Lecture 10a IO Introduction: Storage Devices, Metrics,

1
Lecture 10a I/O Introduction Storage Devices,
Metrics, Productivity

• Pradondet Nilagupta
• Original note from
• Professor David A. Patterson
• Spring 2001

2
I/O

• Formerly the orphan in the architecture domain
• Before
• I/O meant the non-processor and memory stuff
• Disk, tape, LAN, WAN, etc.
• Performance was not a major concern
• Devices characterized as extraneous,
  non-priority, infrequently used, slow
• Exception is the swap area of the disk
• Part of the memory hierarchy
• Part of system performance, but youre in trouble
  if you use it often
• A BIG deal for business applications, sometimes
  critical in scientific apps.

3
I/O Now

• Lots of flavors
• Secondary storage
• Disks, tapes, CD ROM, DVD
• Communication
• Networks
• Human Interface
• Video, audio, keyboards
• Multimedia!
• Real World interfaces.
• Temperature, pressure, position, velocity,
  voltage, current
• Digital Signal Processing (DSP)

4
I/O Trends

• Trends
• market is communications crazy
• voice response transaction systems, real-time
  video
• multimedia expectations
• creates many modes - continuous/fragmented,
  common/rare, dedicated/interoperable
• diversity is even more chaotic than before
• single bucket I/O didnt work before - worse now
• even standard networks come in gigabit/sec
  flavors
• for multicomputers
• I/O is the bottleneck
• Result
• significant focus on system bus performance
• common bridge to the memory system and the I/O
  systems
• critical performance component for the SMP server
  platforms

5
System vs. CPU Performance

• Care about speed at which user jobs get done
• throughput - how many jobs/time
• latency - how quick for a single job
• CPU performance main factor when
• job mix is fits in the memory
• namely there are very few page faults
• I/O performance main factor when
• the job is too big for the memory - paging is
  dominant
• when the job involves lots of unexpected files
• OLTP
• database
• almost every commercial application you can
  imagine
• and then there is graphics specialty I/O
  devices

6
System Performance

• depends on lots of things in the worst case
• CPU
• compiler
• operating System
• cache
• main Memory
• memory-IO bus
• I/O controller or channel
• I/O drivers and interrupt handlers
• I/O devices there are many types
• level of autonomous behavior
• amount of internal buffer capacity
• device specific parameters for latency and
  throughput

7
Motivation Who Cares About I/O?

• CPU Performance 60 per year
• I/O system performance limited by mechanical
  delays (disk I/O)
• Amdahl's Law system speed-up limited by the
  slowest part!
• 10 IO 10x CPU 5x Performance (lose
  50)
• 10 IO 100x CPU 10x Performance (lose 90)
• I/O bottleneck
• Diminishing fraction of time in CPU
• Diminishing value of faster CPUs

8
Storage System Issues

• Historical Context of Storage I/O
• Secondary and Tertiary Storage Devices
• Storage I/O Performance Measures
• Processor Interface Issues
• A Little Queuing Theory
• Redundant Arrarys of Inexpensive Disks (RAID)
• I/O Buses

9
I/O Systems
interrupts
Processor
Cache
Memory - I/O Bus
Main Memory
I/O Controller
I/O Controller
I/O Controller
Graphics
Disk
Disk
Network
10
Keys to a Balanced System

• Its all about overlap of I/O vs. CPU
• Timeworkload TimeCPU TimeI/O Timeoverlap
• Consider the effect of speeding up just one
• Latency vs. Bandwidth

11
I/O System Design Considerations

• Depends on type of I/O device
• size, bandwidth, and type of transaction
• frequency of transaction
• defer vs. do now
• Appropriate memory bus utilization
• What should the controller do
• programmed I/O
• interrupt vs. polled
• priority or not
• DMA
• buffering issues - what happens on over-run
• protection
• validation

12
Types of I/O Devices

• Consider
• Behavior
• Read, Write, Both, once vs. multiple
• size of average transaction
• bandwidth
• latency
• Partner - the speed of the slowest link theory
• human operated (interactive or not)
• machine operated (local or remote)

13
Some I/O Devices
14
Is I/O Important?

• Depends on your application
• business - disks for file system I/O
• graphics - graphics cards or specialty
  co-processors
• parallelism - the communications fabric
• Our focus mainline uniprocessing
• storage subsystems
• networks
• Noteworthy Point
• the traditional orphan
• but now often viewed more as a front line topic

