CORBA) to them

• Computer is a federated distributed system.

• Parallel execution is important

45

MORE SLIDESMORE SLIDESbut there is only so but there is only so

much time.much time.

Too bad

46

The Disk Farm On a CardThe Disk Farm On a CardThe Disk Farm On a CardThe Disk Farm On a CardThe 100GB disc cardAn array of discsCan be used as 100 discs 1 striped disc 10 Fault Tolerant discs ....etcLOTS of accesses/second bandwidth

14"

Life is cheap, its the accessories that cost ya.

Processors are cheap, it’s the peripherals that cost ya (a 10k$ disc card).

47

Parallelism: Parallelism: Performance is the GoalPerformance is the Goal

Goal is to get 'good' performance. Trade time for money.

Law 1: parallel system should be faster than serial system

Law 2: parallel system should give near-linear scaleup or

near-linear speedup orboth.

Parallel DBMSs obey these laws

48

Success StoriesSuccess Stories Online Transaction Processing

– many little jobs

– SQL systems support

• 50 k tpm-C (44 cpu, 600 disk 2 node )

Batch (decision support and Utility)– few big jobs, parallelism inside

– Scan data at 100 MB/s

– Linear Scaleup to 1,000 processors

tran

sact

ion

s /

sec

hardware

recs

/ se

c

hardware

49

The New Law of ComputingThe New Law of Computing

Grosch's Law:

Parallel Law: Needs

Linear Speedup and Linear ScaleupNot always possible

1 MIPS1 $

1,000 $

1,000 MIPS

2x $ is 2x performance

1 MIPS1 $

1,000 MIPS 32 $.03$/MIPS

2x $ is 4x performance

50

Clusters being builtClusters being built Teradata 1,000 nodes

(30k$/slice) Tandem,VMScluster 150 nodes (100k$/slice) Intel, 9,000 nodes @ 55M$ (

6k$/slice) Teradata, Tandem, DEC

moving to NT+low slice price IBM: 512 nodes ASCI @ 100m$ (200k$/slice) PC clusters (bare handed) at dozens of nodes

web servers (msn, PointCast,…), DB servers KEY TECHNOLOGY HERE IS THE APPS.

– Apps distribute data– Apps distribute execution

52

BOTH SMP and BOTH SMP and Cluster?Cluster?

SMPSuper Server

DepartmentalServer

PersonalSystem

Grow Up with SMPGrow Up with SMP4xP6 is now standard4xP6 is now standard

Grow Out with ClusterGrow Out with Cluster

Cluster has inexpensive partsCluster has inexpensive parts

Clusterof PCs

53

Clusters Have AdvantagesClusters 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

54

Meta-Message:Meta-Message: Technology Ratios Are Important Technology Ratios Are Important

Meta-Message:Meta-Message: Technology Ratios Are Important Technology Ratios Are Important

If everything gets faster & cheaper at the same rate THEN nothing really

changes.

Things getting MUCH BETTER:

– communication speed & cost 1,000x– processor speed & cost 100x– storage size & cost 100x

Things staying about the same– speed of light (more or less constant)– people (10x more expensive)– storage speed (only 10x better)

55

Storage Ratios ChangedStorage Ratios Changed 10x better access time 10x more bandwidth 4,000x lower media price DRAM/DISK 100:1 to 10:10 to 50:1

Disk Performance vs Time

1

10

100

1980 1990 2000

Year

seek

s p

er s

eco

nd

ban

dw

idth

: MB

/s

0.1

1.

10.

Cap

acity

(GB

)

Disk accesses/second vs Time

1

10

100

1980 1990 2000

Year

Acc

esse

s p

er S

eco

nd

Storage Price vs TimeMegabytes per kilo-dollar

0.1

1.

10.

100.

1,000.

10,000.

1980 1990 2000

Year

MB

/k$

59

Performance = Storage AccessesPerformance = Storage Accesses not Instructions Executed 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

SortDisc Wait SortDisc WaitDisc Wait OS

Memory WaitMemory Wait

D-Cache Miss

I-Cache MissB-CacheB-Cache

Data MissData Miss

70 MIPS“real” apps have worse Icache misses so run at 60 MIPSif well tuned, 20 MIPS if not

60

Storage Latency: Storage Latency: How Far Away is the Data?How Far Away is the Data?

RegistersOn Chip CacheOn Board Cache

Memory

Disk

12

10

100

Tape /Optical Robot

10 9

106

Sacramento

This CampusThis Room

My Head

10 min

1.5 hr

2 Years

1 min

Pluto

2,000 YearsAndromdeda

Clo

ck T

icks

61

Tape Farms for Tertiary StorageTape Farms for Tertiary StorageNot Mainframe SilosNot Mainframe Silos

Tape Farms for Tertiary StorageTape Farms for Tertiary StorageNot Mainframe SilosNot Mainframe Silos

Scan in 27 hours.many independent tape robots(like a disc farm)

