Software Defined Storage - Open Framework and Intel® Architecture Technologies

Software Defined Storage - Open Framework and Intel® Architecture Technologies

Anjaneya “Reddy” ChagamPrincipal Engineer, Intel Data Center Group

Shayne HuddlestonInfrastructure Architect, Information Services, Oregon State University

DATS001

2

Agenda

• The problem

• Software Defined Storage (SDS) vision

• Why SDS matters?

• Customer perspective

• SDS gaps and Intel response

• Intel role in SDS

• Summary

3

Agenda

• The problem






• Summary

4

The Problem:From 2013 to 2020, the digital universe will grow by a factor of 10, from 4.4 ZB to 44 ZB

It more than doubles every two years.

Data needs are growing at a rate unsustainable with today’s infrastructure and labor costs

Source: IDC – The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things - April 2014

…cost challenges continue to grow…

….data complexity is increasing

…. and storage accounts for 40% of the data center budget.

5

The Problem with Storage Infrastructure

Storage Silos (Traditional)

• Application mapped to specific appliance

• Storage resources optimized to run specific workload

• Isolated storage resources

Challenges

• Cost of managing diverse storage solutions

- Data Growth

- Maintenance, Operations & Support

- Infrastructure

• Vendor lock-in

- Limited scalability

- Flexibility to innovate

• Need for massively shared data

- But not yet cloud ready

Traditional storage management is too complex and inefficient

6

Agenda

• The problem






• Summary

7

Software Defined Infrastructure (SDI)

PROVISIONING MANAGEMENTOrchestration provisions, manages and optimally

allocates resources based on the unique requirements of an application

POOLED RESOURCESNetwork, Storage and Compute elements are

abstracted into resource poolsStorage Network Compute

Services Delivery

Resource Pool

Orchestration Software

Infrastructure Attributes

Application A

Application B

Application C

Application D

Power Performance Security Thermals Utilization Location

SERVICE ASSURANCEPolicies and intelligent monitoring trigger dynamic

provisioning and service assurance as applications are automatically deployed and maintained

8

SDS – A Key Component of SDIDynamic, policy-driven storage resource management

Services Delivery

Software Defined Storage Controller

Orchestration Software

Infrastructure Attributes

App #1 App #2 App #3 App #4

Power Performance Thermals Utilization Location

Storage Systems

Latency Durability

SDS is a framework that delivers a scalable, cost-effective solution to serve the needs of tomorrow’s Data Center

Abstracting Software from Hardware, providing flexibility & scalability

Aggregating diverse provider solutions, increasing flexibility and drive down costs

Provisioning resources dynamically (pay-as-you-grow) increasing efficiency

Orchestrating application access to diverse storage systems through Service Level Agreements (SLAs), increasing flexibility and handle data complexity

9

SDS Architecture

Applications

SDS Controller

Node Node Node

Orchestrator

Storage System[Capacity]

Storage System[Performance]

Data Services

Storage System[SAN]

Storage System[NAS]

SDS Controller• Visibility and Control of ALL

storage resources• Communication between

Apps, Orchestrator and Storage Systems

• Allocates storage resources to meet SLA’s

Node NodeJBOD

Data Services• Application that runs in

data plane to optimize storage

• Ex: Predictive Analytics• Ex: De-Duplication• Ex: Tiering

……….. ………..

SDS : Consolidated Management of Scale-Out and Scale-Up Storage and plug into SDI

Compute ControllerSDN Controller

NASSAN

Northbound API

Southbound API

10

Agenda

• The problem






• Summary

11

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller

High PerfIOPS – 10K

(DC1)


(DC2)

Med PerfTpt – 100Mbps

(DC2)

Med PerfTpt – 50 Mbps

(DC1)

CapacityTpt – 5 Mbps

(DC1)

$$$ Gold $$ Silver $ Bronze

Example: Application provides storage pool

1

2

Gold

App 1

(OLTP)App 2

(Backup)

App 3

(Sharing)

Bronze Silver

Discover

Compose

3 Assign

12

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)



1

2

App 1

(OLTP)

App 2 (Backup)

App 3

(Sharing)

Bronze Silver

Discover

Compose

3 Assign

13

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)



1

2

App 1

(OLTP)

App 2 (Backup)

App 3

(Sharing)

Silver

Discover

Compose

3 Assign

14

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)



1

2

App 1

(OLTP)

App 2 (Backup)

App 3

(Sharing)