15
Technology Trends
Disk Capacity now doubles every 18
months before 1990 every 36 motnhs
Today Processing Power Doubles Every 18
months  Today Memory Size Doubles Every 18
months(4X/3yr)  Today Disk Capacity Doubles
Every 18 months  Disk Positioning Rate (Seek
Rotate) Doubles Every Ten Years!
The I/O GAP
16
Storage Technology Drivers

• Driven by the prevailing computing paradigm
• 1950s migration from batch to on-line processing
• 1990s migration to ubiquitous computing
• computers in phones, books, cars, video cameras,
  
• nationwide fiber optical network with wireless
  tails
• Effects on storage industry
• Embedded storage
• smaller, cheaper, more reliable, lower power
• Data utilities
• high capacity, hierarchically managed storage

17
Historical Perspective

• 1956 IBM Ramac early 1970s Winchester
• Developed for mainframe computers, proprietary
  interfaces
• Steady shrink in form factor 27 in. to 14 in.
• 1970s developments
• 5.25 inch floppy disk formfactor (microcode into
  mainframe)
• early emergence of industry standard disk
  interfaces
• ST506, SASI, SMD, ESDI
• Early 1980s
• PCs and first generation workstations
• Mid 1980s
• Client/server computing
• Centralized storage on file server
• accelerates disk downsizing 8 inch to 5.25 inch
• Mass market disk drives become a reality
• industry standards SCSI, IPI, IDE
• 5.25 inch drives for standalone PCs, End of
  proprietary interfaces

18
Disk History
Data density Mbit/sq. in.
Capacity of Unit Shown Megabytes
1973 1. 7 Mbit/sq. in 140 MBytes
1979 7. 7 Mbit/sq. in 2,300 MBytes
source New York Times, 2/23/98, page C3,
Makers of disk drives crowd even mroe data into
even smaller spaces
19
Historical Perspective

• Late 1980s/Early 1990s
• Laptops, notebooks, (palmtops)
• 3.5 inch, 2.5 inch, (1.8 inch formfactors)
• Formfactor plus capacity drives market, not so
  much performance
• Recently Bandwidth improving at 40/ year
• Challenged by DRAM, flash RAM in PCMCIA cards
• still expensive, Intel promises but doesnt
  deliver
• unattractive MBytes per cubic inch
• Optical disk fails on performace (e.g., NEXT) but
  finds niche (CD ROM)

20
Disk History
1989 63 Mbit/sq. in 60,000 MBytes
1997 1450 Mbit/sq. in 2300 MBytes
1997 3090 Mbit/sq. in 8100 MBytes
source New York Times, 2/23/98, page C3,
Makers of disk drives crowd even mroe data into
even smaller spaces
21
MBits per square inch DRAM as of Disk over
time
9 v. 22 Mb/si
470 v. 3000 Mb/si
0.2 v. 1.7 Mb/si
source New York Times, 2/23/98, page C3,
Makers of disk drives crowd even mroe data into
even smaller spaces
22
Alternative Data Storage Technologies Early 1990s

• Cap BPI TPI BPITPI Data Xfer Access
• Technology (MB) (Million) (KByte/s) Time
• Conventional Tape
• Cartridge (.25") 150 12000 104 1.2
  92 minutes
• IBM 3490 (.5") 800 22860 38 0.9 3000 seconds
• Helical Scan Tape
• Video (8mm) 4600 43200 1638 71 492 45 secs
• DAT (4mm) 1300 61000 1870 114 183 20 secs
• Magnetic Optical Disk
• Hard Disk (5.25") 1200 33528 1880 63 3000 18 ms
• IBM 3390 (10.5") 3800 27940 2235 62 4250 20 ms
• Sony MO (5.25") 640 24130 18796 454 88 100 ms

23
Devices Magnetic Disks

• Purpose
• Long-term, nonvolatile storage
• Large, inexpensive, slow level in the storage
  hierarchy
• Characteristics
• Seek Time (8 ms avg)
• positional latency
• rotational latency
• Transfer rate
• About a sector per ms (5-15 MB/s)
• Blocks
• Capacity
• Gigabytes
• Quadruples every 3 years (aerodynamics)

Track
Sector
Cylinder
Platter
Head
7200 RPM 120 RPS 8 ms per rev ave rot.
latency 4 ms 128 sectors per track 0.25 ms
per sector 1 KB per sector 16 MB / s
24
Disk Device Terminology
Disk Latency Queuing Time Controller time
Seek Time Rotation Time Xfer Time
Order of magnitude times for 4K byte transfers
Seek 8 ms or less Rotate 4.2 ms _at_ 7200
rpm Xfer 1 ms _at_ 7200 rpm
25
Anatomy of a Read Access

