eecc722 - shaaban #1 lec # 13 fall2001 10-31-2001 i/o systems processor cache memory - i/o bus main...

EECC722 - ShaabanEECC722 - Shaaban#1 Lec # 13 Fall2001 10-31-2001

I/O SystemsI/O SystemsProcessor

Cache

Memory - I/O Bus

MainMemory

I/OController

Disk Disk

I/OController

I/OController

Graphics Network

interruptsinterrupts

Time(workload) = Time(CPU) + Time(I/O) - Time(Overlap)


I/O Controller ArchitectureI/O Controller ArchitecturePeripheral Bus (VME, FutureBus, etc.)

HostMemory

ProcessorCache

HostProcessor

Peripheral Bus Interface/DMA

I/O Channel Interface

BufferMemory

ROM

µProc

I/O Controller

Request/response block interface

Backdoor access to host memory


I/O: A System Performance PerspectiveI/O: A System Performance Perspective• CPU Performance: Improvement of 60% per year.

• I/O Sub-System Performance: Limited by mechanical delays (disk I/O). Improvement less than 10% per year (IO rate per sec or MB per sec).

• From Amdahl's Law: overall system speed-up is limited by the slowest component:

If I/O is 10% of current processing time:• Increasing CPU performance by 10 times

5 times system performance increase (50% loss in performance)

• Increasing CPU performance by 100 times 10 times system performance (90% loss of performance)

• The I/O system performance bottleneck diminishes the benefit of faster CPUs on overall system performance.


I/O Performance MeasuresI/O Performance Measures• Diversity: The variety of I/O devices that can be connected to the system.

• Capacity: The maximum number of I/O devices that can be connected to the system.

• Producer/server Model of I/O: The producer (CPU, human etc.) creates tasks to be performed and places them in a task buffer (queue); the server (I/O device or controller) takes tasks from the queue and performs them.

• I/O Throughput: The maximum data rate that can be transferred to/from an I/O device or sub-system, or the maximum number of I/O tasks or transactions completed by I/O in a certain period of time Maximized when task buffer is never empty.

• I/O Latency or response time: The time an I/O task takes from the time it is placed in the task buffer or queue until the server (I/O system) finishes the task. Includes buffer waiting or queuing time. Maximized when task buffer is always empty.


Producer-ServerProducer-ServerModelModel

ThroughputThroughput vs. vs. Response TimeResponse Time

Response Time = TimeSystem = TimeQueue + TimeServer


Components of A User/Computer Components of A User/Computer System TransactionSystem Transaction

• In an interactive user/computer environment, each interaction or transaction has three parts:

– Entry Time: Time for user to enter a command

– System Response Time: Time between user entry & system reply.

– Think Time: Time from response until user begins next command.


User/Interactive Computer Transaction TimeUser/Interactive Computer Transaction Time


Factors Affecting I/O Processing System Performance

• I/O processing computational requirements: – CPU computations available for I/O operations.

– Operating system I/O processing policies/routines.

– I/O Data Transfer Method used.

• I/O Subsystem performance:– Raw performance of I/O devices (i.e magnetic disk performance).

– IO bus capabilities.

– I/O subsystem organization.

– Loading level of I/O devices (queuing delay, response time).

• Memory subsystem performance:– Available memory bandwidth for I/O operations.


Magnetic DisksMagnetic DisksCharacteristics:Characteristics:• Diameter: 2.5in - 5.25in

• Rotational speed: 3,600RPM-10,000 RPM

• Tracks per surface.

• Sectors per track: Outer tracks contain

more sectors.

• Recording or Areal Density: Tracks/in X Bits/in

• Cost Per Megabyte.

• Seek Time: The time needed to move the

read/write head arm.

Reported values: Minimum, Maximum, Average.

• Rotation Latency or Delay:

The time for the requested sector to be under

the read/write head.

• Transfer time: The time needed to transfer a sector of bits.

• Type of controller/interface: SCSI, EIDE

• Disk Controller delay or time.

• Average time to access a sector of data =

average seek time + average rotational delay + transfer time +

disk controller overhead


Since the 1980's smaller form factor disk drives have grown in storage capacity. Today's 3.5 inch form factor drives designed for the entry-server market can store more than 75 Gbytes at the 1.6 inch height on 5 disks.


Drive areal density has increased by a factor of 8.5 million since the first disk drive, IBM's RAMAC, was introduced in 1957. Since 1991, the rate of increase in areal density has accelerated to 60% per year, andsince 1997 this rate has further accelerated to an incredible 100% per year.