Discover

Compose

3 Assign

15

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


Example: Application provides SLO attributes (performance)

1

2

IOPS=7000

App 1 App 2 App 3

Tpt - 2 Mbps Tpt – 30Mbps

Discover

Compose

3 Assign

16

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)



1

2

App 1

App 2 App 3

Tpt - 2 Mbps Tpt – 30Mbps

Discover

Compose

3 Assign

17

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)



1

2

App 1 App 2

App 3

Tpt – 30Mbps

Discover

Compose

3 Assign

18

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)



1

2

App 1 App 2App 3

Discover

Compose

3 Assign

19

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


Example: Application provides SLO attributes (performance, client caching)

1

2

IOPS=7000App 1

Discover

Compose

3 Assign

Client Cache = 200GBApp1

20

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


Example: Application provides SLO attributes (performance, client caching)

1

2

App 1

Discover

Compose

3 Assign

Client Cache = 200GB

App1

21

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

IOPS=170K (R/W)App 1

Discover

Compose

3 Assign

Example: Application provides SLO attributes (ephemeral)

Ephemeral

22

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

IOPS=170K (R/W)

Discover

Compose

3 Assign

App1

Example: Application provides SLO attributes (ephemeral)

23

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

IOPS=170K (R/W)App 1

Discover

Compose

3 Assign

Example: Application provides SLO attributes (ephemeral, local protection)

Persistent, Local Protection

24

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

IOPS=170K (R/W)

Discover

Compose

3 Assign

App1

Example: Application provides SLO attributes (ephemeral, local protection)

25

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

IOPS=9000App 1

Discover

Compose

3 Assign

Example: Application provides SLO attributes (IOPS, noisy neighbor policy)

Compute Controller

26

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

IOPS=9000

App 1

Discover

Compose

3 Assign

App1

Example: Application provides SLO attributes (IOPS, noisy neighbor policy)

Compute Controller

Qemu QoS Max IOPS=9000

27

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

Tpt – 2 MbpsApp 1

Discover

Compose

3 Assign

Example: Application provides SLO attributes (Throughput, noisy neighbor policy)

SDN

28

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

Tpt – 2 Mbps

App 1

Discover

Compose

3 Assign

App1

Example: Application provides SLO attributes (Throughput, noisy neighbor policy)

SDN

tc ul rate 2048kbit

29

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

Tpt – 2 MbpsApp 1

Discover

Compose

3 Assign

Example: Application provides SLO attributes (Performance but storage is full)

Free Pool

2

Policies

Policy #XIf 80% capacityThen mark system unavailable

81% utilization

30

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

App 1

Discover

Compose

3 Assign


Free Pool

2

Policies


81% utilization

31

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

App 1

Discover

Compose

3 Assign


Free Pool

2

Policies


32

Why SDS Matters?

Storage System

[Distributed]

Storage System 1

[SAN]

Storage System[NAS]

Storage System

[Distributed]

Storage System

[Distributed]

SDS Controller


(DC1)


(DC2)


(DC2)


(DC1)


(DC1)


1

2

Discover

Compose

3 Assign


Free Pool

2

Policies


App 1

33

Agenda

• The problem






• Summary

34

Perspectives from Oregon State University (OSU) IT on SDS

20

35

Oregon State University - Introduction

A top research university

• 28,000 Students, 3,500 faculty, 5 datacenters

• Top Carnegie Foundation class of research universities

• One of only two universities in the U.S. to be a Land, Sea, Space and Sun Grant university

• Statewide economic footprint of $2.06 Billion

• 15 Agricultural Experiment Stations; two remote campuses, Hatfield Marine Sciences Center in Newport,, and OSU-Cascades in Bend

21

36

Oregon State University - Information Services

• Central IT provider for OSU (Infrastructure as a Service - storage, compute, networking, facilities)

• Mission is to enable and support Students, Faculty & Science

• Multitude of different workloads that we must support

- Long term digital archive

durable, replicated, WORM

- Virtual Machines

- Virtual Desktops (VDI)

- Research workloads

Genome sequencers generate 20TB/hour sustained high bandwidth write

- OLTP - Oracle, MSSQL, mySQL

- Business analytics (Hadoop, etc.)

