This presentation provides an overview of DataCore's Software-defined Storage Platform and insights into DataCore's latest world-record setting performance achievements on the SPC-1 benchmark. DataCore Parallel I/O, which is at the heart of DataCore's technology, is a unique approach to increasing storage performance by orders of magnitude without the need to acquire more and more hardware.

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Copyright © 2016 DataCore Software Corp. – All Rights Reserved.
Technical Overview
Jeff Slapp
Director, Systems Engineering
Products:
• DataCore SANsymphony™-V
• DataCore Virtual SAN

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HIGH-LEVEL OVERVIEW

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 Widely deployed: Over 10,000 customers & Over 30,000 deployments
 Mature: 10th Generation & 18 years of development
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Any physical host
DataCore + x86 = Enterprise Storage Controller
Any connection
Any hypervisor
Any application
Any storage

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Storage Controller Architectures Compared
Expansion Slots*
CPUs** and Memory***
Controller 1
Expansion Slots
CPUs and Memory
Controller 1
Controller 2
Controller 2
Self-
Contained
Shared
Storage
Controllers
Expansion Slots*
CPUs** and Memory***
Expansion Slots
CPUs and Memory
Tightly-
Coupled
Operating
System
(Software)
Loosely-
Coupled
Operating
System
(Software)Controller Separation (> 100KM)
Discrete Non-
Shared
Storage
Controllers
No Controller Separation
Typical Storage Controllers
DataCore Storage Controllers
Storage Services Only (FC,
iSCSI, CIFS)
Storage (FC, iSCSI, CIFS) and
Application Services (Object, DB)
* Expansion slots only support specific devices
** CPU type is locked in and on vendor timetable
*** Memory is at a premium cost
Disk Is Single Point of Failure
No Single Point of Failure

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Traditional Converged Hyperconverged
Integrate, manage, and
enhance existing
storage
Leverage internal
storage, reduce
complexity and maintain
compute segregation
Consolidate all
functions for smallest
footprint and highest
performance
Same software, with an integrated management console across all three!
Deployment Model Independent
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Co-Existence of All Deployment Models
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BREAKING WITH TRADITION

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Attacking The I/O Problem
• The traditional approach of dealing with the I/O problem is to push the I/O down to the disk.
• This means the only way to deal with increasing I/O demand is to add more disks and/or more
expensive disks (i.e. flash) to the architecture (Hardware Parallelization).
• The result of this is increased cost, size, and complexity, while not significantly impacting
response times.
• A better approach is handling the I/O as soon as it arrives at the system (I/O Parallelization).
Cost(Engines,Disks,Real
Estate,Environmentals,etc.)
Application I/O Demand
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Understanding DataCore Parallel I/O
9
Parallel Application I/O
(Databases, Hypervisors)
Storage Admin: Performance is terrible, we
need to add more disks.
Serial Storage I/O
(Typical Storage)
Parallel Application I/O
(Databases, Hypervisors)
I/O Parallelization
Approach
Storage Admin: Performance is
unbelievable, takes up little space, and is
very affordable.
Parallel Application I/O
(Databases, Hypervisors)
Hardware Parallelization
Approach
Storage Admin: Latency is still very high,
takes up a lot of space, and this is getting
expensive.

10. Where Would You Rather Deal With Your I/O?
Latest Intel E5v4 Processors
194 GHz Parallel I/O Processing Power
across 88 Logical Processors with DDR4 RAM
Latest Intel E7v3 Processors
360 GHz Parallel I/O Processing Power
across 144 Logical Processors with DDR4 RAM
Closest To The Application With The Fastest Components?
LatencySeenByApplicationsandUsers

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Results of Parallel I/O: Performance and Cost

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Results of Parallel I/O : Real Estate
Hitachi VSP G1000 DataCore Parallel Server

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Results of Parallel I/O : Latency
0.22
0.35
0.58
0.64
0.72
0.96
0.07141 0.06595 0.07612 0.08431 0.08749 0.09995
0
0.2
0.4
0.6
0.8
1
1.2
10% Load 50% Load 80% Load 90% Load 95% Load 100% Load
AverageResponseTime(ms)
SPC-1 Workload Generator
Ramp Phase Response Time / Throughput Curve
Hitachi VSP G1000 DataCore/Lenovo (SSD/HDD)

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THE POSSIBILITIES

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Enterprise Hybrid Services with Hyper-V

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Enterprise Hybrid Services with VMware

17. THE BIG DATA EQUATION
HAS BEEN SOLVED
Physical Capacity
368 TB
Processing Capacity
194 GHz
=
+
Lenovo® x3650 M5 with Intel® Xeon® E5v4 Processors
Storage Performance
>1.5 Million IOps
Physical Capacity
7.73 PB
>31.5 Million
Combined
SPC-1 IOps
Native FC and iSCSI
Block Services
1,848 Logical
Processors
31.5 TBs of RAM
and High-Speed
Cache
Unified Compute
AND Storage
HDFS, Ceph, Lustre,
GlusterFS, xDFS,
NFS, CIFS
Big Data
Application
Agnostic
StorageCapabilitiesComputeCapabilitiesPlatformCapabilities
4,074 GHz of
Compute and
Storage I/O Power
1Rack(42U)

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©2015 DataCore Software Corporation All rights reserved. DataCore, the
DataCore logo and SANsymphony are trademarks or registered trademarks of
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mentioned herein may be trademarks of their respective owners.
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