FIOS: A Fair, Efficient Flash I/O Scheduler Stan Park and Kai Shen - - PowerPoint PPT Presentation

FIOS: A Fair, Efficient Flash I/O Scheduler

Stan Park and Kai Shen presented by Jason Gevargizian

Flash devices

• NAND Flash devices are used for storage

○ aka Solid-state Drives (SSDs)

• much higher I/O performance than

mechanical disks

• this increased performance has potential to

alleviate I/O bottlenecks in data-intensive applications

I/O Schedulers & Flash

• conventional I/O schedulers were built for

mechanical storage devices

• they fail to recognize the unique flash

characteristics

○ read-blocked-by-write ○ I/O anticipation ○ suppression of parallelism

• This results in poor fairness and

performance on Flash devices

Write limitations of Flash

• erase-before-write

○ write target locations must be erased before writing

• large erasure granularity

○ 64-256x the size of the read

• consequently, read-write speeds are highly

asymmetric

○ reads are one to two orders of magnitude faster ○ mechanical disks r&w operations are fairly symmetric

read-blocked-by-write

• write limitations lead to read interference on

these devices

• when reads are blocked by writes, reads

suffer a substantial slowdown

• scheduling reads and writes with equal

preference creates unfairness

I/O Anticipation

• anticipation is the idling of a device to

prepare for soon-arriving I/O requests

○ necessary for mechanical devices with high seek times ○ some anticipation is necessary for fairness ■ to avoid premature switching/advancing in quanta and fair queuing approaches

• Flash devices have low access times and

are hurt by the liberal use of anticipation

I/O Parallelism

• Flash devices have built-in parallelism

○ Multiple channels, often each with multiple parallel planes

• conventional fairness oriented I/O

schedulers suppress parallelism

○ simplifies accounting/allocation of device for queues ○ this does not maximize the benefit of Flash devices

I/O Schedulers & Flash

• conventional I/O schedulers were built for

mechanical storage devices

• they fail to recognize the unique flash

characteristics

○ read-blocked-by-write ○ I/O anticipation ○ suppression of parallelism

• This results in poor fairness and

performance on Flash devices

FIOS Design - 4 Techniques

• 1. Fair timeslice management with timeslice

fragmentation and concurrent request issuance

• 2. support read preference to combat read-

blocked-by-write

• 3. enable concurrent issuance to utilize device-

level parallelism

• 4. use I/O anticipation judiciously to maintain

fairness at minimal idling cost

1) Fair Timeslice Management

• each task is allotted one full timeslice within

an Epoch

• timeslices can be fragmented

○ remaining timeslice is calculated after each I/O request completion

• epochs end when either:

○ (1) no task has a non-zero timeslice ○ (2) no task with a positive timeslice has I/O requests

2) Read/Write Interference

• reads suffer dramatic write interference
• read preference policy is used

○ all writes are blocked until all reads complete ■ writes have a large service time, so additional queuing time is small ○ Epoch policy governs read preference (to ensure writes don’t starve)

• read preference is optional (some devices

like vertex don’t have high asymmetry)

3) I/O Parallelism

• many Flash drives have internal parallelism
• after issuing I/O requests, FIOS searches for

additional requests to run in parallel

• I/O Cost accounting

○ outstanding requests can be delayed slightly by parallel request issuance ○ tasks must not be billed for the additional time (spent

• n other tasks’ requests)

3) I/O Parallelism

• I/O Cost accounting

○ FIOS attempts to not bill tasks for this additional device time ○ FIOS calibrates (for the given device once) read and write request times at different sizes, then uses interpolation for other sizes ○ When calibration unavailable, FIOS assumes that

• utstanding requests take an equal time on device

4) I/O Anticipation

• anticipation (idling) is used for mechanical

disks to combat long seek times

• Flash devices are fast and are hurt by I/O

anticipation

• some anticipation is still necessary to ensure

fairness...