22

37

Oregon State University – DC Infrastructure

• 3 central data centers on campus

• Infrastructure

- Heavily virtualized

- High availability metro cluster architecture

- Home grown orchestration layer

- Puppet configuration management

- Active/active replicated block storage

- SAN, NAS storage islands

- Distributed storage (piloting)

23

38

Oregon State University – IT Challenges

24

• Storage demand is growing faster

- Growing at over 40%/year

• Storage is not optimized for diverse workloads

• Traditional storage management is costly and hard to scale

• Lot of touch points to administer, provision, and consume storage resources

• Allocation of storage resources to user workloads is 1 to 1

• IT budget is relatively flat

• Majority of storage cost is hardware and operations

39

Oregon State University – Focus Areas & Plans

25

• Storage growth explosion

- Shift to cost optimized, “pay as you grow” storage architecture

- Distributed storage on standard high volume servers (open source focus)

• Storage optimized for diverse workloads

- SLA and policy driven storage resource allocation

- Storage allocation based on workloads, performance, cost and resiliency requirements

• Storage management

- All storage operations managed by single controller in the DC

Administrator should not have to go beyond the controller except in extraordinary circumstances

- Recognition and classification of performance problems in central dashboard

- Fully automated provisioning and simplified maintenance

New capacity automatically added to appropriate service and controlled by policies

OSU is collaborating with Intel to address storage challenges via open source SDS controller initiative

40

Agenda

• The problem






• Summary

41

SDS Gaps

SDS Controller

Clear Standards

Standards exist, but they are not focused on SDS, not organized together, and

are not complete

Industry wide focus needed to develop standards by either enhancing or developing new standards (e.g., SNIA, TOSCA, DMTF)

Node

Applications

Northbound API

Southbound API

Node Node

SDS Controller SDS Controller

Storage System(Capacity)

Storage System(Performance)

Node Arrays

Orchestrator

42

Storage Control Plane Standards – State & Gaps

Intel plans to work with standard bodies to address SDS gaps

Node

Applications

Northbound API

Southbound API

Node Node




Node Arrays

Orchestrator

Standard Gap

OASIS TOSCA

Applications provide storage requirements using SLOs

IETF Network focus - storage requirements (e.g. policies) not comprehended

CDMI Object storage focus – need extensions for block, file, etc.

SMI-S Appliance focus - requires changes for distributed storage usage

43

SDS Gaps

Clear Standards

SDS Controller

Open, federated “control plane” with pluggable architecture is needed for ecosystem innovation

Node

Applications

Northbound API

Southbound API

Node Node




Node Arrays

Orchestrator

Open SDS Controller does not

exist

44

SDS Controller

Storage

Orchestration

SDN Controller

Storage Systems

Distributed Storage

SLOs File/BlockObject

Service/ Tenant Portal

REST APIs

Users

Storage Hardware,

Media

Unprovisioned

GoldLatency – 5msIOPS – 20000Access - block

SilverLatency – 10msIOPS – 1000Access - block

BronzeLatency – 50msThroughput – 100MbpsAccess - object

Storage Arrays

File/Block

MongoDB* eCommerce

Media Store

SLOs SLOs

Distributed Storage

Compute Controller

45

SDS Controller

Storage

Orchestration

SDN Controller

Storage Systems

Distributed Storage


SLAs

Applica-tions


REST APIs

Users

Storage Hardware,

Media

Unprovisioned




Storage Arrays

File/Block

MongoDB* eCommerce

Media Store

SLOs SLOs

Distributed Storage

Compute Controller

46

SDS Controller

Storage

Orchestration

SDN Controller

Storage Systems

Distributed Storage


SLAs

Applica-tions

SLA SLO


REST APIs

Users

Storage Hardware,

Media

Unprovisioned




Storage Arrays

File/Block

MongoDB* eCommerce

Media Store

SLOs SLOs

Distributed Storage

Compute Controller

47

SDS Controller

Storage

Orchestration

SDN Controller

Storage Systems

Distributed Storage


SLAs

Applica-tions

SLA SLO


REST APIs

Users

Storage Hardware,

Media

Unprovisioned




Storage Arrays

File/Block

MongoDB* eCommerce

Media Store

Provisioning

QoS

SDS Controller

Control Plane

Policy MgmtSLA Storage

SLOMonitoring

REST APIs

SLOs SLOs

Distributed Storage

Compute Controller

48

SDS Controller

Storage

Orchestration

SDN Controller

Storage Systems

Distributed Storage


DataPlane

SLAs

Applica-tions

SLA SLO


REST APIs

Users

Storage Hardware,

Media

Unprovisioned




Storage Arrays

File/Block

MongoDB* eCommerce

Media Store

Provisioning

QoS

SDS Controller

Control Plane


SLOMonitoring

REST APIs

SLOs SLOs

Distributed Storage

Compute Controller

49

SDS Controller

Storage

Orchestration

SDN Controller

Storage Systems

Distributed Storage


DataPlane

SLAs

Applica-tions

SLA SLO


REST APIs

Users

Storage Hardware,

Media

Unprovisioned

Data Services

(optional)




