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Intelligent Cloud Storage EnablementPresented by Mike Wipperfeld

July 22, 2014

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Challenges

• Data continues to grow exponentially – especially with the advent of social content

• Approximately 70% of data is unstructured

• Impact on - Storage costs and

management- Data Protection SLAs

• New deployment options such as cloud provide alternatives

Corporate knowledge has grown from 75 Exabyte in 2007 to 580 Exabyte in 2011 or a CAGR of 67%

- Forrester

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• What files ? • How ?

• Approx. 65% of the total volume is rarely used

• Challenge:– Identify the rarely accessed data– Transport to target– Keep available to users /applications

• Results:– Optimized storage utilization– Reduced operational and

administrative costs

ON PREMISE STORAGE

Typical Scenario

Window Server NAS Filer

NetApp

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you could classify information based on external attributes: analyze, monitor, report?

you could select and move files based on centrally defined policies to second tier storage in the cloud?

you could make it completely transparent?

you could address compliance and access controls through classification based on content analysis

Challenges

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• You can migrate files – transparent for the user - into multiple storage locations

• Users and applications will not notice any difference• With cloud storage, you can integrate the public

cloud with on-premises storage to:• reduce datacenter infrastructure complexity• maximize data protection• reduce overall storage total cost of ownership (TCO) by

60-80%• provision storage more rapidly to reclaim IT time cycles

• With the optional DocuClassify feature, the selection of files can be done based on its content and business values

Enabling the fast introduction of integrated cloud storage without disturbing users and IT operations

Intelligent Content Management

More Intelligent Content Management

Window Server NAS Filer

NetApp

Cloud Storage

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• Analysis & ClassificationAnalysis and classification of the file inventory with customer-defined rules.

• MigrationCreation of a copy on secondary storage in accordance with rules based on criteria such as meta-data or content-based classification.

• ReleaseExchange of the original file with a small reference file which looks and acts just like the original.

• RecallRestoration of the original file from secondary storage upon request.

After the initial set-up, these processes run independent and transparently in the background.

Analysis & Classification

ReleaseRecall

Migration

Identifying the right files

Moving files transparently to secondary storage

Storage Optimization – Functional principle in four processes

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Purpose of the assessment: 

•Analyze the current file structure

•Illustrate results in multiple ways for examination

•Make recommendations regarding future HSM or archiving options

•Make recommendations about optimal usage of file classification

Quantitative Analysis

Number and volume of:

•Server

•Disks

•Shares

•Directories

•Capacity Growth

Qualitative Analysis

•Distribution by Age

•Distribution by Size

•HSM/Archiving Simulation

•File types

•Users

•Groups

•Duplicates

File Assessment– Purpose and scope

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Analysis – Date Analysis & Assessment

Quantitative• Server• Disks• Shares• Directories• Capacity Growth • Forecast

Qualitative Analysis• Distribution by Age• Distribution by Size• HSM Simulation• Analysis of Data Types• User Distribution• Group Distribution• Duplicates

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Filter for “last modified” > 360 days> 700 GB can be released

Simulate Results of a File MigrationQuantify Storage Space Savings

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Continuous Tracking of Storage Growth Trigger action when threshold is reached

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Primary Storage

Secondary Storage

Filter Options

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• Reference files are transparent to end-users and applications• No change in file name, extension, size or date• Reference files act like files, meaning they can be copied, moved, renamed…• Applications can work with the archived files without any customisation requirements

The only visible changes: Attribute “O” for offline files and a clock symbol on the icon -> NTFS native feature

Reference Files A Small But Important Detail

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Migration Adapter for NetAppProviding HSM functionality for NetApp filers CC NodeResponsible for all DocuFile Migration Adapter operations and communication with the DocuSuite Control Center FPolicy NodeResponsible for file recalls via the FPolicy Server

FPolicy ServerDetects access to files residing on the ONTAP file system of the NetApp file server. In case a reference file is accessed, the DocuFile Migration Adapter initiates a recall from secondary storage

Typical Deployment and Main Components

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• Meta data based classification enables high performance on high volumes of data

• Pattern Matching is performant and exact when using classification criteria that can be expressed in regular expressions

• Machine Learning with linguistic-statistical analysis has universal applicability and delivers strong results with fuzzy classification criteria

• Partnerships with

– KPMG

– Deloitte

– Fontis International

Content Based ClassificationEnhances migration precision and effectiveness

metadata-based

metadata-based

... ...

Machine LearningMachine Learning

PatternMatchingPattern

Matching

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• Critical information that should not be moved to the cloud.

• Sensitive information that needs to be encrypted before moving it to the cloud.

• Important information that needs to be archived.

• Passive information that is little used and could be moved to IRM immediately.

DocuClassifyDocuClassify

Identifying the Right FilesOnly Classification Enables Automation

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Keep it simple

The Classification Cube® is a concept for an universal classification scheme for companies of all kinds which…

– is easy enough to be implemented quickly,

– is flexible enough to be adjusted and expanded in a simple way and

– meets the most significant and most urgent requirements.

Our cube is the key guideline which helps to finish the project successfully together with the document classes.

A cube as the solution

Easy Implementation with the Classification Cube®

A Universal Technical Approach

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(1) Any information object brings Metadata (e.g. location, name, creator etc.) with it. The actual set can vary with the type of information.