The price per megabyte of disk storage has been decreasing at about 40% per year based on improvements in data density,-- even faster than the price decline for flash memory chips. Recent trends in HDD price per megabyte show an even steeper reduction.


Disk Access Time ExampleDisk Access Time Example• Given the following 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 the disk is idle, so no queuing delay exist.• What is Average Disk Access Time for a 512-byte 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


I/O Data Transfer MethodsI/O Data Transfer Methods• Programmed I/O (PIO): PollingProgrammed I/O (PIO): Polling

– The I/O device puts its status information in a status register.

– The processor must periodically check the status register.

– The processor is totally in control and does all the work.

– Very wasteful of processor time.

• Interrupt-Driven I/O:Interrupt-Driven I/O:– An interrupt line from the I/O device to the CPU is used to

generate an I/O interrupt indicating that the I/O device needs CPU attention.

– The interrupting device places its identity in an interrupt vector.

– Once an I/O interrupt is detected the current instruction is completed and an I/O interrupt handling routine is executed to service the device.


I/O data transfer methodsI/O data transfer methodsDirect Memory Access (DMA):Direct Memory Access (DMA): • Implemented with a specialized controller that transfers data between

an I/O device and memory independent of the processor.

• The DMA controller becomes the bus master and directs reads and writes between itself and memory.

• Interrupts are still used only on completion of the transfer or when an error occurs.

• DMA transfer steps:– The CPU sets up DMA by supplying device identity, operation,

memory address of source and destination of data, the number of bytes to be transferred.

– The DMA controller starts the operation. When the data is available it transfers the data, including generating memory addresses for data to be transferred.

– Once the DMA transfer is complete, the controller interrupts the processor, which determines whether the entire operation is complete.


Introduction to Queuing TheoryIntroduction to Queuing Theory

• Concerned with long term, steady state than in startup: – where => Arrivals = Departures

• Little’s Law:

Mean number tasks in system = arrival rate x mean response time

• Applies to any system in equilibrium, as long as nothing in the black box is creating or destroying tasks.

Arrivals Departures


I/O Performance & Little’s Queuing LawI/O Performance & Little’s Queuing Law

• Given: An I/O system in equilibrium input rate is equal to output rate) and: – Tser : Average time to service a task

– Tq : Average time per task in the queue

– Tsys : Average time per task in the system, or the response time,

the sum of Tser and Tq

– r : Average number of arriving tasks/sec– Lser : Average number of tasks in service.

– Lq : Average length of queue

– Lsys : Average number of tasks in the system,

the sum of L q and Lser

• Little’s Law states: Lsys = r x Tsys

• Server utilization = u = r / Service rate = r x Tser

u must be between 0 and 1 otherwise there would be more tasks arriving than could be serviced.

Proc IOC Device

Queue server

System


A Little Queuing TheoryA 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?

Proc IOC Device

Queue server

System


A Little Queuing Theory

• Server spends a variable amount of time with customers– Weighted mean time m1 = (f1 x T1 + f2 x T2 +...+ fn x Tn)/F

• where (F=f1 + 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% < 2.3 x average, 63% < average

• Hypoexponential distribution C < 1 : most close to average, C=0.5 => 90% < 2.0 x average, only 57% < average

• Hyperexponential distribution C > 1 : further from average C=2.0 => 90% < 2.8 x average, 69% < average

Proc IOC Device

Queue server

System

Avg.


A Little Queuing Theory: Variable Service TimeA Little Queuing Theory: Variable Service Time

• Server spends a variable amount of time with customers– Weighted mean m1 = (f1xT1 + f2xT2 +...+ fnXTn)/F (F=f1+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 doesn’t capture variance– Can derive m1(z) = 1/2 x m1 x (1 + C)– No variance => C= 0 => m1(z) = 1/2 x m1

Proc IOC Device

Queue server

System


A Little Queuing Theory:A Little Queuing Theory:Average Wait TimeAverage 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) + r x Tq x Ts er

Tq = 1/2 x u x Ts er x (1 + C) + u x Tq

Tq x (1 – u) = Ts er x u x (1 + C) /2Tq = Ts er x u x (1 + C) / (2 x (1 – u))

