HPE 3PAR StoreServ3.3.1. UpdateErmo Seikku ermo@hpe.comSenior Presales Consultant, Storage AmbassadorHPE FINLAND and BALTICS

ESE170228a

HPE 3PAR OS 3.3.1 The biggest software release for 3PAR yet

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Express Writes for iSCSI and 16Gb/s FCRead and Write performance optimizationsFaster VM clonesSmoother I/O transition during code upgradesAdaptive Flash Cache enhancements

Performance

Adaptive Data ReductionInline compressionEnhanced deduplication engineData Packing

Data Reduction

Peer Persistence with 3DCAsync Streaming RCIP and higher latencyCLX 4.0 Persistent Checksum for Linux base OSAdaptive Sparing 2.0

Resilience

SSMC 3.1 & SP 5.0Improved capacity and data reduction reportingSmart SAN 2.0Bigger virtual volume sizesVVols support for iSCSI and replication

Ease of use

Separate session

HPE 3PAR OS 3.3.1CompressionDedup EnhancementsSSMC 3.1SP 5.0Support for larger raw capacityLarger Volume SizesExpress Writes for iSCSI and 16Gb/s FCPersistent Checksum with standard T10 DIFAdaptive Sparing 2.0Express Layout for all DrivesSelf Identifying Drives3DC Peer Persistence On Node CLX Async Streaming longer distance and RCIP supportRemote Copy Scalability and Performance improvements Adaptive Flash Cache Enhancements File Persona 1.3Write Cache for node down in a 2 node systemCombo Carts DC PCM Support Replication for VMware VVolsVMware VVols over iSCSI2 Factor Authentication Security Updates Improved serviceabilitySmartSAN 2.0

(Recent) HP/HPE 3PAR OS Evolution

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HP 3PAR OS 3.1.3Max limits increasePerformance OptimizationsPriority Optimization: Latency GoalMxN ReplicationAdaptive Sparing480GB/920GB SSD w/ 5 Year Warranty1.2TiB 10K and 4TiB 7.2K HDDsUpgrade Automation (SW/Drives)Peer Motion: load balancing and clustersResiliency improvementsSR-on-Node Performance AlertsOnline-Import for EMC

HP 3PAR OS 3.2.17000 Converged modelsHP 3PAR StoreServ 7440Adaptive Flash CacheExpress WritesFIPS 140-2 EKMAO on VVsetsPeer Persistence for MSFTVMware VVOLSTunesys fixesResiliency improvementsThin DeduplicationFile Persona1,92/3.84TiB cMLC SSDs

HP 3PAR OS 3.2.2HP 3PAR StoreServ 8000/20000 SystemsStoreServ Management Console 2.2Support for Higher ScalabilityPersistent Checksum Remote Copy Asynchronous Streaming Peer Persistence for RHELHP StoreOnce Recovery Manager Central 1.1Storage Federation (4x4 multi-directional)Online Import for HDSPriority Optimization: <1ms latency goalAdaptive Flash Cache enhancements iSCSI VLAN taggingVMware VVOLs higher scalabilityAutonomic Rebalance enhancements On-node System Reporter changesAdaptive Optimization new optionsLDAP improvements SmartSAN supportExpress Layout

2014 2015 2017

Performance and scalability

Performance and ScalabilityiSCSI Performance improvements

– Express Writes – Can significantly increase Write performance for small IOs– Allows initiator to send Data along with the Command

–Eliminates 1x RTT per IO

– TCP Window Scaling - Improves Large Write IO performance– Enabled TCP Windows Scaling from 64k to 256k in the 3PAR iSCSI driver

–Google “Bandwidth Delay Product” for details why

– Optimized iSER – https://en.wikipedia.org/wiki/ISCSI_Extensions_for_RDMA– Removed unnecessary register reads (each can take 1000s of CPU cycles)

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0

1

2

3

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5

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0 20000 40000 60000 80000 100000 120000 140000 160000

iSCSI Express Writes latency improvement

RW-8k-ON RW-8k-OFF-k

HPE 3PAR iSCSI Express Writes Latency improvements (100 % read 8k block size)

