HPE 3PAR StoreServ3.3.1. UpdateErmo Seikku [email protected] 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
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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
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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!