• Notation: r average number of arriving customers/second

Tser average time to service a customeru server utilization (0..1): u = r x Tser

Tq average time/customer in queueLq average length of queue:Lq= r x Tq


A Little Queuing Theory: M/G/1 and M/M/1A 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 C=1 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 Tser

Tq average time/customer in queue


M/M/m QueueM/M/m Queue• I/O system with Markovian request arrival rate r

• A single queue serviced by m servers (disks + controllers) each with Markovian Service rate = 1/ Tser

Tq = Tser x u /[m (1 – u)]

u = r x Tser / m

m number of servers

Tser average time to service a customer u server utilization (0..1): u = r x Tser / m

Tq average time/customer in queue


I/O I/O QueuingQueuing Performance: An Example Performance: An Example• A processor sends 10 x 8KB disk I/O requests per second, requests &

service are exponentially distributed, average disk service time = 20 ms

• On average: – How utilized is the disk, u?– What is the average time spent in the queue, Tq? – What is the average response time for a disk request, Tsys ?– What is the number of requests in the queue Lq? In system, Lsys?

• We have:

r average number of arriving requests/second = 10Tser average time to service a request = 20 ms (0.02s)

• We obtain:

u server utilization: u = r x Tser = 10/s x .02s = 0.2Tq average time/request 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/request in system: Tsys = Tq +Tser= 25 msLq average length of queue: Lq= r x Tq

= 10/s x .005s = 0.05 requests in queueLsys average # tasks in system: Lsys = r x Tsys = 10/s x .025s = 0.25


A Little Queuing Theory: Another ExampleA 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: r average number of arriving customers/second= 20

Tser average time to service a customer= 12 msu server utilization (0..1): u = r 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 queue:Lq= r x Tq

= 20/s x .0038s = 0.076 requests in queue Lsys average # tasks in system : Lsys = r x Tsys = 20/s x .016s = 0.32


A Little Queuing Theory: Yet Another ExampleA Little Queuing Theory: Yet 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: r average number of arriving customers/second= 10

Tser average time to service a customer= 20 msu server utilization (0..1): u = r 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 queue:Lq= r x Tq

= 10/s x .006s = 0.06 requests in queueLsys average # tasks in system :Lsys = r x Tsys = 10/s x .026s = 0.26


Designing an I/O SystemDesigning an I/O System• When designing an I/O system, the components that make

it up should be balanced.

• Six steps for designing an I/O systems are– List types of devices and buses in system– List physical requirements (e.g., volume, power, connectors, etc.)– List cost of each device, including controller if needed– Record the CPU resource demands of device

• CPU clock cycles directly for I/O (e.g. initiate, interrupts, complete)

• CPU clock cycles due to stalls waiting for I/O• CPU clock cycles to recover from I/O activity (e.g., cache flush)

– List memory and I/O bus resource demands– Assess the performance of the different ways to organize these

devices


Example: Determining the I/O BottleneckExample: Determining the I/O BottleneckAccounting For I/O Queue TimeAccounting For I/O Queue Time

• Assume the following system components:– 500 MIPS CPU– 16-byte wide memory system with 100 ns cycle time– 200 MB/sec I/O bus – 20 20 MB/sec SCSI-2 buses, with 1 ms controller overhead– 5 disks per SCSI bus: 8 ms seek, 7,200 RPMS, 6MB/sec

• Other assumptions– All devices used to 60% capacity.– Treat the I/O system as an M/M/m queue.– Requests are assumed spread evenly on all disks.– Average I/O size is 16 KB

– OS uses 10,000 CPU instr. for a disk I/O

• What is the average IOPS? What is the average bandwidth?• Average response time per IO operation?


• The performance of I/O systems is still determined by the portion with the lowest I/O bandwidth

– CPU : (500 MIPS)/(10,000 instr. per I/O) x .6 = 30,000 IOPS

CPU time per I/O = 10,000 / 500,000,000 = .02 ms– Main Memory : (16 bytes)/(100 ns x 16 KB per I/O) x .6 = 6,000 IOPS

Memory time per I/O = 1/10,000 = .1ms– I/O bus: (200 MB/sec)/(16 KB per I/O) x .6 = 12,500 IOPS– SCSI-2: (20 buses)/((1 ms + (16 KB)/(20 MB/sec)) per I/O) = 7,500 IOPS

SCSI bus time per I/O = 1ms + 16/20 ms = 1.8ms– Disks: (100 disks)/((8 ms + 0.5/(7200 RPMS) + (16 KB)/(6 MB/sec)) per I/0) x .6 = 6,700 x .6 = 4020 IOPS

