Redesigning your Storage Architecture: NAND Flash Deep Dive – Vaughn Stewart, VP, Enterprise Architect, Pure Storage

1. 1| © 2015 Pure Storage Inc.
NAND FLASH DEEP DIVE
Vaughn Stewart
VP, Enterprise Architect, Pure Storage
v@purestorage.com | @vStewed | vaughnstewart.com
REDESIGNING STORAGE INFRASTRUCTURE

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~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

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Flash is changing what we ‘know’ about storage

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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

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Maximum performance (IOPs) is easy to market
Performance is a sizing exercise… which increases in cooreleation with hardware purchased

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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

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A large impact on application performance is processing time
Reducing latency increases CPU utilization
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%
Latency is the under recognized benefit of all-flash storage
~100 µs ~100 µs
~600 µs
Time
CPU
I/O
CPU
Request I/O
CPU
Process I/O
Storage
Service I/O

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A large impact on application performance is processing time
Reducing latency increases CPU utilization
Conclusion: all-flash storage optimizes CPU allowing for more IOPs per host (accelerate
business) or the reduction in the # of hosts (reduce capex)
Latency is the under recognized benefit of all-flash storage
Avg. Disk Latency 5ms 10ms 15ms 20ms 30ms
CPU Utilization Benefit 6.6X 12.8X 19.1X 25.3X 37.9X

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Re IOPs: All-flash can eliminate storage support calls
Tier 1 Legacy Disk Pure Storage FlashArray
26ms
.3-
.7ms

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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. 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

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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)

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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

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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

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Effective $/GB is the modern cost of storage

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The Objective

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Absurd Testing

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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

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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

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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

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