• Steps
• memory mapped I/O over bus to controller
• controller starts access
• seek rotational latency wait
• sector is read and buffered (validity checked)
• controller says ready or DMAs to main memory and
  thensays ready
• Calculating Access Time

Access Time Cont. Delay Block Size 0.5
Seek Time Pick Time Check Delay
Bandwidth
RPM
Time for Head to pass Over sector
Seek Time is very non-linear Accelerate and
decelerate times Complicate actual value
26
Seagate ST31401N (1993)

• 5.25 diameter
• 2.8 GB formatted capacity
• 2627 cylinders
• 21 tracks/cylinder
• 99 sectors per track - variable density encoded
• 512 bytes per sector
• rotational speed 5400 RPM
• average seek - random cylinder to cylinder 11
  ms
• minimum seek 1.7 ms
• maximum seek 22.5 ms
• transfer rate 4.6 MB/sec

27
Disk Trends

• bits/unit area is common improvement metric
• assumption is fixed density encoding
• may not get this since variable density control
  is cheaper
• Trends
• Until 1988 - 29 improvement per year
• doubles density in three years
• After 1988 (and thin film oxide deposition) - 60
  per year
• 4x density in three years
• Today
• 644 Mbits/in2
• 3 Gbit/in2 demonstrated in labs

Areal Density Tracks
Bits
Platter surface Inch
Track Inch
28
Disk Price Trends

• Personal Computer (as advertised)
• In 1995 dollars

29
Cost per Megabyte

• Improved 100x in 12 years
• 1983 300
• 1990 9
• 1995 0.90
• Comparison 1995
• SRAM chips 200 /MB with access times around 10
  ns
• DRAM chip 10 /MB with access times around 800
  ns
• there are chips with access times around 300 ns
  with 40 /MB
• DRAM boards 20 /MB and access times around 1
  us
• Disk .9 /MB access times around 100 msec
• Disk futures look bright
• all doomsday predictions have failed - e.g.
  bubble memories

30
Disk Alternatives

• DRAM and a battery
• big reduction in seek time and lower latency
• more reliable as long as the battery holds up
• cost is not attractive
• prediction theyll fail just like bubbles
• only some reasons will differ
• Optical Disks
• CDs are fine for archival storage due to their
  write once nature
• Theyre also cheap and high density
• unlikely to replace disks until many writes are
  possible
• numerous efforts at creating this technology
• none likely to mature soon however
• role is likely to be for archival store and SW
  distribution

31
Other Alternatives

• Robotic Tape Storage
• STC Powderhorn - 60 TB at 50/GB
• could hold the library of Congress in ASCII
• problem is tapes tend to wear out so rare access
  role is key
• Optical Juke Boxes
• now latency depends on even more mechanical
  gizmos
• Tapes - DAT vs. the old stuff
• amazing how dense you can be if you dont have to
  be right
• The new storage frontiers Terabyte per cm3
• plasma
• biological
• holographic spread spectrum
• none are anywhere close to deployment

32
I/O Connection Issues

• connecting the CPU to the I/O device world
• Typical choice is a bus - advantages
• shares a common set of wires and protocols
  cheap
• often based on some standard - PCI, SCSI, etc.
  portable
• But significant disadvantages for performance
• generality poor performance
• lack of precise loading model
• length may also be expandable
• multiple devices imply arbitration and therefore
  contention
• can be a bottleneck
• Common choice multiple busses in the system
• fast ones for memory and CPU to CPU interaction
  (maybe I/O)
• slow for more general I/O via some converter or
  bridge IOA

33
Nano-layered Disk Heads

• Special sensitivity of Disk head comes from
  Giant Magneto-Resistive effect or (GMR)
• IBM is leader in this technology
• Same technology as TMJ-RAM breakthrough we
  described in earlier class.

