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Page 1: 2012 open storage summit   keynote

CCA - NoDerivs 3.0 Unported License - Usage OK, no modifications, full attribution** All unlicensed or borrowed works retain their original licenses

Cloud Storage Futures (previously: Designing Private & Public Clouds)

May 22nd, 2012

Randy Bias, CTO & Co-founder

@randybias

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Part 1:

The Two Cloud Architectures

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A Story of Two Clouds

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Scale-out

Enterprise

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... Driven by Two App Types

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New Elastic Apps

Existing Apps

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Cloud Computing ... Disrupts

EnterpriseComputing

"Client-Server"

CloudComputing

"Web"

MainframeComputing

"Big Iron"

1960 1980 2000 2020

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Cloud Computing ... Disrupts

Disruption

EnterpriseComputing

"Client-Server"

CloudComputing

"Web"

MainframeComputing

"Big Iron"

1960 1980 2000 2020

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Page 7: 2012 open storage summit   keynote

Cloud Computing ... Disrupts

Disruption Disruption

EnterpriseComputing

"Client-Server"

CloudComputing

"Web"

MainframeComputing

"Big Iron"

1960 1980 2000 2020

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Page 8: 2012 open storage summit   keynote

Cloud Computing ... Disrupts

Disruption Disruption Disruption

EnterpriseComputing

"Client-Server"

CloudComputing

"Web"

MainframeComputing

"Big Iron"

1960 1980 2000 2020

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Mainframe“big-iron”

Enterprise“client/server”

Cloud“scale-out”

SLA

Scaling

Hardware

HA Type

Software

Consumption

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IT – Evolution of Computing Models

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99.999

Vertical

Custom

Hardware

Centralized

Centralized Service

Mainframe“big-iron”

Enterprise“client/server”

Cloud“scale-out”

SLA

Scaling

Hardware

HA Type

Software

Consumption

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IT – Evolution of Computing Models

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99.999

Vertical

Custom

Hardware

Centralized

Centralized Service

99.9

Horizontal

Enterprise

Software

Decentralized

Shared Service

Mainframe“big-iron”

Enterprise“client/server”

Cloud“scale-out”

SLA

Scaling

Hardware

HA Type

Software

Consumption

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IT – Evolution of Computing Models

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99.999

Vertical

Custom

Hardware

Centralized

Centralized Service

99.9

Horizontal

Enterprise

Software

Decentralized

Shared Service

Always On

Commodity

Distributed

Self-service

Mainframe“big-iron”

Enterprise“client/server”

Cloud“scale-out”

SLA

Scaling

Hardware

HA Type

Software

Consumption

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IT – Evolution of Computing Models

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Enterprise Computing(existing apps built in silos)

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Cloud Computing(new elastic apps)

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Traditional apps Elastic cloud-ready apps

APPS

INFRA

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Scale-out apps require elastic infrastructure

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Traditional apps Elastic cloud-ready apps

APPS

INFRA

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Scale-out Cloud Technology

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Scale-out Principles

• Small failure domains

• Risk acceptance vs. risk mitigation

• More boxes for throughput & redundancy

• Assume app manages complexity:

• Data replication

• Assumes infrastructure is unreliable:

• Server & data redundancy

• Geo-distribution

• Auto-scaling

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What’s a failure domain?

• “Blast radius” during a failure

• What is impacted?

• Public SAN failures:

• FlexiScale SAN failure in 2007

• UOL Brazil in 2011:

• http://goo.gl/8ct9n

• There are many more

• Enterprise HA ‘pairs’ typically support BIG failure domains

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Two Diff Arches for Two Kinds of Apps

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Loca

tion

of c

ompl

exity

Infra

App

Up OutPrimary scaling dimension

Elastic Scale-out Cloud

“Enterprise” Cloud

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Part 2:

Storage Architectures Are Changing

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Uptime in InfraEvery part is redundant

Data mgmt in InfraBigger SAN/NAS/DFS

Two Diff Storages for Two Kinds of Clouds

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“Scale-out” Storage

“Classic”Storage

Uptime in appsMinimal h/w redundancy

Data mgmt in appsSmaller failure domains

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Difference in Tiers

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Tier $ Purpose Classic Scale-out

1 $$$$Mission Critical

•SAN, then NAS•10-15K RPM•SSD

•On-demand SAN (EBS)•DynamoDB (AWS)•Variable service levels

2 $$ Important •NAS then SAN•7.2K RPM

•DAS•App / DFS to scale out

3 $Archive & Backups

•Tape•Nearline 5.4K •Object Storage

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• In scale-out systems, apps are managing the data:

• Riak / Scale-out distributed data store

• Hadoop+HDFS / Scale-out distributed computation systems

• Cassandra / Scale-out distributed columnar database

The Biggest Difference is in Where Data Management Resides

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Cassandra / Netflix use case

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• 3 x Replication

• Linearly scaling performance

• 50 - 300 nodes

• > 1M writes/second

• When is this perfect?