Tser = (8 ms + 0.5/(7200 RPMS) + (16 KB)/(6 MB/sec) = 8+4.2+2.7 = 14.9ms

• The disks limit the I/O performance to r = 4020 IOPS• The average I/O bandwidth is 4020 IOPS x (16 KB/sec) = 64.3 MB/sec

• Tq = Tser x u /[m (1 – u)] = 14.9ms x .6 / [100 x .4 ] = .22 ms

• Response Time = Tser + Tq+ Tcpu + Tmemory + Tscsi =

14.9 + .22 + .02 + .1 + 1.8 = 17.04 ms

Example: Determining the I/O BottleneckExample: Determining the I/O Bottleneck Accounting For I/O Queue Time Accounting For I/O Queue Time


RAID (Redundant Array of Inexpensive Disks)• The term RAID was coined in a 1988 paper by Patterson, Gibson

and Katz of the University of California at Berkeley.

• In that article, the authors proposed that large arrays of small, inexpensive disks could be used to replace the large, expensive disks used on mainframes and minicomputers.

• In such arrays files are "striped" and/or mirrored across multiple drives.

• Their analysis showed that the cost per megabyte could be substantially reduced, while both performance and fault tolerance could be increased.

• 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!


Basic RAID OrganizationsBasic RAID Organizations

• Non-Redundant (RAID Level 0)

• Mirrored (RAID Level 1)

• Memory-Style ECC (RAID Level 2)

• Bit-Interleaved Parity (RAID Level 3)

• Block-Interleaved Parity (RAID Level 4)

• Block-Interleaved Distributed-Parity (RAID Level 5)

• P+Q Redundancy (RAID Level 6)

• Striped Mirrors (RAID Level 10)


Manufacturing Advantages of Disk ArraysManufacturing Advantages of Disk Arrays

14”10”5.25”3.5”

3.5”

Disk Array: 1 disk design

Conventional: 4 disk designs

Low End High End

Disk Product Families


RAID Subsystem OrganizationRAID Subsystem Organization

hostarray

controller

single boarddisk

controller

single boarddisk

controller

single boarddisk

controller

single boarddisk

controller

hostadapter

manages interfaceto host, DMA

control, buffering,parity logic

physical devicecontrol

often piggy-backedin small format devices

striping software off-loaded from host to array controller

no applications modifications

no reduction of host performance


Non-Redundant (RAID Level 0)Non-Redundant (RAID Level 0)• RAID 0 simply stripes data across all drives (minimum 2 drives) to

increase data throughput but provides no fault protection.

– Sequential blocks of data are written across multiple disks in stripes, as follows:

• The size of a data block, which is known as the "stripe width", varies with the implementation, but is always at least as large as a disk's sector size.

• This scheme offers the best write performance since it never needs to update redundant information.

• It does not have the best read performance.– Redundancy schemes that duplicate data, such as mirroring, can

perform better on reads by selectively scheduling requests on the disk with the shortest expected seek and rotational delays.


Optimal Size of Data Striping UnitOptimal Size of Data Striping Unit• Lee and Katz [1991] use an analytic model of non-redundant disk arrays to derive an

equation for the optimal size of data striping unit.

• They show that the optimal size of data strip-ing is equal to:

• Where:

– P is the average disk positioning time,

– X is the average disk transfer rate,

– L is the concurrency, Z is the request size, and

– N is the array size in disks.

• Their equation also predicts that the optimal size of data striping unit is dependent only the relative rates at which a disk positions and transfers data, PX, rather than P or X individually. Lee and Katz show that the opti-mal

• striping unit depends on request size; Chen and Patterson show that this dependency can be ignored without significantly affecting performance.

N

ZLPX )1(


Mirrored (RAID Level 1)Mirrored (RAID Level 1)• Utilizes mirroring or shadowing of data using twice as many disks as a

non-redundant disk array.

• Whenever data is written to a disk the same data is also written to a redundant disk, so that there are always two copies of the information.

• When data is read, it can be retrieved from the disk with the shorter queueing, seek and rotational delays

• If a disk fails, the other copy is used to service requests.

• Mirroring is frequently used in database applications where availability and transaction rate are more important than storage efficiency.


Memory-Style ECC (RAID Level 2)Memory-Style ECC (RAID Level 2)• RAID 2 performs data striping with a block size of one bit or byte, so

that all disks in the array must be read to perform any read operation.