Coil for writing
34
Advantages of Small Formfactor Disk Drives
Low cost/MB High MB/volume High MB/watt Low
cost/Actuator
Cost and Environmental Efficiencies
35
Tape vs. Disk

• Longitudinal tape uses same technology as
• hard disk tracks its density improvements
• Disk head flies above surface, tape head lies on
  surface
• Disk fixed, tape removable
• Inherent cost-performance based on geometries
• fixed rotating platters with gaps
• (random access, limited area, 1 media /
  reader)
• vs.
• removable long strips wound on spool
• (sequential access, "unlimited" length,
  multiple / reader)
• New technology trend
• Helical Scan (VCR, Camcoder, DAT)
• Spins head at angle to tape to improve
  density

36
Current Drawbacks to Tape

• Tape wear out
• Helical 100s of passes to 1000s for longitudinal
• Head wear out
• 2000 hours for helical
• Both must be accounted for in economic /
  reliability model
• Long rewind, eject, load, spin-up times not
  inherent, just no need in marketplace (so far)
• Designed for archival

37
Automated Cartridge System
8 feet
STC 4400
10 feet

• 6000 x 0.8 GB 3490 tapes 5 TBytes in 1992
  500,000 O.E.M. Price
• 6000 x 10 GB D3 tapes 60 TBytes in 1998
  
  
• Library of Congress all information in the
  world in 1992, ASCII of all books 30 TB
  

38
Relative Cost of Storage TechnologyLate
1995/Early 1996

• Magnetic Disks
• 5.25 9.1 GB 2129 0.23/MB 1985 0.22/M
  B
• 3.5 4.3 GB 1199 0.27/MB 999 0.23/MB
• 2.5 514 MB 299 0.58/MB 1.1
  GB 345 0.33/MB
• Optical Disks
• 5.25 4.6 GB 1695199 0.41/MB 1499189
  0.39/MB
• PCMCIA Cards
• Static RAM 4.0 MB 700 175/MB
• Flash RAM 40.0 MB 1300 32/MB
• 175 MB 3600 20.50/MB

39
Storage System Issues

• Historical Context of Storage I/O
• Secondary and Tertiary Storage Devices
• Storage I/O Performance Measures
• Processor Interface Issues
• A Little Queuing Theory
• Redundant Arrarys of Inexpensive Disks (RAID)
• I/O Buses

40
Disk I/O Performance
Response Time (ms)
300
Metrics Response Time Throughput
200
100
0
100
0
Throughput ( total BW)
Response time Queue Device Service time
41
Response Time vs. Productivity

• Interactive environments
• Each interaction or transaction has 3 parts
• Entry Time time for user to enter command
• System Response Time time between user entry
  system replies
• Think Time Time from response until user begins
  next command
• 1st transaction
• 2nd transaction
• What happens to transaction time as shrink system
  response time from 1.0 sec to 0.3 sec?
• With Keyboard 4.0 sec entry, 9.4 sec think time
• With Graphics 0.25 sec entry, 1.6 sec think time

42
Response Time Productivity

• 0.7sec off response saves 4.9 sec (34) and 2.0
  sec (70) total time per transaction greater
  productivity
• Another study everyone gets more done with
  faster response, but novice with fast response
  expert with slow

43
Disk Time Example

• Disk Parameters
• Transfer size is 8K bytes
• Advertised average seek is 12 ms
• Disk spins at 7200 RPM
• Transfer rate is 4 MB/sec
• Controller overhead is 2 ms
• Assume that disk is idle so no queuing delay
• What is Average Disk Access Time for a Sector?
• Ave seek ave rot delay transfer time
  controller overhead
• 12 ms 0.5/(7200 RPM/60) 8 KB/4 MB/s 2 ms
• 12 4.15 2 2 20 ms
• Advertised seek time assumes no locality
  typically 1/4 to 1/3 advertised seek time 20 ms
  12 ms

44
Storage System Issues

• Historical Context of Storage I/O
• Secondary and Tertiary Storage Devices
• Storage I/O Performance Measures
• Processor Interface Issues
• A Little Queuing Theory
• Redundant Arrarys of Inexpensive Disks (RAID)
• I/O Buses

45
Processor Interface Issues

• Processor interface
• Interrupts
• Memory mapped I/O
• I/O Control Structures
• Polling
• Interrupts
• DMA
• I/O Controllers
• I/O Processors
• Capacity, Access Time, Bandwidth
• Interconnections
• Busses

46
I/O Interface
CPU
Memory
memory bus
Independent I/O Bus
Seperate I/O instructions (in,out)
Interface
Interface
Peripheral
Peripheral
CPU
Lines distinguish between I/O and memory
transfers
common memory I/O bus
40 Mbytes/sec optimistically 10 MIP
processor completely saturates the bus!
VME bus Multibus-II Nubus
Memory
Interface
Interface
Peripheral
Peripheral
47
Memory Mapped I/O
CPU
Single Memory I/O Bus No Separate I/O
Instructions
ROM
RAM
Memory
Interface
Interface
Peripheral
Peripheral
CPU
I/O