92 000 IOPS@2msExpress Writes OFF

139 000 IOPS@1,4msExpress Writes ON

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Raw Capacity and Number of Drives (Up to) Twice the amount of Raw Capacity than 3.2.2

Array 8200 8400 8440 8450 20450 20800 20850 20840

Max Drives, SSD 120 240 480 480 576 1152 1152 1152

Max Drives, All 240 576 960 480 576 2304 1152 2304

Max Raw SSD TiB

850 (450)

1700 (900)

3350 (1850)

3350 (1850)

4000 (2000)

8000 (4000)

8000 (4000)

8000 (4000)

Max Raw – All TiB 1000 2400 4000 3350 4000 9600 8000 9600

ResiliencePeer Persistence with 3DC

Extended data protection through 3PAR Remote CopySituation before 3PAR OS 331

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Host cluster

HPE 3PAR

Host cluster

HPE 3PAR

Synchronous

Dual site active/passive (sync)• Dual sites offers increased data protection• Services generally run on a single site

Host cluster

HPE 3PAR

Host cluster

HPE 3PAR

Synchronous

Host cluster

HPE 3PAR

Asynchronous

Asynchronous

Synchronous Long Distance• Third site reduces impact on services• Services still generally run on one site• Data still protected against full site failure

Stretch (Metro) cluster

HPE 3PAR HPE 3PAR

Synchronous

Peer Persistence• Stretched cluster between datacenter 1 and 2

• Offers full Peer Persistence functionality• Planned maintenance non-disruptive• Unplanned maintenance minimally disruptive

100% native, no appliancesManaged through SSMC

Extended data protection through 3PAR Remote CopyWith 3PAR OS 331 we are introducing Peer Persistence 3 Datacenter

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Asynchronous

Host cluster

HPE 3PAR

Host cluster

HPE 3PAR

Synchronous

Host cluster

HPE 3PAR

Asynchronous

Asynchronous

Peer Persistence 3 Datacenter• Stretched cluster between datacenter 1 and 2• Offers full Peer Persistence functionality• Planned maintenance non-disruptive• Unplanned maintenance minimally disruptive

100% native, no appliancesManaged through SSMC

Host accessSites A and B form a single, stretched clusterSite C is connected to a separate fabric

FailoverTransparent failover between A <-> BAutomatic failover between A <-> B

Site naming conventionPrimarySecondaryTertiary

3DC Peer Persistence

New HPE 3PAR 8000 Host Bus AdaptersCombo cards to add more flexibility in File Persona and Replicated deployments

4-port FC combo card adapter2 x 10GbE + 2 x 16Gb FC ports

4-port 10GbE combo card adapter2 x 10GbE + 2 x 10GbE iSCSI ports*

More Flexibility

• Possible to configure File Persona and iSCSI on a 8000 2N system

• Possible to configure File Persona, RCFC, Persistent Ports on a 8000 2N system

• Possible to configure RCIP over 10Gb ports on 8000

Available on all 3PAR 8000models in 1H CY2017

* The 10GbE iSCSI ports do NOT support FCoE

Combo HBAs require HPE 3PAR OS 3.3.1

(Adaptive) Data ReductionEnhanced deduplication engine (V3)Inline compression

New: HPE 3PAR Adaptive Data Reduction

*)Please see terms and Conditions of 3PAR Get Thinner Guarantee

• Over 4:1 compaction (Thin + ADR with Get Thinner) • Available on any all flash 8K or 20K systems• Always in-line and selectively applied• Savings estimation and modeling built-into SSMC

Deduplication Prevent storing duplicate data

CompressionReduce data footprint

Data PackingPack multiple pages together

Zero DetectRemove zeros inline

Express Indexing Meta data accelerationdone in 3PAR ASIC

Express ScanIntelligent compression done in Intel CPU

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Architected for total system efficiencyto lower TCO without compromise

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Compaction is Adaptive Data Reduction and Thin Provisioning together – showing total system savings