4) I/O Anticipation

• FIOS uses anticipation minimally
• anticipation is employed to delay epoch

ending for anticipating tasks that have remaining timeslice left (but no requests)

• anticipation is also employed after read

completion to mitigate read-blocked-by-write

○ anticipates instead of immediately issuing write

FIOS Design - 4 Techniques

• 1. Fair timeslice management with timeslice

fragmentation and concurrent request issuance

• 2. support read preference to combat read-

blocked-by-write

• 3. enable concurrent issuance to utilize device-

level parallelism

• 4. use I/O anticipation judiciously to maintain

fairness at minimal idling cost

Evaluation - Schedulers

FIOS is compared against three fairness-

• riented I/O schedulers:
• 1. Linux CFQ scheduler (Completely Fair

Queuing)

• 2. SFQ (Start-time Fair Queuing with a

concurrency depth)

• 3. a quanta-based I/O scheduler similar to one

employed in Argon

Evaluation - Flash Devices

Evaluation was performed on the following drives:

• Intel X225-M Flash-based SSD (2009)
• Mtron Pro 7500 Flash-based SSD (2008)
• OCZ Vertex 3 Flash-based SSD (2011)
• SanDisk CompactFlash drive on 6-Watts

“wimpy” node

Evaluation - Workloads

• Set of synthetic benchmarks with varying I/O

concurrency

• realistic applications

○ SPCweb workload on Apache ○ TPC-C workload on MySQL database

• CompactFlash drive in a low-power wimpy

node using FAWN Data Store workload

Synthetic Benchmarks

• 1-reader 1-writer that continuously issue

4KB reads/writes respectively

• 4-reader 4-writer of 4KB reads/writes
• 4-reader 4-writer with thinktime

○ exponentially distributed thinktime between I/O requests such that total thinktime is equal to I/O device time

• 4KB-reader and 128KB reader

Synthetic Benchmarks

• slowdown is latency

ratio to running-alone

• raw, CFQ, & SFQ

suffer read interference

• quanta performs fairer

due to aggressive per- task quantum

• quanta suffers in

performance due to excessive anticipation and lack of parallelism

Synthetic Benchmarks

• on vertex, all achieve

decent fairness due to low read-write asymmetry

Synthetic Benchmarks (continued)

• the 4KB-reader and 128KB-reader benchmark reveals a case

where even on the vertex, only FIOS and quanta achieve fairness

Synthetic Benchmarks

• overall concurrent

efficiency (relative throughput of concurrent execution compared to running- alone throughput)

• quanta suffers for its

aggressive methods for fairness

SPECweb and TPC-C

• read-only SPECweb99 workload running on

an Apache 2.2.3 webserver

• write intensive TPC-C running on MySQL

5.5.13 database

• each application is driven by a closed-loop

load generator with 4 concurrent clients

○ each client issuing requests continuously

SPECweb and TPC-C

• write intensive TPC-C

experiences more slowdown on Flash

• again, quanta suffers from

excessive anticipation

• FIOS performs the best
• n these benchmarks

Low-Power CompactFlash

• low-power wimpy node like the ones used

with FAWN

○ node contains an Alix board with single-core 500MHz AMD Geode CPU, 256MB SDRAM

• 16GB SanDisk CompactFlash drive

○ does not have parallelism

• FAWN Data Store application workload

○ two tasks, one performing hash gets and another performing hash puts

Low-Power CompactFlash

• quanta and FIOS perform

the most fair

• quanta does not suffer as

it had from lack of paralleization (since CompactFlash does not support it)

Wrap Up

• Flash storage devices show potential to

alleviate I/O bottlenecks

• conventional I/O schedulers do not account

for the unique characteristics of Flash

• the authors propose FIOS as a method to

better handle the scheduling of I/O for Flash devices with promising results

Discussion

Thank you. Questions?

To the class: Much of the benefit of FIOS relies upon read-write speed discrepancy of Flash. Do you think FIOS and systems like it will be useful for very long?