L2
Memory Bus
I/O bus
Memory
Bus Adaptor
48
Programmed I/O (Polling)
CPU
Is the data ready?
busy wait loop not an efficient way to use the
CPU unless the device is very fast!
no
Memory
IOC
yes
read data
device
but checks for I/O completion can be dispersed
among computationally intensive code
store data
done?
no
yes
49
Interrupt Driven Data Transfer
CPU
add sub and or nop
user program
(1) I/O interrupt
(2) save PC
Memory
IOC
(3) interrupt service addr
device
read store ... rti
interrupt service routine
User program progress only halted during
actual transfer 1000 transfers at 1 ms each
1000 interrupts _at_ 2 ?sec per interrupt
1000 interrupt service _at_ 98 ?sec each 0.1 CPU
seconds
(4)
memory
-6
Device xfer rate 10 MBytes/sec 0 .1 x 10
sec/byte 0.1 ?sec/byte
1000 bytes
100 ?sec 1000 transfers x 100 ?secs 100 ms
0.1 CPU seconds
Still far from device transfer rate! 1/2 in
interrupt overhead
50
Direct Memory Access
Time to do 1000 xfers at 1 msec each
1 DMA set-up sequence _at_ 50 ?sec 1 interrupt _at_ 2
?sec 1 interrupt service sequence _at_ 48
?sec .0001 second of CPU time
CPU sends a starting address, direction, and
length count to DMAC. Then issues "start".
0
ROM
CPU
Memory Mapped I/O
RAM
Memory
DMAC
IOC
device
Peripherals
DMAC provides handshake signals for
Peripheral Controller, and Memory Addresses and
handshake signals for Memory.
DMAC
n
51
Summary

• Disk industry growing rapidly, improves
• bandwidth 40/yr ,
• areal density 60/year, /MB faster?
• queue controller seek rotate transfer
• Advertised average seek time benchmark much
  greater than average seek time in practice
• Response time vs. Bandwidth tradeoffs
• Value of faster response time
• 0.7sec off response saves 4.9 sec and 2.0 sec
  (70) total time per transaction greater
  productivity
• everyone gets more done with faster response,
  but novice with fast response expert with slow
• Processor Interface today peripheral processors,
  DMA, I/O bus, interrupts

52
Storage System Issues

• Historical Context of Storage I/O
• Secondary and Tertiary Storage Devices
• Storage I/O Performance Measures
• Processor Interface Issues
• A Little Queuing Theory
• Redundant Arrarys of Inexpensive Disks (RAID)
• I/O Buses

53
Introduction to Queueing Theory
Arrivals
Departures

• More interested in long term, steady state than
  in startup Arrivals Departures
• Littles Law Mean number tasks in system
  arrival rate x mean reponse time
• Observed by many, Little was first to prove
• Applies to any system in equilibrium, as long as
  nothing in black box is creating or destroying
  tasks

54
A Little Queuing Theory Notation

• Queuing models assume state of equilibrium
  input rate output rate
• Notation
• l average number of arriving customers/secondTs
  er average time to service a customer
  (tradtionally m 1/ Tser )u server utilization
  (0..1) u l x Tser (or u l / m)Tq average
  time/customer in queue Tsys average
  time/customer in system Tsys Tq
  TserLq average length of queue Lq l x Tq
  Lsys average length of system Lsys r x Tsys
• Littles Law Lengthsystem rate x Timesystem
  (Mean number customers arrival rate x mean
  service time)

55
A Little Queuing Theory

• Service time completions vs. waiting time for a
  busy server randomly arriving event joins a
  queue of arbitrary length when server is busy,
  otherwise serviced immediately
• Unlimited length queues key simplification
• A single server queue combination of a servicing
  facility that accomodates 1 customer at a time
  (server) waiting area (queue) together called
  a system
• Server spends a variable amount of time with
  customers how do you characterize variability?
• Distribution of a random variable histogram?
  curve?