(2) Classification rule assigns a document type to the information object. Each DT comes with a set of properties which are used by the classification rule. The property values enrich the metadata of the information object.

(3) Any application can – based on the classification expressed in the properties values of a specific information object – trigger an action (e.g. archiving) for this object. This could be other DocuSuite modules or 3rd party applications.

Document Class

Document Class Classification

PropertiesClassification

Properties

MetadataMetadata

(1)

Classification Rules

Information Object

(File, Mail, SP, ...)

Information Object

(File, Mail, SP, ...)

(2)

Property Values

Property Values

(3rd Party) Actions

(3rd Party) Actions

(2)

(3)

Document Class Metadata Properties Actions

Invoice Location Retention If (retention>0) then archive

Engineering Plan (CAD) Location User

Project IDRestricted Access

If (Restricted Access) then block access on mobile devices

Document Classes and the Classification CubeThe Document Class Model

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BC

AC

Security SettingSecurity Setting: Confidential : Confidential ComplianceCompliance : 5 year retention : 5 year retention

Summary Project DocumentProcess of Tagging to the Information Object thru the ADS or Security Descriptor

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SharePoint

OfficeSharePoint…

SharePoint

OfficeSharePoint…

Target ArchivesVariable Retention period1 Year5 Years10 Years25 years

…

Target ArchivesVariable Retention period1 Year5 Years10 Years25 years

…

1. Selection2. Transport3. Report

Generation

Classification and File Movement

File/SharePoint Object Migration

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Introduce Cloud Storage using intelligent content management as an enabler

• Identify files to offload based on metadata such as age, location, type, ...

• Immediately start to offload your primary storage

• Harvest your benefits e.g. reduced storage TCO, better backup and DR

Add automatic classification for even more benefits

• Introduce file classification classes based on business demand

• Use self-learning, trainable classifier to automatically classify all files

• Automatically attach business value to every file and use it as a trigger to offload files

Leverage classification for security and governance task

• Introduce Dynamic Access Control to ease access rights management

• Increase security by automatic encryption of confidential files

• Boost effectiveness of any DLP solution

Starting small you can expand the capabilities of this system from storage optimization to classification based information governance.

Starting small you can expand the capabilities of this system from storage optimization to classification based information governance.

Progression: Introduction and Expansion

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Intelligent Content Management

•Significant cost reduction in storage infrastructure•Easy implementation with minimal admin effort•High degree of automation•Permanent availability of all relevant data•Highly scalable•Fulfillment of legal requirements•Higher transparency of unstructured data•Optimize use of resources and investments•Implementation of rule-based Information Lifecycle Management (ILM)

Classification

•Automated classification fulfills regulatory compliance and information security requirements relative to encryption and access control•Mitigate risk due to knowledge of the value of your information•Boosts in productivity due to higher transparency levels•Movement toward value-based information management

Company Confidential

Summary Advantages

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• Large medium-sized company based in the automation technology sector

• Head of Information Management Server

• 80 TB on Windows Server 2008 R2 and 2012

• Few relevant datasets should be moved to an external service provider

• Motivation: optimization of costs and change in awareness within the business departments

• No transparency in terms of data relevance

Challenge

• Initial on-site visit: More precise analysis of the demand, especially in terms of granularity

• Decision: classic HSM (without classification) vs. intelligent HSM (with classification)

• Presentation file assessment as starting point for analysis

• Solution:

– migration for transparent moving of data– classification (external attribute analysis,

reporting) and optional content classification

ApproachCustomer

Concrete Customer Request With Regard to ClassifictionStorage Optimization through moving of “irrelevant” data

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– Railway Engines– Trams– Train Engines– High Speed Trains– Transport Solutions

Rail Systems

– Trucks– Buses– Engine Brakes– Special Products

Commercial Brake Systems

The Knorr-Bremse Group is the world’s leading manufacturer of braking systems for rail and commercial vehicles, vehicle air conditioning systems andtorsional vibration dampers

Customer Reference

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– 120 TB total storage– 16.000 users, 120 locations– 8000 SAP users, 1000 CAD users

Key metrics:

– 6 NetApp V-Filer, 1 Windows File Server– Growth rate 30% per year– 24 TB ( 26,4 Million Files) managed by

DocuFile– Tier 1 Storage growth -> BU / Recovery

cost increase– Only a fraction of the files is in active use

201224 TB

201332 TB

201443 TB

201555 TB

Active Active Active Active

Typical Challenge

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Secondary Storage Tier

Primary Storage

– 39% of the managed storage volume has been migrated

– Individual quota between Server/Filer varies between 21% and 62%

– Successfully increased migration quota by fine tuning the migration policies

– Back-Up and Snapshot performance vastly improved

21 – 62% migrated

Results

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DocuLynx PortfolioAddressing Informance Governance and Lifecycle Challenges

DocuLynx Portfolio Enterprise Information Infrastructure

Solutions• DocuLynx 360- a Content Services Platform for

Smart Applications• Active Information Archiving Platformo DocuSuiteo DocHarbor/Haven• DocuClassify• DocuSearch Enterprise Information Application

Solutions• AP Automation• E-Signature Information Conversion Services

(eg.Scanning)

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Thank You!