ADR is an umbrella term that describes the technologies in HPE 3PAR designed to reduce the footprint of data

Deduplication

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How deduplication worksReducing data footprint by eliminating duplicate data writes

Deduplication looks to match entire blocks against ones that have already been written to prevent duplicate writes

Removing duplicate data can lead to significant savings as many data types

show repeated data that can be reduced

Deduplication looks for complete blocks that contain the same data

Incoming blocks Existing blocks

HPE 3PAR StoreServ deduplicationAdvanced inline, in-memory deduplication

Host writes data to the array held in cache pages to

increase write performance

Duplicates are removed, only unique data is flushed to the

SSDs reducing writes

The 3PAR ASIC, paired with Express Index lookup tables provides high performance, low-latency inline deduplication

The 3PAR ASIC checks to see if the pages are

duplicates of existing pages

Dedup lookup table

Potential duplicates are confirmed with a bit-for-bit check

HPE 3PAR deduplicationImplementation in HPE 3PAR OS 3.2.x (TDVV1/2)

Dedup-enabled TPVV

Private space (one per dedup-enabled TPVV)

Shared space (one per CPG)

New incoming writes have a hash calculated. If the hash hasn’t been seen before, the data is stored in the shared space.

If a calculated hash matches an existing one, the array uses the ASIC to detect if the new data matches the existing data. If it does, no new data is written.

If the data does not match, it is a hash collision (where the hash is the same but the data is different). This data is stored in the private space.

When data in the shared space is no longer referencedby any dedup-enabled VV, the space is freed from the shared space and is available for new data.

Small private space only contains collision data

Large shared space contains most data, including a large amount of unique data

The nature of deduplicationSpace savings through deduplication

A system is achieving a 2:1 deduplication ratio1.6TB has been written to the system

This is not what happens in the real world!

Deduplication Ratio - 2:1This would mean that, looking at the capacity of the array, 50% of the used capacity would be duplicate data

Deduplicated Data800GB

Host written: 1.6TB

Assumption – Every page of data is being

deduplicated once

Deduplication in the real worldA small amount of data is very heavily duplicated

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Unique data

Duplicated data (11:1)

720GB

80GB + 720GB = 800GB (2:1 deduplicated)

80GB

80GB x 11 = 880GB + 720GB = 1.6TB (rehydrated)

“Duplicated” data (1:1)

Customer data Filesystem reports 1.6TB written

Good for deduplication data Unique data

880GB 720GB

This “unique” data still requires metadata information for deduplication lookup table etc.

Compression-optimized deduplication approachNew compression-friendly and easy to admin implementation (TDVV3)

All writes with new hashes are written to the private space first, not shared space. All hashes are tracked.

When data is seen for the second time, the data is moved from the private space to shared space.

The majority of data by capacity will be held in private space.

Shared space is used more efficiently, meaning a smaller amount of shared space can offer increased deduplication scalability while reduced metadata improves performance.

Private space (one per dedup-enabled TPVV)

Dedup-enabled TPVV

Shared space (one per CPG)

Small shared space contains only duplicate data

Large private spaces hold majority of data

What does the new implementation mean?Advantages of the new deduplication implementation

Up to 8x better scalabilityBetter deduplication scalability with more efficient use of DDS

Improved savingsStoring only duplicate data in the DDS means more chances to deduplicate data

Simplified managementImproved scalability means fewer CPGs for more deduplicated volumes

Improved performanceIncreased IOPS, bandwidth and reduced latency for all platforms

Compression

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Data segments

CompressionData reduction by compressing data

Compression algorithms work by inspecting data in blocks and removing superfluous information

Within each block, there will be repeated data and often padding around the real data

Compression removes the repeated data and padding space to reduce the capacity required to store the data

111110000011111

000000000000000

000000000000000

000000000000000

000000000000000

000000000000000

111111111111111

100010111010001

100010111010001

100001000010000

100001000010000

100010111010001

111111111111111

111110000011111

HPE 3PAR StoreServ Data Reduction TechnologiesWorking together for optimal results