56
A Little Queuing Theory

• Server spends a variable amount of time with
  customers
• Weighted mean m1 (f1 x T1 f2 x T2 ... fn x
  Tn)/F (Ff1 f2...)
• variance (f1 x T12 f2 x T22 ... fn x Tn2)/F
  m12
• Must keep track of unit of measure (100 ms2 vs.
  0.1 s2 )
• Squared coefficient of variance C variance/m12
• Unitless measure (100 ms2 vs. 0.1 s2)
• Exponential distribution C 1 most short
  relative to average, few others long 90 average, 63
• Hypoexponential distribution C to average, C0.5 90 57
• Hyperexponential distribution C 1 further
  from average C2.0 90 average

Avg.
57
A Little Queuing Theory Variable Service Time

• Server spends a variable amount of time with
  customers
• Weighted mean m1 (f1 x T1 f2 x T2 ... fn x
  Tn)/F (Ff1 f2...)
• Squared coefficient of variance C
• Disk response times C ? 1.5 (majority seeks average)
• Yet usually pick C 1.0 for simplicity
• Another useful value is average time must wait
  for server to complete task m1(z)
• Not just 1/2 x m1 because doesnt capture
  variance
• Can derive m1(z) 1/2 x m1 x (1 C)
• No variance C 0 m1(z) 1/2 x m1

58
A Little Queuing TheoryAverage Wait Time

• Calculating average wait time in queue Tq
• If something at server, it takes to complete on
  average m1(z)
• Chance server is busy u average delay is u x
  m1(z)
• All customers in line must complete each avg
  Tser
• Tq u x m1(z) Lq x Ts er 1/2 x u x Tser
  x (1 C) Lq x Ts er Tq 1/2 x u x Ts er x
  (1 C) l x Tq x Ts er Tq 1/2 x u x Ts er x
  (1 C) u x TqTq x (1 u) Ts er x u x
  (1 C) /2Tq Ts er x u x (1 C) / (2 x (1
  u))
• Notation
• l average number of arriving
  customers/secondTser average time to service a
  customeru server utilization (0..1) u r x
  TserTq average time/customer in queueLq
  average length of queueLq r x Tq

59
A Little Queuing Theory M/G/1 and M/M/1

• Assumptions so far
• System in equilibrium
• Time between two successive arrivals in line are
  random
• Server can start on next customer immediately
  after prior finishes
• No limit to the queue works First-In-First-Out
• Afterward, all customers in line must complete
  each avg Tser
• Described memoryless or Markovian request
  arrival (M for C1 exponentially random),
  General service distribution (no restrictions), 1
  server M/G/1 queue
• When Service times have C 1, M/M/1 queueTq
  Tser x u x (1 C) /(2 x (1 u)) Tser x
  u / (1 u)
• Tser average time to service a
  customeru server utilization (0..1) u r x
  TserTq average time/customer in queue

60
A Little Queuing Theory An Example

• processor sends 10 x 8KB disk I/Os per second,
  requests service exponentially distrib., avg.
  disk service 20 ms
• On average, how utilized is the disk?
• What is the number of requests in the queue?
• What is the average time spent in the queue?
• What is the average response time for a disk
  request?
• Notation
• l average number of arriving customers/second
  10Tser average time to service a customer 20
  ms (0.02s)u server utilization (0..1) u l x
  Tser 10/s x .02s 0.2Tq average time/customer
  in queue Tser x u / (1 u) 20 x
  0.2/(1-0.2) 20 x 0.25 5 ms (0 .005s)Tsys
  average time/customer in system Tsys Tq Tser
  25 msLq average length of queueLq l x Tq
  10/s x .005s 0.05 requests in queueLsys
  average tasks in system Lsys l x Tsys
  10/s x .025s 0.25

61
A Little Queuing Theory Another Example

• processor sends 20 x 8KB disk I/Os per sec,
  requests service exponentially distrib., avg.
  disk service 12 ms
• On average, how utilized is the disk?
• What is the number of requests in the queue?
• What is the average time a spent in the queue?
• What is the average response time for a disk
  request?
• Notation
• l average number of arriving
  customers/second 20Tser average time to service
  a customer 12 msu server utilization (0..1) u
  l x Tser /s x . s Tq average
  time/customer in queue Ts er x u / (1 u)
  x /( ) x
  msTsys average time/customer in
  system Tsys Tq Tser 16 msLq average length
  of queueLq l x Tq /s x s
  requests in queue Lsys average tasks
  in system Lsys l x Tsys /s x s
  

