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FIOS: A Fair, Efficient Flash I/O Scheduler

Stan Park Kai Shen

University of Rochester

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Background

Flash is widely available as mass storage, e.g. SSD

$/GB still dropping, affordable high-performance I/ODeployed in data centers as well low-power platforms

Adoption continues to grow but very little work in robust I/Oscheduling for Flash I/O

Synchronous writes are still a major factor in I/O bottlenecks

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Flash: Characteristics & Challenges

No seek latency, low latency variance

I/O granularity: Flash page, 2–8KB

Large erase granularity: Flash block, 64–256 pagesArchitecture parallelism

Erase-before-write limitation

I/O asymmetryWide variation in performance across vendors!

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Motivation

Disk is slow → scheduling has largely beenperformance-oriented

Flash scheduling for high performance ALONE is easy (justuse noop)

Now fairness can be a first-class concern

Fairness must account for unique Flash characteristics

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Motivation: Prior Schedulers

Fairness-oriented Schedulers: Linux CFQ, SFQ(D), Argon

Lack Flash-awareness and appropriate anticipation support

Linux CFQ, SFQ(D): fail to recognize the need for anticipationArgon: overly aggressive anticipation support

Flash I/O Scheduling: write bundling, write block preferential, andpage-aligned request merging/splitting

Limited applicability to modern SSDs, performance-oriented

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Motivation: Read-Write Interference

0 1 2

P r o b a b i l i t y d e n s i t y

I/O response time (in msecs)

Intel SSD read (alone)

0 0.2 0.4 0.6



Vertex SSD read (alone)

0 100 200 300


← all respond quickly


CompactFlash read (alone)

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Motivation: I/O Anticipation Support

Reduces potential seek cost for mechanical disks

...but largely negative performance effect on FlashFlash has no seek latency: no need for anticipation?

No anticipation can result in unfairness: premature servicechange, read-write interference

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Motivation: I/O Anticipation Support

0

1

2

3

4

I / O s

l o w

d o w n r a t i o

L i n u x C F Q , n o a n t i c .

S F Q ( D ) , n o a n t i c .

F u l l − q u a n t u m a n t i c .

Read slowdown

Write slowdown

Lack of anticipation can lead to unfairness; aggressive anticipationmakes fairness costly.

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FIOS: Policy

Fair timeslice management: Basis of fairness

Read-write interference management: Account for Flash I/Oasymmetry

I/O parallelism: Recognize and exploit SSD internalparallelism while maintaining fairness

I/O anticipation: Prevent disruption to fairness mechanisms

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FIOS: Timeslice Management

Equal timeslices: amount of time to access device

Non-contiguous usageMultiple tasks can be serviced simultaneously

Collection of timeslices = epoch; Epoch ends when:

No task with a remaining timeslice issues a request, orNo task has a remaining timeslice

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FIOS: Interference Management

0 1 2



Intel SSD read (with write)

0 0.2 0.4 0.6



Vertex SSD read (with write)

0 100 200 300



CompactFlash read (with write)

Reads are faster than writes → interference penalizes readsmore

Preference for servicing reads

Delay writes until reads complete

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FIOS: I/O Parallelism

SSDs utilize multiple independent channels

Exploit internal parallelism when possible, minding timesliceand interference management

Parallel cost accounting: New problem in Flash schedulingLinear cost model, using time to service a given request sizeProbabilistic fair sharing: Share perceived device time usageamong concurrent users/tasks

Cost = T elapsed P issuance

T elapsed is the requests elapsed time from its issuance to itscompletion, and P issuance is the number of outstanding requests(including the new request) at the issuance time

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FIOS: I/O Anticipation - When to anticipate?

Anticipatory I/O originally used for improving performance on diskto handle deceptive idleness: wait for a desirable request.Anticipatory I/O on Flash used to preserve fairness.

Deceptive idleness may break:

timeslice management

interference management

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FIOS: I/O Anticipation - How long to anticipate?

Must be much shorter than the typical idle period for disks

Relative anticipation cost is bounded by α

, where idle period isT service ∗

α

1−αwhere T service is per-task exponentially-weighted

moving average of per-request service time(Default α = 0.5)

ex. I/O → anticipation → I/O → anticipation → I/O → · · ·

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Implementation Issues

Linux coarse tick timer → High resolution timer for I/Oanticipation

Inconsistent synchronous write handling across file system andI/O layers

ext4 nanosecond timestamps lead to excessive metadataupdates for write-intensive applications

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Results: Beyond Read-Write Fairness

0

8

16

proportionalslowdown ←

I / O s

l o w d o w n r

a t i o

4−reader 4−writer on Vertex SSD

R a w d e v i c e I / O

L i n u x C F Q S F Q ( D )

Q u a n t a F I O S

Mean read latency

Mean write latency

0

2

4

6

8

proportionalslowdown

← I/O

slowdownr

atio

4KB−reader and 128KB−reader on Vertex SSD


L i n u x C F Q

S F Q ( D ) Q u a n t a

F I O S

Mean latency 4KB read

Mean latency 128KB read

FIOS achieves fairness not only with read-write asymmetry butalso requests of varying cost.

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Results: SPECweb co-run TPC-C

0

2

4

6

8

10

12

R e s p o n s e t i m e s l o w d o w n r a t i o

SPECweb and TPC−C on Intel SSD

28


L i n u x C F Q

S F Q ( D ) Q u a n t a

F I O S

SPECweb

TPC−C

FIOS exhibits the best fairness compared to the alternatives.

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Results: FAWNDS (CMU, SOSP’09) on CompactFlash

0

1

2

3

T a s k s l o w d o w n r a t i o

← proportional slowdown


L i n u x C F Q

S F Q ( D )

Q u a n t a

F I O S

FAWNDS hash gets FAWNDS hash puts

FIOS also applies to low-power Flash and provides efficient fairness.

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Conclusion

Fairness and efficiency in Flash I/O scheduling

Fairness is a primary concernNew challenge for fairness AND high efficiency (parallelism)

I/O anticipation is ALSO important for fairness

I/O scheduler support must be robust in the face of variedperformance and evolving hardware

Read/write fairness and BEYOND

May support other resource principals (VMs in cloud).

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