• A RAID 2 system would normally have as many data disks as the word size of the computer, typically 32.

• In addition, RAID 2 requires the use of extra disks to store an error-correcting code for redundancy. – With 32 data disks, a RAID 2 system would require 7 additional disks for a

Hamming-code ECC.

– Such an array of 39 disks was the subject of a U.S. patent granted to Unisys Corporation in 1988, but no commercial product was ever released.

• For a number of reasons, including the fact that modern disk drives contain their own internal ECC, RAID 2 is not a practical disk array scheme.


Bit-Interleaved Parity (RAID Level 3)Bit-Interleaved Parity (RAID Level 3)• One can improve upon memory-style ECC disk arrays ( RAID 2) by

noting that, unlike memory component failures, disk controllers can easily identify which disk has failed. Thus, one can use a single parity disk rather than a set of parity disks to recover lost information.

• As with RAID 2, RAID 3 must read all data disks for every read operation.– This requires synchronized disk spindles for optimal performance, and

works best on a single-tasking system with large sequential data requirements. An example might be a system used to perform video editing, where huge video files must be read sequentially.


Block-Interleaved Parity (RAID Level 4)Block-Interleaved Parity (RAID Level 4)• RAID 4 is similar to RAID 3 except that blocks of data are striped across

the disks rather than bits/bytes.• Read requests smaller than the striping unit access only a single data disk. • Write requests must update the requested data blocks and must also

compute and update the parity block. – For large writes that touch blocks on all disks, parity is easily computed by

exclusive-or’ing the new data for each disk.

– For small write requests that update only one data disk, parity is computed by noting how the new data differs from the old data and apply-ing those differences to the parity block.

• This can be an important performance improvement for small or random file access (like a typical database application) if the application record size can be matched to the RAID 4 block size.


Block-Interleaved Distributed-Parity (RAID Level 5)• The block-interleaved distributed-parity disk array eliminates the

parity disk bottleneck present in RAID 4 by distributing the parity uniformly over all of the disks.

• An additional, frequently overlooked advantage to distributing the parity is that it also distributes data over all of the disks rather than over all but one.

• RAID 5 has the best small read, large read and large write performance of any redundant disk array. – Small write requests are somewhat inefficient compared with redundancy

schemes such as mirroring however, due to the need to perform read-modify-write operations to update parity.


Problems of Disk Arrays: Small WritesProblems of Disk Arrays: Small Writes

D0 D1 D2 D3 PD0'

+

+

D0' D1 D2 D3 P'

newdata

olddata

old parity

XOR

XOR

(1. Read) (2. Read)

(3. Write) (4. Write)

RAID-5: Small Write Algorithm

1 Logical Write = 2 Physical Reads + 2 Physical Writes


P+Q Redundancy (RAID Level 6)P+Q Redundancy (RAID Level 6)

• An enhanced RAID 5 with stronger error-correcting codes used .

• One such scheme, called P+Q redundancy, uses Reed-Solomon codes, in addition to parity, to protect against up to two disk failures using the bare minimum of two redundant disks.

• The P+Q redundant disk arrays are structurally very similar to the block-interleaved distributed-parity disk arrays (RAID 5) and operate in much the same manner.

– In particular, P+Q redundant disk arrays also perform small write opera-tions using a read-modify-write procedure, except that instead of four disk accesses per write requests, P+Q redundant disk arrays require six disk accesses due to the need to update both the ‘P’ and ‘Q’ information.


RAID 5/6: High I/O Rate ParityRAID 5/6: High I/O Rate Parity

A logical writebecomes fourphysical I/Os

Independent writespossible because ofinterleaved parity

Reed-SolomonCodes ("Q") forprotection duringreconstruction

A logical writebecomes fourphysical I/Os

Independent writespossible because ofinterleaved parity

Reed-SolomonCodes ("Q") forprotection duringreconstruction

D0 D1 D2 D3 P

D4 D5 D6 P D7

D8 D9 P D10 D11

D12 P D13 D14 D15

P D16 D17 D18 D19

D20 D21 D22 D23 P

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.Disk Columns

IncreasingLogical

Disk Addresses

Stripe

StripeUnit

Targeted for mixedapplications


RAID 10 (Striped Mirrors)RAID 10 (Striped Mirrors)• RAID 10 (also known as RAID 1+0) was not mentioned in the original

1988 article that defined RAID 1 through RAID 5.