62
A Little Queuing Theory Another Example

• processor sends 20 x 8KB disk I/Os per sec,
  requests service exponentially distrib., avg.
  disk service 12 ms
• On average, how utilized is the disk?
• What is the number of requests in the queue?
• What is the average time a spent in the queue?
• What is the average response time for a disk
  request?
• Notation
• l average number of arriving
  customers/second 20Tser average time to service
  a customer 12 msu server utilization (0..1) u
  l x Tser 20/s x .012s 0.24Tq average
  time/customer in queue Ts er x u / (1 u)
  12 x 0.24/(1-0.24) 12 x 0.32 3.8 msTsys
  average time/customer in system Tsys Tq Tser
  15.8 msLq average length of queueLq l x Tq
  20/s x .0038s 0.076 requests in queue Lsys
  average tasks in system Lsys l x Tsys
  20/s x .016s 0.32

63
A Little Queuing TheoryYet Another Example

• Suppose processor sends 10 x 8KB disk I/Os per
  second, squared coef. var.(C) 1.5, avg. disk
  service time 20 ms
• On average, how utilized is the disk?
• What is the number of requests in the queue?
• What is the average time a spent in the queue?
• What is the average response time for a disk
  request?
• Notation
• l average number of arriving
  customers/second 10Tser average time to service
  a customer 20 msu server utilization (0..1) u
  l x Tser 10/s x .02s 0.2Tq average
  time/customer in queue Tser x u x (1 C)
  /(2 x (1 u)) 20 x 0.2(2.5)/2(1 0.2) 20
  x 0.32 6.25 ms Tsys average time/customer in
  system Tsys Tq Tser 26 msLq average length
  of queueLq l x Tq 10/s x .006s 0.06
  requests in queueLsys average tasks in system
  Lsys l x Tsys 10/s x .026s 0.26

64
Storage System Issues

• Historical Context of Storage I/O
• Secondary and Tertiary Storage Devices
• Storage I/O Performance Measures
• Processor Interface Issues
• A Little Queuing Theory
• Redundant Arrarys of Inexpensive Disks (RAID)
• I/O Buses

65
Network Attached Storage
Decreasing Disk Diameters
14" ? 10" ? 8" ? 5.25" ? 3.5" ? 2.5" ? 1.8" ?
1.3" ? . . . high bandwidth disk systems based on
arrays of disks
High Performance Storage Service on a High
Speed Network
Network provides well defined physical and
logical interfaces separate CPU and storage
system!
Network File Services
OS structures supporting remote file access
3 Mb/s ? 10Mb/s ? 50 Mb/s ? 100 Mb/s ? 1 Gb/s ?
10 Gb/s networks capable of sustaining high
bandwidth transfers
Increasing Network Bandwidth
66
Manufacturing Advantages
of Disk Arrays
Disk Product Families
Conventional 4 disk designs
14
10
5.25
3.5
High End
Low End
Disk Array 1 disk design
3.5
67
Replace Small of Large Disks with Large of
Small Disks! (1988 Disks)
IBM 3390 (K) 20 GBytes 97 cu. ft. 3 KW 15
MB/s 600 I/Os/s 250 KHrs 250K
IBM 3.5" 0061 320 MBytes 0.1 cu. ft. 11 W 1.5
MB/s 55 I/Os/s 50 KHrs 2K
x70 23 GBytes 11 cu. ft. 1 KW 120 MB/s 3900
IOs/s ??? Hrs 150K
Data Capacity Volume Power Data Rate I/O
Rate MTTF Cost
large data and I/O rates high MB per cu. ft.,
high MB per KW reliability?
Disk Arrays have potential for
68
Array Reliability

• Reliability of N disks Reliability of 1 Disk
  ? N
• 50,000 Hours ? 70 disks 700 hours
• Disk system MTTF Drops from 6 years to 1
  month!
• Arrays (without redundancy) too unreliable to
  be useful!

