UKSM: Swift Memory Deduplication via Hierarchical and Adaptive

Memory Region Distilling

Nai Xia* Chen Tian* Yan Luo+ Hang Liu+ Xiaoliang Wang**: Nanjing University +: University of Massachusetts Lowell

Feb/15/2018

Background• What is Kernel Samepage Merging (KSM)?

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page 1

page 2Identical ?

page 1

page 2Update, different?

page 1

page 2

• Goal: Reduce memory consumption when duplication exists.• Effectiveness: There exist tremendous (~86%) memory duplications in

real-world applications, Change et al. [ISPA 2011].

…

Merge Unmerge

Unique Challenges

• Storage deduplication deals with relatively static content, only concerns about duplication ratio. • Sparse Indexing [FAST 2009] , CAFTL [FAST 2011], El-Shimi et al. [ATC 2012], Cao et al. [Just now]

• Responsiveness:• Remove duplications before they exhaust the memory.

• Dynamic nature:• Duplication status may change over time.

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Accelerate the deduplication of memory which is dynamic in nature!

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Outline

• Observation (Opportunity)• Overview• Hierarchical Region Distilling• Adaptive Partial Hashing• Evaluation• Conclusion

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Observation I: Pages within the Same Region Present Similar Patterns0 200 400 600 800 1000

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2

4

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8x 104

KVM Memory SpaceDu

plica

ted

Page

s

0 200 400 600 800 10000

2000

4000

6000

8000

Docker Memory Space

Dupl

icate

d Pa

ges

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• Test: Apache web server and MySQL database serving wordpress website in Ubuntu 16.04 (kernel version 4.4).

Duplicated pages concentrate by memory region.

*Please refer to our paper for other pattern analysis

Observation II: Hashing Needs to Be Adaptive

• Various applications need different hashing strengths to differentiate:• Image applications contain pages with highly similar contents.• Crypto applications contain diverse contents.

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We should adjust hashing strength accordingly.

Page i

Page j

Page i

Page j

Overview

• Assuming we have 9 memory regions, i.e., R0 – R8.

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R0 R1 R2 R3 R4

R5 R6 R7 R8

Ri

Low HighSimilarity

Overview

• Hierarchical memory region clustering.

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R0

R1

R2

R3

R4R5 R6

R7

R8

Level 1

Level 2

Level N

Ri

Low HighSimilarity

…

Overview

• Hierarchical region distilling.

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R0

R1

R2

R3

R4R5 R6

R7

R8

Level 1

Level 2

Level N

Ri

Low HighSimilarity

…

R3R3

R8

Overview

• Hierarchical region distilling.

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Ri

Low HighSimilarity

R0

R1

R2 R4R5 R6

R7

R8

Level 1

Level 2

Level N

Round n

…

R0

R1

R2 R4R5 R6

R7

Level 1

Level 2

Level N

Round n + 1

…

R3

R3 R8

Overview

• Hierarchical region distilling + Adaptive partial hashing.

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Ri

Low HighSimilarity

R0

R1

R2 R4R5 R6

R7

R8

Level 1

Level 2

Level N

R0

R1

R2 R4R5 R6

R7

…

Round n Round n + 1

R3

R3 R8

Overview

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R0

R1

R2 R4R5 R6

R7

R8

Level 1

Level 2

Level N

R0

R1

R2 R4R5 R6

R7

• Takeaway 1: Promote/demote regions.

Takeaway 1…

Takeaway 2

• Takeaway 2: Sampling offset shift.

Takeaway 3

• Takeaway 3: Hash strength adjustment.

Round n Round n + 1

• Hierarchical region distilling + Adaptive partial hashing.

Hierarchical Region Distilling• Memory region characterization – Signatures:• Vcow: promote regions whose COW-broken ratios are lower than this.• Vdup: promote regions whose duplication ratios are higher than this.• Vlife: regions living longer than this threshold can be effectively scanned.

• Default empirical values:• Vcow = 10%, Vdup = 20% and Vlife = 100ms.

