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Data Compression and Deduplication

© 2010 Cisco Systems, Inc. All rights reserved.LOC 2010 2

Data Redundancy Elimination Landscape

Array Filer

Hosts

(Inline and

Offline)

Network

(Inline)

Storage

(Inline and

Offline)

Data Domain

Netapp’s A-SIS

VMWARE DeDE

IBM DDE for Storage Tank

Solaris ZFS

Cisco MDS + WAAS

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Cisco’s Focus Areas for

Redundancy Elimination

Data Center

Cloud Deployments

Data Center to Data Center Connectivity

Branch Office Connectivity

Disaster Recovery using long distance replication Data Center

Cloud

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Challenges in Deduplication

CPU Intensive Chunking Operations

CPU Intensive Fingerprint Calculation such as SHA-1

Managing Large Indexes in Memory as well as in Persistent Storage

Reducing Disk I/O for Index Lookup

Load Balancing and Scalability in shared SAN environments

Inter Host communication

Host based deduplication for Shared File Systems

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Cisco’s Network Centric Redundancy Elimination

Fully Integrated System for lowering WAN OPEX

Deduplication

Compression

TCP optimization

Encryption

Hardware Assistance for Compression

Scope for Hardware Assists in Deduplication

Coordination less approach for Shared SAN file systems

Application/Host/Array Agnostic

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WAN Optimization

TCP Optimizations

IP WANMDS

Interconnecting Data Centers using MDS 9000

DWDM Optical MAN

SAN Extension

FCIP protocol for SAN extension

Secondary Data CenterPrimary Data Center

MDS

RedundnacyElimination

Hardware Compression

Security

Encryption

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Cisco WAAS for Interconnecting Data Centers

Data Redundancy Elimination includes

Data Deduplication: application-agnostic technique eliminates redundant data from TCP streams providing up to 100:1 reduction

Persistent LZ Compression: session-based compression provides up to an additional 10:1 compression even for messages that have been optimized by deduplication

Dedupe Dedupe

LZ LZ

Synchronized

Compression

History

WAN

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DC-DC Replication Using Cisco WAAS

WAN

Cisco

Catalyst

6500

Cisco

Catalyst

6500

Cisco

WAAS

Cisco

WAASGigE GigE

Storage

arrays

Storage

arrays

DATA CENTER (Primary) DATA CENTER (Backup)

• Ease of deployment

• Integration with existing network topologies

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Storage Replication in WCCP Mode

WAN

Cisco

Catalyst

6500

Cisco

Catalyst

6500

Cisco

WAAS

GigEGigE

WCCPWCCP

Storage

arrays

Storage

arrays

Higher availability and scalability

through N+1 clustering

Cisco

WAAS

DATA CENTER (Primary) DATA CENTER (Backup)

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Storage Replication using Cisco MDS and WAAS

WAN

Cisco

Catalyst

6500

Cisco

Catalyst

6500

Cisco

WAAS

Cisco

WAAS

GigE GigE

Storage

arraysStorage

arrays

Cisco MDS 9000

with IP Service

Modules

Cisco MDS 9000

with IP Service

Modules

FCIP

FCFC

FCIP

• Improve end-to-end network resilience (against port

failure)

• Optimize data replication for legacy FC storage

• Support multiple heterogeneous FC storage arrays

DATA CENTER (Primary) DATA CENTER (Backup)

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Trends in Deduplication

Use of Solid State Disks to overcome Index Lookup and retrieval latency

Price Performance Trade off

Design Choices to overcome poor random write throughput of SSDs

Random write IOPS is still about 350

Adaptive deduplication that combines Inline and Offline methods

Data Agnostic Deduplication

Integrated approach for File and Block Deduplication

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