• The term is now used to mean the combination of RAID 0 (striping) and RAID 1 (mirroring).

• Disks are mirrored in pairs for redundancy and improved performance, then data is striped across multiple disks for maximum performance.

• In the diagram below, Disks 0 & 2 and Disks 1 & 3 are mirrored pairs.

• Obviously, RAID 10 uses more disk space to provide redundant data than RAID 5. However, it also provides a performance advantage by reading from all disks in parallel while eliminating the write penalty of RAID 5.


RAID Levels Comparison:Throughput Per Dollar Relative to RAID Level 0.


RAID ReliabilityRAID Reliability• Redundancy in disk arrays is motivated by the need to overcome

disk failures.

• When only independent disk failures are considered, a simple parity scheme works admirably. Patterson, Gibson, and Katz derive the mean time between failures for a RAID level 5 to be:

MTTF (disk)2 / N (G - 1) MTTR(disk)• where MTTF(disk) is the mean-time-to-failure of a single disk,• MTTR(disk) is the mean-time-to-repair of a single disk, • N is the total number of disks in the disk array• G is the parity group size

• For illustration purposes, let us assume we have• 100 disks that each had a mean time to failure (MTTF) of 200,000 hours

and a mean time to repair of one hour. If we organized these 100 disks into parity groups of average size 16, then the mean time to failure of the system would be an astounding 3000 years! Mean times to failure of this

• magnitude lower the chances of failure over any given period of time.


SystemSystem Availability: Orthogonal RAIDs Availability: Orthogonal RAIDs

Redundant Support Components: power supplies, controller, cables

ArrayController

StringController

StringController

StringController

StringController

StringController

StringController

. . .

. . .

. . .

. . .

. . .

. . .

Data Recovery Group: unit of data redundancy


System-Level AvailabilitySystem-Level Availability

Fully dual redundantI/O Controller I/O Controller

Array Controller Array Controller

. . .

. . .

. . .

. . . . . .

.

.

.RecoveryGroup

Goal: No SinglePoints ofFailure

Goal: No SinglePoints ofFailure

host host

with duplicated paths, higher performance can beobtained when there are no failures


RAID Case Studies: RAID Case Studies: NCR 6298• The NCR 6298 Disk Array Subsystem, released in 1992, is a low cost RAID

subsystem supporting RAID levels 0, 1, 3 and 5.

• Designed for commercial environments, the system supports up to four controllers, redundant power supplies and fans, and up to 20 3.5” SCSI-2 drives. All components

• power supplies, drives, and controllers—can be replaced while the system services requests.

• The array controller architecture features a unique lock-step design that requires almost no buffering. For all requests except RAID level 5 writes, data flows directly through the controller to the drives. The controller duplexes the data stream for mirroring configurations and generates parity for RAID level 3 synchronously with data transfer.

• The host interface is fast, wide, differential SCSI-2 (20 MB/S), while the drive channels are fast, narrow SCSI-2 (10 MB/S). Because of the lock-step architecture, transfer bandwidth to the host is limited to 10 MB/S for RAID level 0, 1 and 5. However, in RAID level 3 configurations, performance on large transfers has been measured at over 14 MB/S


NCR 6298


RAID Case Studies: RAID Case Studies:

The RAID-II Storage Server• RAID-II is a high-bandwidth, network file server designed and implemented at the

University of California at Berkeley.

• RAID-II interfaces a SCSI-based disk array to a HIPPI network.

• One of RAID-II’s unique features is its ability to provide

• high-bandwidth access from the network to the disks without transferring data through the relatively slow file server (a Sun4/280 workstation) memory system. To do this, the RAID project designed a custom printed-circuit board called the XBUS card.

• The XBUS card provides a high-bandwidth path between the major system components: the HIPPI network, four VME busses that connect to VME disk controllers, and an interleaved, multi-ported semiconductor memory. The XBUS card also contains a parity computation engine that generates parity for writes and reconstruction on the disk array.

• The data path between these system components is a 4 8 crossbar switch that can sustain approximately 160 MB/s. The entire system is controlled by an external Sun 4/280 file server through a memory-mapped control register interface.

• The maximum bandwidth of RAID-II is between 20 and 30 MB/s, enough to support the full disk bandwidth of approximately 20 disk drives.


The RAID-II Storage Server

eecc722 - shaaban #1 lec # 13 fall2001 10-31-2001 i/o systems processor cache memory - i/o bus main...

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