• data size unknown

• growth unknown

• lots of elastic dynamism

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Cassandra / Netflix use case

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• DAS (‘ephemeral store’)

• Per node perf is constant

• disk

• CPU

• network

• Client write times constant

• Nothing special here

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Cassandra / Netflix use case

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• On-demand & app-managed

• Cost per GB/hr: $.006

• Cost per GB/mo: $4.14

• Includes: storage, DB, storage admin, network, network admin, etc. etc. etc.

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Part 3:

Scale-out Storage ... Now & Future

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Only Change is Certain

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There are a few basic approaches being taken ...

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Dedicated Storage SW

9K Jumbo Frames

SSD caches (ZIL/L2ARC)

No replication

Max HW redundancy

• In-rack SAN == faster, bigger DAS w/ better stat-muxing

• Accept normal DAS failure rates

• Assume app handles data replication

• Like AWS ‘ephemeral storage’

• KT architecture

• Customers didn’t “get it”

• “Ephemeral SAN” not well understood

Scale-out SAN

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AWS EBS - “Block-devices-as-a-Service”

• Scale-out SAN (sort of)

• Block scheduler

• Async replication

• Some failure tolerance

• Scheduler:

• Allocates customer block devices across many failure domains

• Customer run RAID inside VMs to increase redundancy

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Core Network1

2

31

2

3

RAC

K

RAC

K

EBS Clusters

1

2

3

4

5

6

7

8

Intra-rack cluster async replication

Inter-rack cluster async replication

VM1

VM2

N1

N2

EBSScheduler

Cloud Control System

API

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DAS + Big Data(Storage + Compute + DFS)

• Storage capability:

• Replication

• Disk & server failure

• Data rebalancing

• Data locality

• rack awareness

• Checksums (basic)

• Also:

• Built in computation

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Distributed File Systems (DFS) over DAS

• Storage capability:

• Replication

• Disk & server failure

• Data rebalancing

• Checksums (w/ btrfs)

• Block devices

• Also:

• No computation

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ceph architecture

✴ Looks familiar doesn’t it?

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Why is DFS at the Physical Layer Dangerous for Scale-out?

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DFS

==

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DAS + Database Replication / Scaling

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• Storage capability:

• Async/Sync Replication

• Server failure

• Checksums (sort of)

• Also:

• Std RDBMS

• SQL i/f

• Well understood

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• Storage capability:

• Replication

• Disk & server failure

• Data rebalancing

• Checksums (sometimes)

• Also:

• Looks like a big web app

• Uses a DHT/CHT to ‘index’ blobs

• Very simple

Object Storage

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Where does OpenStorage Fit?

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Scale-out Solution

Purpose / Tier Virtual or Physical?

Fit

Scale-out SAN Tier-1/2 Physical In-rack SAN

EBS Tier-1 PhysicalEBS Clusters

(scale-out SAN)

DAS+BigData Tier-2 VirtualReliable, bit-rot resistant DAS

DAS+DFS Tier-2 Physical / VirtualReliable, bit-rot resistant DAS

(unproven)

DAS+DB Tier-2 Virtual In-VM reliable DAS

Object Storage Tier-3 PhysicalReliable, bit-rot resistant DAS

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Summarizing ZFS Value in Scale-out

• Data integrity & bit rot an issue that few solve today

• Most SAN/NAS solutions don’t ‘scale down’

• Commodity x86 servers are winning

• There are two scale-out places ZFS wins:

• Small SAN clusters

• Best DAS management

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Summary

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Conclusions / Speculations

• Build the right cloud

• Which means the right storage for *that* cloud

• A single cloud might support both ...

• Open storage can be used for both ...

• ... WITH the appropriate design/forethought

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Q&A@randybias


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