Paper by: Chris Ruemmler and John Wikes

Presentation by: Timothy Goldberg, Daniel Sink, Erin Collins, and Tony Luaders

IntroductionDisk Drive performance improvements at 7-

10%Compared to microprocessors at 40-60% or

disk storage capacities at 60-80% (annually)Simulation models to compare alternative

approachesHigh quality disk drive model

Error factor 14 times smaller

OutlineIntroductionCharacteristics of Modern Disk Drives

Recording ComponentsPositioning ComponentsDisk Controller

Modeling Disk Drives

Characteristics of Modern DiskNon-removable magnetic disk drivesContain a mechanism and controller

Recording Components: rotation disks and heads

Positioning Components: moves heads into correct position with track-following system

Emphasis on features that could be important when creating a disk drive model

Recording ComponentsSmaller disks:

Less surface area for dataLess power consumptionCan spin fasterSmaller seek distances

Increased storage density:Better linear recording density, maximum rate of

flux changesPacking separate tracks of data more closely

togetherMay contain from 1 to 12 platters

Stack rotates in lockstep

Recording ComponentsSpindle rotation speed:

Higher spin speed increases transfer rates, shortens rotation latencies

Higher power consumption, requires better bearings

Each platter surface has a disk headResponsible for recording (writing)And sensing (reading) magnetic flux variation

Single Read-Write data channelCan be switched between the headsResponsible for encoding and decoding data stream

into or from a series of magnetic phase changes stored on the disk

Disk Drive

Positioning ComponentsData surfaces are set up to store data in tracksModern disks have about 2,000 cylinders and are

3.5 inches.Cylinder is a single stack of tracks at a common

distance from the spindleTo access the data stored on a track, the disk

arms must rotate all the disks to get the desired track to the disk head.

This system ensures that the track is reached even with interruptionsExternal vibrations, shocks, and disk flaws (non

circular tracks)

SeekingThe speed of head movementFaster seeking requires more power

Half the seek time requires 4x powerSeek is composed of:

Speedup (arm moves until at half seek distance)

Coast (for long seeks, max velocity)Slowdown (rest close to desired track)Settle (puts disk head on desired location)

Track FollowingFine-tuning the head position at the end of

the seek and keeping the head on the desired track

Determines if head is correctly aligned by using positioning information on the disk at manufacturing time

Performs head switchesWhen the controller switches its data channel

from one surface to the next in the same cylinder

Data layoutA disk appears to its client computer as a linear

vector of addressable blocks which are mapped to physical sectors on the disk.

Using this method, the disk can hide bad sectors and do low-level performance optimizations.

Zoning: tracks are longer at the outside of a platter than at the inside.Maximize storage capacity

Track skewing: faster sequential access across track boundariesAllows data to be read or written at nearly full

media speedSparing: stores a list of flaws in the desk surface

to be skipped

The Disk ControllerMediates access to the mechanismRuns the track-following systemTransfers data between the disk drive and

the clientManages an embedded cache

caching of requestsSpeed-matching buffer can be extended to

include some form of caching for both reads and writes.

Caches in disk drives are relatively small because of space limitations.

Read-ahead: faster than seeking if the cache gets a hit

Write caching: saves cache informationCache is volatile, losing its contents if power to the

drive is lostCommand queuing: allows for multiple

outstanding requests at the same timeDisk controller determines the best execution order,

subject to additional host constraints.

Modeling Disk Drives

The Simulator• Based in C++ using a version of the AT&T

tasking library• The Basic ideas are readily applicable to

other simulation environments• The disk drive is modeled as two tasks and

some additional control structures• Task one models the mechanism, including the

head and platter (rotation) positions.• Task two, the direct memory access engine

(DMA), models the SCSI bus interface and its transfer engine.

The Simulator• The cache object buffers requests between

two tasks and is used to manage the asynchronous interactions between the bus interface and the disk mechanism tasks.

• The simulator can process about 2,000 I/Os per second on an HP9000 Series 800 Model H50 system• This allows 1 million requests to be serviced in

approximately 10 minutes

Evaluation• Took week long samples from a longer trace

series of HP-UX (Unix) computer systems. • A metric to evaluate the models used a time

distribution curve for the real drive and the model output and use the root mean square of the horizontal distance between these two curves.

No modeling•Uses a constant fixed time for each I/O•A demerit factor that is 35% of the average I/O time•This model is not good

A simple model•A better model requires:

•A seek time linear with the distance•No head-settle effects or head-switching costs•A rotational delay•A fixed controller overhead•A transfer time linear with the length of request

•demerit of 15% of a mean I/O time

Modeling head-positioning effects•Determined which track and cylinder the request started on and where it ended•Added a fix cost of 2.5 ms for each head and track switch•Demerit of 6.2% of a mean I/O time

Modeling rotation position•Calculate rotational latency by keeping track of rotational position of the disk•Account for spare sectors•A demerit of 2.6% of mean I/O time

Modeling data caching•Uses both read-ahead and immediate reporting•Large disparity due to caching•50% of request are completed in 3ms or less•Demerit of 112% is not acceptable!

Modeling data caching•Added aggressive read-ahead and immediate reporting to the model•Demerit is now only 5.7% of the mean I/O time

Model summary•Careful modeling is neither too difficult nor too costly•A good model needs careful calibration and tuning•These features and others may become particularly important when a workload has large data transfers