Various commercial products adopt UKSM and observe different sweet spots.

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* COW: copy on write

Hierarchical Region Distilling

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Region Ri Sample & Hash

Treemerge

Treeunmerge

Adjust Vdup

*: We adopt Linux KSM black-red tree design to track ’merged’ and ’unmerged’ pages.

Write on merged tree, adjust Vcow

move page from unmerged to merged tree

Adaptive Partial Hashing

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Half hashing strength Strength = Strength ± DeltaProbe state

Adjusthash strength

We optimize SuperFastHash with the following key contributions:• Minimizing collisions – Optimizing avalanche for SuperFastHash [Hsieh 2004].• Progressive hashing – Support additivity while adjust hash strengths.

Hash Hash value H2 (round n+1)

Combine to H1,2Hash Hash value H1 (round n)1

st half

2nd half

Sampled page

Evaluation

• 6,000 Lines of Code in Linux kernel.• OS: Vanilla kernel 4.4. • Hardware: • Intel® Core ™ i7 CPU 920 with four 2.67 GHz cores.• 12 GB memory.

• For fair comparison• KSM is upgraded to SuperFastHash.

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Evaluation Goals

• How efficient is UKSM on different workloads?• How flexible is UKSM regarding customization?• What’s the responsiveness of UKSM vs KSM?• How does adaptive partial hashing perform compared to non-adaptive

algorithm?• What’s the performance penalty of UKSM?

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Evaluation Goals

• How efficient is UKSM on different workloads?• How flexible is UKSM regarding customization?• What’s the responsiveness of UKSM vs KSM?• How does adaptive partial hashing perform compared to non-adaptive

algorithm?• What’s the performance penalty of UKSM?

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Parameter Analysis

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0 50 100 150 200 250 3000

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90

100

Seconds

CPU

Util

izat

ion

(%)

FullQuiet

0 50 100 150 200 250 3000

1000

2000

3000

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5000

6000

Seconds

Mem

ory

Savi

ng (M

B)

FullMediumLowQuiet

• UKSM allows four levels of scanning strengths:• Level Full allows upto 95% CPU consumption and can scan the entire memory in 2 seconds.• Each lower level will half the CPU and potentially increase the scan time by 2x.

Setting: Booting 25 VMs, each with 1 VCPU, 1GB memory.

Catching up time

Responsiveness Analysis

0 100 200 300 400 500 600Seconds

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7000

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11000

Mem

ory

Util

izat

ion

(MB)

UKSMKSM 100 PagesKSM 1000 PagesKSM 2000 Pages

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611

95

615

0 100 200 300 400 500 600Seconds

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100

CPU

(% o

ne c

ore)

UKSMKSM 100 PagesKSM 1000 PagesKSM 2000 Pages

UKSM is 8.3×, 12.6×, 11.5× more efficient than KSM at scan speed of 100, 1000, 2000 pages.

Efficiency = "#"$%&'()*+,-./0$+'1"23*$+

Setting: Two processes, each with 4GB memory. One contains identical pages while the other random ones.

Related Work

• Content-based approach:• VMware ESX server, IBM active memory deduplication, Red Hat ksmtuned.• Majority of them treat every page equally.

• I/O hint based approach:• KSM++ [Resolve 2012], XLH[Usenix ATC 2013], CMD [VEE 2014].• Cannot track anonymous memory space (no I/O) or require hardware change.

• SmartMD [Usenix ATC ‘17]:• Consider various page sizes; we are orthogonal.

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Conclusion

• Memory deduplication faces the unique challenges. Our techniques:• Hierarchical region distilling.• Adaptive partial hashing.

• UKSM saves 12.6x and 5x more memory than KSM on static and dynamic workload, respectively, in the same time envelope.

• UKSM is an in production system: https://github.com/dolohow/uksm.• It has ~110 (watch, star and fork) after less than one year in GitHub.

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Thank You & Questions?

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We would like to thank our shepherd Dr. Hong Jiang and anonymous reviewers!