Presentazione PureStorage @ VMUGIT UserCon 2015

1 | © 2015 Pure Storage Inc.

NAND FLASH DEEP DIVE Vaughn Stewart VP, Enterprise Architect, Pure Storage [email protected] | @vStewed | vaughnstewart.com

REDESIGNING STORAGE INFRASTRUCTURE

mailto:[email protected]

http://twitter.com/vstewed

http://vaughnstewart.com/


~30% of workloads not virtualized

30-60% of VI admin’s time spent tending storage

Outages and downtime due to physical infrastructure

DISK STORAGE LIMITS VIRTUALIZATION


Flash is changing what we ‘know’ about storage


Top questions around all-flash arrays

1: I don’t need the performance of an All-Flash Array (AFA)

2: I cant afford an AFA

3: How do I benchmark an AFA


Maximum performance (IOPs) is easy to market

Performance is a sizing exercise… which increases in cooreleation with hardware purchased


A large impact on application performance is processing time

Reducing latency increases CPU utilization

Time to process = 100 µs + 5,000 µs + 100 µs = 5,200 µs

Process efficiency on CPU is wait time / processing time (200 µs / 5,200 µs) = 3.8%

Latency is the under recognized benefit of all-flash storage

~100 µs ~100 µs

~5,000 µs

Time

CPU

I/O

CPU

Request I/O

CPU

Process I/O

Storage

Service I/O




Time to process = 100 µs + 600 µs + 100 µs = 800 µs

Process efficiency on CPU is wait time / processing time (200 µs / 800 µs) = 25.0%


~100 µs ~100 µs

~600 µs

Time

CPU

I/O

CPU

Request I/O

CPU

Process I/O

Storage

Service I/O




Conclusion: all-flash storage optimizes CPU allowing for more IOPs per host (accelerate business) or the reduction in the # of hosts (reduce capex)


Avg. Disk Latency 5ms 10ms 15ms 20ms 30ms

CPU Utilization Benefit 6.6X 12.8X 19.1X 25.3X 37.9X


Re IOPs: All-flash can eliminate storage support calls

Tier 1 Legacy Disk Pure Storage FlashArray

26ms

.3-

.7ms

10 | © 2015 Pure Storage Inc. Source: ESG report on Pure Storage resilience with converged, virtualized workloads

Including Horizon View, Oracle, Exchange, and other VM workloads -- simultaneously

Large scale databases Desktop virtualization Business critical apps

Everything consolidated No performance impact

1500 high-performance virtual desktops

Flash provides the power to consolidate every workload

11 | © 2015 Pure Storage Inc. Source: ESG report on Pure Storage resilience with converged, virtualized workloads

Including Horizon View, Oracle, Exchange, and other VM workloads -- simultaneously

Some AFAs maintain performance even through failure


Data reduction technologies are the norm with AFAs

Deduplication: only store unique data blocks

Generally good at reducing VM binaries (OS and application files)

Finer granularity increases the data reduction rates

512B – Pure Storage

4KB – NetApp FAS

8KB – XtremIO

16KB – HP 3Par

Compression: store data in capacity optimized format

Generally good at reducing storage capacity of applications

Inline compression tends to provide moderate savings (2:1 common)

Vendors balance CPU / latency trade-offs

Post process compression tends to provide additional savings (3:1 common)


Thin provisioning: a data reduction technology?

Thin provisioning is a dynamic provisioning mechanism Eliminates the need to pre-allocate storage capacity

Some applications force thick provisioning Some arrays provide pattern removal to ensure thin provisioning

Question calculating thin provisioning in storage savings ratios

LUN

Data

LUN

Data

LUN

Data

2:

1

3:

1

4:

1

Unchanged

data capacity


Reduce your storage requirements up to 50% with vSphere 6

vSphere 6 supports T10 UNMAP in the VM

41.1% reduction in this example

Requirements:

vSphere 6

EnableBlockDelete

VM virtual HW 11

Thin VMDKs

SCSI-2 UNMAP in Guest OS

Windows 8+

No LINUX


Effective $/GB is the modern cost of storage


The Objective


Absurd Testing


Architecture matters – hardware, software and target use case

Always benchmark performance….

Under normal operations

Near system capacity

During system failures

During SW & HW upgrades / expansions

Always benchmark resiliency, availability and data management features

Loss of controller, loss of mulitple HDDs/SSDs

Data management like snapshots, replication, clones, etc

Recommend testing with actual data

Better to restore data from a backup than to create a synthetic test


Whenever possible test with actual application data

Most synthetic benchmark tools are not designed for data reducing arrays

Many implement data patterns that are ignored by disk arrays by are identified by data reducing arrays resulting in false results

Known problem apps: IOMeter, SQLIO, diskspd, ATTO, FIO

Recommended app: VDBench

Synthetic data aids in filling up arrays but have little validity to real applications

Synthetic reducible data sets may not match array’s reported savings

Non-reducible data set disregards the value software provides in an array architecture

May decrease bandwidth by ~3X-4X

May increase AFA garbage collection load by ~2X-8X


Flash for

the Price of Disk

$5/GB

MLC flash

Deduplication

Compression

2012

Economic Innovation

Flash is

the New Tier-1

Non-disruptive operations

Snapshots

Replication

Data Encryption

All-inclusive software

Deep ecosystem integration

2014

Mass Adoption

The All-Flash

Data Center

Tier 2 economics

New metrics:

W/GB

GB/RU

PB/Admin

2016

Future Innovation

Introducing

Tier-0

<1ms latency

100Ks of IOPS

Consistent performance

2010

Performance


Helping 1,100+ Customers Worldwide Accelerate

21

INTERNET AND MEDIA HEALTHCARE

CONSUMER

AND RETAIL

TELECOMMUNICATIONS

AND SERVICE PROVIDERS

SOFTWARE / SAAS TECHNOLOGY

FINANCE AND

INSURANCE

GOVERNMENT

AND EDUCATION

ENERGY


For more information, please visit purestorage.com

Thank You

Technology

Presentazione PureStorage @ VMUGIT UserCon 2015