Storage Arrays

File/Block

MongoDB* eCommerce

Media Store

Provisioning

QoS

SDS Controller

Control Plane


SLOMonitoring

REST APIs

SLOs SLOs

Distributed Storage

Compute Controller

50

Open Source SDS Controller - PrototypeHorizon

Dashboard

SDS Controller

Cinder Manila GlanceObject

Storage

Command Line Interface

Neutron

Nova

Ke

yst

on

e

Ceilometer

Distributed Storage

SAN Distributed Storage

NAS Distributed Storage

Distributed Storage

2

1

1 SDS controller discovers storage systems and capabilities (e.g., perf, capacity, tiers, etc.)

Admin composes storage pools (e.g., gold, silver, bronze)

Application requests storage service using SLOs.

Controller allocates storage volume from pool that can best service the request.

Storage gets assigned to Nova or App in VM.

Controller works with compute, network to set QoS.

2

1 1 13

3

4

4 4

4

Develop key requirements by working with ecosystem and finalize open source enabling plans in 2H’14

51

Agenda

• The problem






• Summary

52

How is Intel Helping?

Virtual Storage Manager (VSM)†

Plug-ins (e.g. Intel ISA-L Open

Source Version)COSBench

Object Storage RA Virtual Block RA

Silicon innovations, software optimizations to accelerate Open source Software Defined Storage

Swift Ceph Cinder

Media Transcoding VDI

INTEL®

TRUE SCALEINTEL®

SSDINTEL®

ETHERNET

Intel® Enterprise Edition for Lustre* software

Intel® Data Plane Development Kit

•Intel® Intelligent Storage Acceleration Library (Intel® ISA-L)•Intel® Cache Acceleration Software (Intel® CAS)

STORAGE

Intel® Platform Storage Extensions

Intel Ingredients

(Silicon, Platforms, Software)

NETWORK

Intel® Open Network PlatformIntel® QuickAssist AcceleratorsIntel® Silicon Photonics

COMPUTEIntel® Processor GraphicsIntel® Trusted Execution Technology (Intel® TXT)Intel® AVXIntel® AES New Instructions

Intel® Media SDKfor Servers

Intel® Advanced Vector Extensions (Intel® AVX); †Virtual Storage Manager (VSM) to be open sourced in Q4’14

Open Source(Storage)

Reference Architectures

53

Agenda

• The problem






• Summary

54

Summary

• Software Defined Storage is needed to address tomorrow’s challenges

- Data needs are growing at a rate unsustainable with today’s infrastructure and labor costs

- Traditional storage silos drives management complexity and inefficient

• SDS is a framework aimed at serving the needs of emerging storage requirements. But there are gaps -

- Industry wide focus is needed to create standards for application and storage system interoperability

- Open, federated “control plane” that provisions, monitors, ensures SLAs are needed

• Intel is working with community and ecosystem to address these gaps

55

Call to Action

• Take advantage of SDS disruptive wave

- Consider SDS POCs in Q1’15 (based on open source controller)

• Engage with the community to address storage challenges

- Provide feedback on storage requirements

- Help us drive open source, standards based Software Defined Storage controller

• Innovate storage offerings with Open source framework

56

Additional Sources of Information

• A PDF of this presentation is available from our Technical Session Catalog: www.intel.com/idfsessionsSF. This URL is also printed on the top of Session Agenda Pages in the Pocket Guide.

• Demos in the showcase –

- Transform storage with Software Defined Storage controller, Swift and Ceph (Booth: 122)

- Fujitsu Hyperscale*: New software-based distributed storage system featuring Intel® Virtual Storage Manager software (Booth: 123)

- Making it Real: Rack Scale Architecture for Software Defined Infrastructure and Orchestration (Booth: 120)

http://www.intel.com/idfsessionsSZ

57

Additional Sources of Information

Poster Chats:

• DATC003 - Optimizing Ceph with Intel® Technologies

- Data Center & SDI Community, Station 2, Tuesday, 3:00pm – 5:00pm

• DATC010 - Improving Storage Performance Through I/O Hinting

- Data Center & SDI Community, Station 2, Tuesday, 5:00pm – 7:00pm

58

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Risk FactorsThe above statements and any others in this document that refer to plans and expectations for the second quarter, the year and the future are forward-looking statements that involve a number of risks and uncertainties. Words such as “anticipates,” “expects,” “intends,” “plans,” “believes,” “seeks,” “estimates,” “may,” “will,” “should” and their variations identify forward-looking statements. Statements that refer to or are based on projections, uncertain events or assumptions also identify forward-looking statements. Many factors could affect Intel’s actual results, and variances from Intel’s current expectations regarding such factors could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the following to be important factors that could cause actual results to differ materially from the company’s expectations. Demand for Intel's products is highly variable and, in recent years, Intel has experienced declining orders in the traditional PC market segment. Demand could be different from Intel's expectations due to factors including changes in business and economic conditions; consumer confidence or income levels; customer acceptance of Intel’s and competitors’ products; competitive and pricing pressures, including actions taken by competitors; supply constraints and other disruptions affecting customers; changes in customer order patterns including order cancellations; and changes in the level of inventory at customers. Intel operates in highly competitive industries and its operations have high costs that are either fixed or difficult to reduce in the short term. Intel's gross margin percentage could vary significantly from expectations based on capacity utilization; variations in inventory valuation, including variations related to the timing of qualifying products for sale; changes in revenue levels; segment product mix; the timing and execution of the manufacturing ramp and associated costs; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials or resources; and product manufacturing quality/yields. Variations in gross margin may also be caused by the timing of Intel product introductions and related expenses, including marketing expenses, and Intel's ability to respond quickly to technological developments and to introduce new products or incorporate new features into existing products, which may result in restructuring and asset impairment charges. Intel's results could be affected by adverse economic, social, political and physical/infrastructure conditions in countries where Intel, its customers or its suppliers operate, including military conflict and other security risks, natural disasters, infrastructure disruptions, health concerns and fluctuations in currency exchange rates. Intel’s results could be affected by the timing of closing of acquisitions, divestitures and other significant transactions. Intel's results could be affected by adverse effects associated with product defects and errata (deviations from published specifications), and by litigation or regulatory matters involving intellectual property, stockholder, consumer, antitrust, disclosure and other issues, such as the litigation and regulatory matters described in Intel's SEC filings. An unfavorable ruling could include monetary damages or an injunction prohibiting Intel from manufacturing or selling one or more products, precluding particular business practices, impacting Intel’s ability to design its products, or requiring other remedies such as compulsory licensing of intellectual property. A detailed discussion of these and other factors that could affect Intel’s results is included in Intel’s SEC filings, including the company’s most recent reports on Form 10-Q, Form 10-K and earnings release.

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Backup

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SDS – Industry Definitions

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SDS – Industry DefinitionsIDC: “Any storage software stack that can be installed on any commodity resources (x86 hardware,

hypervisors, or cloud) and/or off-the-shelf computing hardware and used to offer a full suite of storage services and federation between the underlying persistent data placement resources to enable data mobility of its tenants between these resources.”

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SNIA: Software-defined storage (SDS) is a term for computer data storage technologies which separate storage hardware from the software that manages the storage infrastructure. The software enabling a

software-defined storage environment provides policy management for feature options such as deduplication, replication, thin provisioning, snapshots and backup.

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Wikipedia: Software-defined storage (SDS) is a term for computer data storage technologies which

separate storage hardware from the software that manages the storage infrastructure.

Characteristics: 1) Abstraction of logical storage services and capabilities from the underlying physicalstorage systems, and in some cases pooling across multiple different implementations. 2) Automationwith policy-driven storage provisioning with service-level agreements replacing technology details. 3)

Commodity hardware with storage logic abstracted into a software layer. 4) Scale-out storage architecture.

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Wikipedia: Software-defined storage (SDS) is a term for computer data storage technologies which

separate storage hardware from the software that manages the storage infrastructure.

Characteristics: 1) Abstraction of logical storage services and capabilities from the underlying physicalstorage systems, and in some cases pooling across multiple different implementations. 2) Automationwith policy-driven storage provisioning with service-level agreements replacing technology details. 3)

Commodity hardware with storage logic abstracted into a software layer. 4) Scale-out storage architecture.

Intel’s definition includes “all” above elements but with emphasis on end to end storage management at a data center level

Software Defined Storage - Open Framework and Intel® Architecture Technologies

Technology

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