DataCore Technology Overview

Copyright © 2016 DataCore Software Corp. – All Rights Reserved.


Technical OverviewJeff SlappDirector, Systems EngineeringProducts:• DataCore SANsymphony™-V• DataCore Virtual SAN


HIGH-LEVEL OVERVIEW

Copyright © 2016 DataCore Software Corp. – All Rights Reserved. 3

Widely deployed: Over 10,000 customers & Over 30,000 deployments Mature: 10th Generation & 18 years of development

Any physical host

DataCore + x86 = Enterprise Storage Controller

Block and File Svcs FC, iSCSI, NFS, SMB

Any connection

Any hypervisor

Any application

Any storage


Storage Controller Architectures Compared

Expansion Slots*

CPUs** and Memory***

Controller 1Expansion 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


Traditional Converged HyperconvergedIntegrate, 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

5


Hosts

Virtual Disks

Virtual Storage Pools

ManagementConsole

Hyper-converged Storage Model

Co-Existence of All Deployment Models

Converged Storage Model

Traditional SAN Controller Model

6


BREAKING WITH TRADITION


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).

Cos

t (E

ngin

es, D

isks

, Rea

l E

stat

e, E

nviro

nmen

tals

, etc

.)

Application I/O Demand

Hardw

are P

arall

eliza

tion

I/O Parallelization

8


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)


I/O ParallelizationApproach

Storage Admin: Performance is unbelievable, takes up little space, and is

very affordable.


Hardware ParallelizationApproach

Storage Admin: Latency is still very high, takes up a lot of space, and this is getting

expensive.

Where Would You Rather Deal With Your I/O?

Latest Intel E5v4 Processors194 GHz Parallel I/O Processing Power

across 88 Logical Processors with DDR4 RAM

Latest Intel E7v3 Processors360 GHz Parallel I/O Processing Power

across 144 Logical Processors with DDR4 RAM

Or All The Way Down Here, Where It’s Too Late?

Closest To The Application With The Fastest Components?

Late

ncy

Seen

By

App

licat

ions

and

Use

rs


Results of Parallel I/O: Performance and Cost


Results of Parallel I/O : Real Estate

Hitachi VSP G1000 DataCore Parallel Server


Results of Parallel I/O : Latency

10% Load 50% Load 80% Load 90% Load 95% Load 100% Load0

0.2

0.4

0.6

0.8

1

1.2

0.22

0.35

0.580.64

0.72

0.96

0.07141 0.06595 0.07612 0.08431 0.08749 0.09995

Ramp Phase Response Time / Throughput CurveHitachi VSP G1000 DataCore/Lenovo (SSD/HDD)

SPC-1 Workload Generator

Ave

rage

Res

pons

e Ti

me

(ms)


THE POSSIBILITIES


Enterprise Hybrid Services with Hyper-V


Enterprise Hybrid Services with VMware

* Expected Capacity Availability in Q2’16

THE BIG DATA EQUATION HAS BEEN SOLVED

Physical Capacity368 TB

Processing Capacity194 GHz

=

+

Lenovo® x3650 M5 with Intel® Xeon® E5v4 Processors

Storage Performance>1.5 Million IOps

Physical Capacity7.73 PB

>31.5 Million CombinedSPC-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

Storage Capabilities

Com

pute Capabilities

Platform C

apabilities

4,074 GHz of Compute and

Storage I/O Power

1 Rack (42U

)


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