10K$ robot 14 tapes500 GB 5 MB/s 20$/GB 30 Maps

100 robots

50TB 50$/GB 3K Maps

27 hr Scan

1M$

62

0.01

0.1

1

10

100

1,000

10,000

100,000

1,000,000

1000 x Disc Farm STC Tape Robot 6,000 tapes, 8 readers

100x DLT Tape Farm

GB/K$

Maps

SCANS/Day

Kaps

The Metrics: The Metrics: Disk and Tape Farms Win Disk and Tape Farms Win

The Metrics: The Metrics: Disk and Tape Farms Win Disk and Tape Farms Win

Data Motel:Data checks in, but it never checks out

63

Tape & Optical: Tape & Optical: Beware of the Beware of the Media MythMedia Myth

Tape & Optical: Tape & Optical: Beware of the Beware of the Media MythMedia Myth

Optical is cheap: 200 $/platter 2 GB/platter => 100$/GB (2x cheaper than disc)

Tape is cheap: 30 $/tape 20 GB/tape => 1.5 $/GB (100x cheaper than disc).

64

Tape & Optical Tape & Optical RealityReality: : Media is 10% of System CostMedia is 10% of System Cost

Tape & Optical Tape & Optical RealityReality: : Media is 10% of System CostMedia is 10% of System CostTape needs a robot (10 k$ ... 3 m$ ) 10 ... 1000 tapes (at 20GB each) => 20$/GB ... 200$/GB

(1x…10x cheaper than disc)

Optical needs a robot (100 k$ ) 100 platters = 200GB ( TODAY ) => 400 $/GB

( more expensive than mag disc ) Robots have poor access times Not good for Library of Congress (25TB) Data motel: data checks in but it never checks out!

65

The Access Time MythThe Access Time MythThe Access Time MythThe Access Time MythThe Myth: seek or pick time dominatesThe reality: (1) Queuing dominates (2) Transfer dominates BLOBs (3) Disk seeks often shortImplication: many cheap servers

better than one fast expensive server– shorter queues– parallel transfer– lower cost/access and cost/byte

This is now obvious for disk arraysThis will be obvious for tape arrays

Seek

Rotate

Transfer

Seek

Rotate

Transfer

Wait

66

Billions Of Clients Billions Of Clients

Every device will be “intelligent” Doors, rooms, cars… Computing will be ubiquitous

67

Billions Of ClientsBillions Of ClientsNeed Millions Of ServersNeed Millions Of Servers

MobileMobileclientsclients

FixedFixedclients clients

ServerServer

SuperSuperserverserver

ClientsClients

ServersServers

All clients networked All clients networked to serversto servers May be nomadicMay be nomadic

or on-demandor on-demand Fast clients wantFast clients wantfasterfaster servers servers

Servers provide Servers provide Shared DataShared Data ControlControl CoordinationCoordination CommunicationCommunication

68

1987: 256 tps Benchmark 1987: 256 tps Benchmark 14 M$ computer (Tandem) A dozen people False floor, 2 rooms of machines

Simulate 25,600 clients

A 32 node processor array

A 40 GB disk array (80 drives)

OS expert

Network expert

DB expert

Performance expert

Hardware experts

Admin expert

Auditor

Manager

69

1988: DB2 + CICS Mainframe1988: DB2 + CICS Mainframe65 tps65 tps

IBM 4391 Simulated network of 800 clients 2m$ computer Staff of 6 to do benchmark

2 x 3725 network controllers

16 GB disk farm4 x 8 x .5GB

Refrigerator-sizedCPU

70

1997: 10 years later1997: 10 years later1 Person and 1 box = 1250 tps1 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 transaction4x200 Mhz cpu1/2 GB DRAM12 x 4GB disk

Hardware expertOS expertNet expertDB expertApp expert

3 x7 x 4GB disk arrays

71

What Happened?What Happened? Moore’s law:

Things get 4x better every 3 years (applies to computers, storage, and networks)

New Economics: Commodityclass price/mips software $/mips k$/yearmainframe 10,000 100 minicomputer 100 10microcomputer 10 1

GUI: Human - computer tradeoffoptimize for people, not computers

mainframeminimicro

time

pric

e

72

What Happens NextWhat Happens Next

Last 10 years: 1000x improvement

Next 10 years: ????

Today: text and image servers are free

25 $/hit => advertising pays for them Future:

video, audio, … servers are free“You ain’t seen nothing yet!”

1985 20051995

perf

orm

ance

73

Smart Cards Smart Cards Smart Cards Smart Cards

Bull CP8 two chip card first public demonstration 1979

Then (1979)

EMV card with dynamic authentication(EMV=Europay, MasterCard, Visa standard)

door key, vending machines, photocopiers

Now (1997)

Courtesy of Dennis Roberson NCR.

74

Smart Card Smart Card Memory CapacityMemory Capacity

ApplicationsApplications

Cards will be able to storedata (e.g. medical)books, movies,…money

Source: PIN/Card -Tech/ Courtesy of Dennis Roberson NCR

1990 1992 1996 1998 2000 2002

Mem

ory

Siz

e (B

its) 300 M

1 M

3 K

10 K

You are here

2004

16 KB todaybut growing super-exponentially