Tradeoffs in Scalable Data Routing for Deduplication Clusters

Tradeoffs in Scalable Data Routing for Deduplication Clus-

tersFAST '11

Wei Dong From Princeton UniversityFred Douglis, Kai Li, Hugo Patterson, Sazzala Reddy, Philip Shilance From EMC

2011. 04. 21 (Thu)Kwangwoon univ. SystemSoftware Lab.

HoSeok Seo1

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Introduction This paper proposes

a deduplication cluster storage system having a primary node with the a hard disk

Basically cluster storage systems are... a well-known technique to increase capacity

but have 2 problems- less deduplication than the single node system- not exhibit linear performance

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Introduction Goal

Scalable Throughput- using Super-chunk for data transfer- maximize the parallelism of disk I/O by balanced routing data to nodes- reduce bottleneck of disk I/O utilizing cache locality

Scalable Capacity- using a cluster storage system- route repeated data to the same node- maintain the balanced utilization between nodes

High Deduplication like single node system- using a super-chunk that consist of consecutive chunks

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Introduction Chunk

Definition- A segment of Data stream

Merits- when a chunk size is small...

• Show high deduplication

- when a chunk size is big...• Show high throughput

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Introduction Super-chunk

Definition- Consist of consecutive chunks

Merits- Maintain high cache locality- Reduce system overhead- Get similar deduplication rate of chunk

Demerits- Risk of duplication creation- Can result in imbalance utilization between nodes

Issues of super-chunk- How they are formed- How they are assigned to nodes- How they route super-chunks to nodes for a balance

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Dataflow of Deduplication Cluster

1. Divide Data Streams into Chunks

2. Create fingerprints of chunks

3. Create a super-chunk

4. Select a representative for a super-chunk in chunks

5. Route a super-chunk to one of nodes

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Deduplication flow at a node (cont.)

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Deduplication flow at a node

Dup?at dedup

logic

Finger-print in cache?

Finger-print in index?

Write Fingerprint & Chunkto a container

no

yes

no

Dediplication Done

yesno

Is a con-tainer full?

Write a container to a disk

A chunk

Load fingerprints were written at the

same time to cache

yes

Color box means that it re-quires disk access

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What is Container? Container

Definition- fixed-size large pieces in a disk- consist of two part : Fingerprint & Chunk Data

Usage- Use it to store Fingerprint & Chunk of non-duplicated data into a disk

Finger-prints Chunk Data

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Issue 1 : How super-chunk are formed? How super-chunk are formed?

Determine an average super-chunk size- Experimented with a variety size from 8KB to 4MB- Generally 1MB is a good choice

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Issue 2 :How they assigned to nodes Use Bin Manager running on master node Bin Manager executes rebalance between nodes by bin migration(For stateless

routing)

1. assign number of binto a super-chunk

node 1

node 2

node 3

node N

bin1 bin2 bin3 ... bin Mnode1 node2 node3 ... node N

bin man-ager

M>N

a super-chunk

2. find a node by number of bin

3. route a super-chunk to a node

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Issue 3 :How they route super-chunks to nodes for bal-ance Use two DATA Routing to overcome demerits of super-chunk

stateless technique with a bin migration- light-weight and well suited for most balanced workloads

stateful technique- Improve deduplication while avoiding data skew

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Stateless Technique Basic

1. Create fingerprint about each chunks 2. Select a representative fingerprint in fingerprints 3. allocate a bin to super-chunk ( such mod #bin )

How to Create fingerprint Hash all of chunk ( a.k.a hash(*) ) Hash N byte of chunk ( a.k.a hash(N) ) ※ Use SHA-1 Hash function

How to select representative fingerprint first maximum minimum

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Stateful Technique (cont.) Merits compare to Stateless

Higher Deduplication like single node backup system Balanced overload Bin migration no longer needed

Demerits Increased operations Increased cost of memory or communication

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Stateful Technique Process

Calculate "weighted voting"

Select a node that has the highest weighted voting

number of match * overloaded value

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number of match : number of duplication chunk at each nodeoverloaded value : overloaded utilization of node relative to the average storage utilization

1.0

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Datasets

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Evaluation Metrics Capacity

Total Deduplication (TD)- the original dataset size % deduplication size

Data Skew- Max node utilization % avg node utilization

Effective Deduplication (ED)- TD % Data Skew

Normalized ED- Show that how much deduplication close to a single-node system

Throughput # of on-disk fingerprint index lookups

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Experimental Results :Overall Effectiveness Using Trace-driven simulation

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Experimental Results :Overall Effectiveness

with mig

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Experimental Results :Feature Selection

HYDRAstor- Routing chunks to nodes according to content- Good performance- Worse deduplication rate due to 64KB chunks

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Experimental Results :Cache Locality and Throughput

Logical Skew : max(size before dedupe) / avg ( size before dedupe)

Max lookup : maximum normalized total number of fingerprint index lookupsED : Effective Deduplication

(32node) (32node)

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Experimental Results :Effect of Bin Migration

The ED drops between migration points due to increasing skew.

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Summary

Stateless Stateful

Small Clusters

LargeClusters ALL

Deduplication Good Bad Good

Data Skew Good Bad Good

Overhead Good Good Bad

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Conclusion 1. Using Super-chunks for data routing is superior to using individ-

ual chunks to achieve scalable throughput while maximizing dedu-plication

2. The stateless routing method (hash(64)) with bin migration is a simple and efficient way

3. The effective deduplication of the stateless routed cluster may drop quickly as the number of nodes increases.To solve this problem, proposed stateful data routing approach.Simulations show good performance when using up to 64 nodes in a cluster