with GPU in Hybrid Storage Systems Prince Hamandawana, Awais Khan, - - PowerPoint PPT Presentation

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with GPU in Hybrid Storage Systems Prince Hamandawana, Awais Khan, - - PowerPoint PPT Presentation

Dec 03, 2022 276 likes •442 views

Accelerating the Data Deduplication Performance with GPU in Hybrid Storage Systems Prince Hamandawana, Awais Khan, Changgyu Lee , Sungyong Park, Youngjae Kim Department of Computer Science and Engineering Sogang University, Seoul, Republic of

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SLIDE 1

Accelerating the Data Deduplication Performance with GPU in Hybrid Storage Systems

Prince Hamandawana, Awais Khan, Changgyu Lee, Sungyong Park, Youngjae Kim Department of Computer Science and Engineering Sogang University, Seoul, Republic of Korea PDSW-DISCS 17 WIP session November 13, 2017, Denver, USA

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Laboratory for Advanced System Software

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SLIDE 2

Inline Deduplication in Cloud Storage System

 To achieve high space utilization in Tiered Cloud Storage System, following techniques are discussed in community

1. Compression 2. Erasure Coding

 Can’t remove replicated data across cluster  Difficult to deploy inline mode

3. Inline Data Deduplication

 Higher Storage Efficiency by removing replicated data across cluster  Eliminating duplicated data in Cache tier  But, overhead of inline deduplication directly affects to performance.

 In Hybrid Storage system, Cache-tier nodes equip SSDs and inline deduplication can reduce amount of writes to SSD.

→ Lower Write Amplification, Longer Lifetime

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SLIDE 3

Inline Deduplication Framework on Ceph

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Storage Node #1 Storage Node #2 Storage Node #3 Cache Node #1 Cache Node #1 Cache Tier (SSD) Storage Tier (SSD)

Fingerprint Index Fingerprint Index

Object CRUSH Algorithm

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SLIDE 4

1 2 3 4 Object

Inline Deduplication Framework on Ceph

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Storage Node #1 Storage Node #2 Storage Node #3 Cache Node #1 Cache Node #1 Cache Tier (SSD) Storage Tier (SSD)

Fingerprint Index Fingerprint Index

Chunking

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SLIDE 5

1 2 3 4 1 2 3 4

Inline Deduplication Framework on Ceph

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Storage Node #1 Storage Node #2 Storage Node #3 Cache Node #1 Cache Node #1 Cache Tier (SSD) Storage Tier (SSD)

Fingerprint Index Fingerprint Index

Fingerprinting

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SLIDE 6

Fingerprint Index

1 2 3 4 1 2 3 4

Inline Deduplication Framework on Ceph

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Storage Node #1 Storage Node #2 Storage Node #3 Cache Node #1 Cache Node #1 Cache Tier (SSD) Storage Tier (SSD)

Fingerprint Index

Deduplication Check Not Duplicate

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SLIDE 7

Fingerprint Index

1 2 3 4 2 3 4

Inline Deduplication Framework on Ceph

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Storage Node #1 Storage Node #2 Storage Node #3 Cache Node #1 Cache Node #1 Cache Tier (SSD) Storage Tier (SSD)

Fingerprint Index

Deduplication Check Duplicate

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SLIDE 8

Fingerprint Index

1 3 4 3 4

Inline Deduplication Framework on Ceph

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Storage Node #1 Storage Node #2 Storage Node #3 Cache Node #1 Cache Node #1 Cache Tier (SSD) Storage Tier (SSD)

Fingerprint Index

Deduplication Check Increase Reference Count

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SLIDE 9

Fingerprint Overhead and GPU Acceleration

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 Deduplication overhead consists of  Chunking  Calculating Fingerprint  Fingerprint Query  We observed fingerprint overhead is more than 70% in total deduplication overhead.  To reduce fingerprinting overhead, we propose to use GPU Acceleration for fingerprinting.

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SLIDE 10

GPU

Accelerating Fingerprint Calculation with GPU

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Storage Node #1 Storage Node #2 Storage Node #3 Cache Node #1 Cache Node #1 Cache Tier (SSD) Storage Tier (SSD)

Fingerprint Index Fingerprint Index

GPU Fingerprinting 1 2 3 4

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SLIDE 11

GPU

Accelerating Fingerprint Calculation with GPU

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Storage Node #1 Storage Node #2 Storage Node #3 Cache Node #1 Cache Node #1 Cache Tier (SSD) Storage Tier (SSD)

Fingerprint Index Fingerprint Index

GPU Fingerprinting 1 2 3 4 1 2 3 4

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SLIDE 12

GPU

Accelerating Fingerprint Calculation with GPU

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Storage Node #1 Storage Node #2 Storage Node #3 Cache Node #1 Cache Node #1 Cache Tier (SSD) Storage Tier (SSD)

Fingerprint Index Fingerprint Index

GPU Fingerprinting 1 2 3 4 1 2 3 4

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SLIDE 13

Experiment Setup

 Ceph Jewel v10.2.5  CUDA Toolkit 8.0  4 OSD server

 Intel Xeon ES-2640 v3 @ 2.60GHz  32GB memory  12GB NVIDIA Tesla K80 GPU  2 SSDs (Cache Tier), 4 HDD (Storage Tier)

 Ceph RBD Client  Total 1GB size random 4MB writes using fio benchmark

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SLIDE 14

Preliminary Results

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2 4 6 8 10 12 14 16 18 20 CPU GPU CPU GPU CPU GPU CPU GPU 128 256 512 1024 Total Time (sec) Chunk Size (KB) Chunking Fingerprint Fingerprint Query

65% Reduced

 GPU Fingerprinting reduced about 65% of fingerprint

  • verhead.

 Total Deduplication overhead is reduced to 52%.

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SLIDE 15

Q&A

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 Contact: Changgyu Lee (changgyu@sogang.ac.kr) Department of Computer Science and Engineering Sogang University, Seoul, Republic of Korea