Hot spares support reconstruction in parallel
with access very high media availability can be
achieved
69
Redundant Arrays of Disks
Files are "striped" across multiple
spindles  Redundancy yields high data
availability
Disks will fail Contents reconstructed from data
redundantly stored in the array
Capacity penalty to store it Bandwidth penalty
to update
Mirroring/Shadowing (high capacity
cost) Horizontal Hamming Codes
(overkill) Parity Reed-Solomon Codes Failure
Prediction (no capacity overhead!) VaxSimPlus
Technique is controversial
Techniques
70
Redundant Arrays of DisksRAID 1 Disk
Mirroring/Shadowing
recovery group
 Each disk is fully duplicated onto its
"shadow" Very high availability can be
achieved Bandwidth sacrifice on write
Logical write two physical writes Reads may
be optimized Most expensive solution 100
capacity overhead
Targeted for high I/O rate , high availability
environments
71
Redundant Arrays of Disks RAID 3 Parity Disk
10010011 11001101 10010011 . . .
P
logical record
1 0 0 1 0 0 1 1
1 1 0 0 1 1 0 1
1 0 0 1 0 0 1 1
0 0 1 1 0 0 0 0
Striped physical records
Parity computed across recovery group to
protect against hard disk failures 33
capacity cost for parity in this configuration
wider arrays reduce capacity costs, decrease
expected availability, increase
reconstruction time Arms logically
synchronized, spindles rotationally synchronized
logically a single high capacity, high
transfer rate disk
Targeted for high bandwidth applications
Scientific, Image Processing
72
Redundant Arrays of Disks RAID 5 High I/O Rate
Parity
Increasing Logical Disk Addresses
D0
D1
D2
D3
P
A logical write becomes four physical
I/Os Independent writes possible because
of interleaved parity Reed-Solomon Codes ("Q")
for protection during reconstruction
D4
D5
D6
P
D7
D8
D9
P
D10
D11
D12
P
D13
D14
D15
Stripe
P
D16
D17
D18
D19
Targeted for mixed applications
Stripe Unit
D20
D21
D22
D23
P
. . .
. . .
. . .
. . .
. . .
Disk Columns
73
Problems of Disk Arrays Small Writes
RAID-5 Small Write Algorithm
1 Logical Write 2 Physical Reads 2 Physical
Writes
D0
D1
D2
D3
D0'
P
old data
new data
old parity
(1. Read)
(2. Read)
XOR


XOR
(3. Write)
(4. Write)
D0'
D1
D2
D3
P'
74
RA90 Ave. Seek 18.5 ms Rotation 16.7 ms Xfer
Rate 2.8 MB/s Capacity 1200 MB
Normal Operating Range of Most Existing Systems
42 Array Group
IBM Small Disks Ave. Seek 12.5 ms Rotation
14 ms Xfer Rate 2.4 MB/s Capacity 300 MB
IO/sec
Mirrored RA90's
all reads
all writes
75
Subsystem Organization
array controller
host
single board disk controller
host adapter
manages interface to host, DMA
single board disk controller
control, buffering, parity logic
single board disk controller
physical device control
single board disk controller
striping software off-loaded from host to array
controller no applications modifications no
reduction of host performance
often piggy-backed in small format devices
76
System Availability Orthogonal RAIDs
Array Controller
String Controller
. . .
String Controller
. . .
String Controller
. . .
String Controller
. . .
String Controller
. . .
String Controller
. . .
Data Recovery Group unit of data redundancy
Redundant Support Components fans, power
supplies, controller, cables
End to End Data Integrity internal parity
protected data paths
77
System-Level Availability
host
host
Fully dual redundant
I/O Controller
I/O Controller
Array Controller
Array Controller
. . .
. . .
. . .
Goal No Single Points of Failure
. . .
. . .
. . .
with duplicated paths, higher performance can
be obtained when there are no failures
Recovery Group
78
Summary A Little Queuing Theory

• Queuing models assume state of equilibrium
  input rate output rate
• Notation
• r average number of arriving customers/secondTs
  er average time to service a customer
  (tradtionally ? 1/ Tser )u server utilization
  (0..1) u l x Tser Tq average time/customer in
  queue Tsys average time/customer in system Tsys
  Tq TserLq average length of queue Lq l x
  Tq Lsys average length of system Lsys l x
  Tsys
• Littles Law Lengthsystem rate x Timesystem
  (Mean number customers arrival rate x mean
  service time)

79
Summary Redundant Arrays of Disks (RAID)
Techniques
1 0 0 1 0 0 1 1
1 0 0 1 0 0 1 1
Disk Mirroring, Shadowing (RAID 1)
Each disk is fully duplicated onto its "shadow"
Logical write two physical writes 100
capacity overhead
1 0 0 1 0 0 1 1
0 0 1 1 0 0 1 0
1 1 0 0 1 1 0 1
1 0 0 1 0 0 1 1
Parity Data Bandwidth Array (RAID 3)
Parity computed horizontally Logically a single
high data bw disk
High I/O Rate Parity Array (RAID 5)
Interleaved parity blocks Independent reads and
writes Logical write 2 reads 2
writes Parity Reed-Solomon codes