When used together, duplicate pages are removed first and unique pages are then compressed

Express Index tables

Deduplication3PAR ASIC

CompressionIntel CPU

Data in cache

Resulting data written to SSD

Express Scan

Unique data Data Packing

Advanced inline, in-memory compression with deduplication

Expected compression results

Structured data (databases) Unstructured data (file data)

50%Average savings

35%Average savings

2:1 1.5:1

Compressed data presents a unique problemAfter compression, blocks are odd sizes

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16KB

16KB

16KB

16KB

16KB

16KB

2.3KB

8.3KB

5.2KB

3.2KB

10.7KB

1.1KB

Total: 96KB Total: 30.8KBSaving: 65.2KB

3.12:1

CompressedUncompressed

?

The odd sizes of compressed pages make them difficult to store

Append-only data structuresUsed by many all-flash arrays

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As data is written to the system, it is compressed and then combined into a single stripe

The complete stripe, with any metadata required, is written to SSD sequentially

When hosts overwrite data, the old blocks are invalidated and new data is written to new stripes

At some point, a post-process task must take the existing data and write it to a new stripe with data from other partial stripes

Extremely inefficient use of space as data is overwrittenRequires the array to ‘hide’ space for housekeeping (overprovisioning)

Requires backend I/O intensive housekeeping to keep up with massive amount of garbage

Stripe

Storing compressed data in variable block sizesSome systems use variable block sizes (base 2 – 4K, 8K, 16K)

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16KB

16KB

16KB

16KB

16KB

16KB

2.3KB

8.3KB

5.2KB

3.2KB

10.7KB

1.1KB

4KB

4KB

4KB

16KB

8KB

16KB

Total: 96KB Total: 30.8KBSaving: 65.2KB

3.12:1

Total: 52KBSaving: 44KB

1.85:1

CompressedUncompressed Backend

Padding per-backend page means lots of wasted space, resulting in lower total system efficiency

Btw. The array would likely still report compression as 3.12:1

HPE 3PAR Data PackingPacking multiple pages to increase data reduction efficiency

Deduplicated, compressed pages in cache ready for

writing vary in size

Varying pages are packed into a single 16KiB improving performance and efficiency

Complete 16KiB pages are written to SSD. Express

Indexing used for lookups

Data Packing improves metadata efficiency and creates flash-friendly read and write sizes

HPE 3PAR Data PackingMaximize total system efficiency and performance

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16KB

16KB

16KB

16KB

16KB

2.3KB

1.1KB

5.2KB

3.2KB

2.9KB

CompressedUncompressed Data Packing(RAM)

Data Storing(SSD)

Data localityPack volume data together

to increase likelihood of changing blocks together

Update in-placeInstead of appending data or only writing new data,

update existing data

No compromisePacking pages together for no wasted space, even over time

Garbage-freeVirtually no garbage is created so GC is fast and infrequent

SCM-readyArchitected to optimize benefits of Storage Class Memory

16KB

Selected from the same VV

Selective Adaptive Data ReductionAllowing more efficient use of system resources

Different data types have different requirementsFor each data type, enable the technologies that provide benefits and disable the technologies that don’t

Oracle databaseThin Provisioned

Compressed(2:1)

Exchange serverThin Provisioned

DeduplicatedCompressed(3:1 - 1.5:1)

Compressed videoThin Provisioned

VDI environmentThin Provisioned

DeduplicatedCompressed(10:1 - 2:1)

Compression and encryptionApplication-level encryption destroys opportunities to compress (and deduplicate) data

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Oracle with database-level encryption

Encrypted data is sent to 3PAR array

Encryption scrambles data meaning it’s not possible to compress

Compression ratio will be 1:1, the amount of flash stored will be the same as with thin provisioning

Compression and encryptionUse HPE 3PAR Self-Encrypting drives to offer compression savings

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Oracle without encryption

Data is sent to 3PAR array

Databases often compress well, offering 2:1 savings

Compressed data is written down to SEDs, which encrypt the already compressed data, offering savings and encryption

Thank you!