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Storage Decisions 2012 | © TechTarget

Randy Kerns

Senior Strategist

Evaluator Group

21st Century Storage –

What’s New and

What’s Changing

Overview

● New technologies in storage

- Continued evolution

- Each has great economic value

- Differing adoption rates

- Some require operational changes

● Vendors offering solutions

- Differences vary

- Some vendors are slow to deliver

May have a intermediate solution first


Technologies Covered Here

● Solid-state technology

● Scale-out storage systems

● Storage virtualization disk virtualization

- Includes storage pooling, thin provisioning, device

protection

● Storage systems with storage as an application

● Object-based storage


Evaluator Group view as the most important …currently:


Overview

Solid-State Technology


● Multiple implementations

- DRAM-based: uses battery to

retain state

- NAND – FLASH

SLC: single level cell: 1 or 0,

100,000’s of writes

MLC: multiple level cell: 3 or 4

states, 10,000’s of writes

Wear-leveling to balance usage

across cells

ECC used for correction of

single and double bit errors

● PCM: phase change

memory – future



● Usages

- SSD: Solid-state device,

Solid-state disk or drive

- Flash memory cards for

servers

- Tiering in storage systems

- Caching in storage systems

- All solid state storage systems

- Consumer


Solid-State Device Usage

● Justifications for SSDs

- Rugged – no moving parts

- Performance – no rotational latency or actuator movement

as with HDDs

- Reliability – MTBF: typically 6x over HDD

- Operational costs – power requirement is typically 1/4th of

HDD


Solid-State Devices in Storage

● General forms

- As HDD replacement in existing storage system architecture

HDD form factor

Connected with storage interface – Fibre Channel, SAS, SATA

For server, still requires HBA

- Memory extension – add-in card to the server or system on

PCIe Bus (called Flash card)

- PCIe-SSD (or SSS) – solid state device that connects to PCIe

bus via extender adapter

● Trend – data reduction in SSDs

- Compression & deduplication to multiply capacity

- New techniques will drive towards price parity with HDDs


Solid-State Devices in Storage

● All SSD systems

- Storage platforms that only use SSDs

- Can be designed with only SSD

Optimize for performance and data placement – equal wear out

Consideration different with compression and deduplication vs.

storage systems with spinning disks

- Some traditional disk system vendors will sell systems with

only SSDs installed

Still have design considerations for spinning disks

- Many new vendors

A great deal to learn about storage, reliability, availability,

support, etc.

Each try to feature something “new”


All SSD System Vendors

● PureStorage

● Nimbus

● Nimble Data

● Whiptail

● Nexgen

● Solid Fire

● Texas Memory Systems

● Violin

● Kaminario

● Virident

● CacheIQ


Solid-State Devices

High-Performance

Disks

High-Capacity

Disks Data

Mo

vem

en

t

● In-the-box tiering

Major performance

boost

Limit economic

impact of costs of

higher performance

drives

Utilizes resources

better

Storage System Tiering “Within-the-Box


Tiering vs. Caching

● Storage tiers

- Implies a particular price and performance metric

- Provides actual capacity

- All content resides on media

- Performance is limited only to media speed

- May be improved with caching

- Major gain: storage consolidation

● Caching

- Not considered actual storage

- All capacity must be backed by non-volatile media

- Performance limited to size of cache

- Limited use for random workloads

Actual

location

for data

Data is

transient



File and Block

Scale-Out Storage

Scale-Out Storage: Benefits

● Historically, storage systems scaled up by adding more

storage devices

- Created imbalance of access density IOPS/GB

- Economic choice created administrative challenge

● Scale-out technologies added more performance along

with capacity

- More control function - controllers


Scale-Out vs. Scale Up


Scale-Out Storage: File Storage

● Scale-out NAS

- Implemented using distributed file system usually

Clustered hardware

Global namespace across nodes

- Key implementation points

Linkage between nodes – InfiniBand, Ethernet:10Gb

I/O balancing between nodes

Capacity balancing between nodes

Coherency – across distance

Switching requirements


Scale-Out NAS Vendors

● Dell FS7600 / FS8600

● EMC Isilon

● HP StoreAll X9000

● HDS HNAS

● IBM SONAS

● NetApp FAS 8.1.1

● Xyratex ClusterStor

● And others


Scale-Out Storage: Block Storage

● Two approaches used

- Multiple controller cards to common backend storage

device pools

Backplane connected typically

Normally associated with high-end enterprise systems

- Federation of separate controller nodes (included with NAS

systems in some cases as integrated unified storage)

Complexities in cache coherency and I/O routing

Vendor differences


Scale-Out Block Storage Vendors

● Data Direct Networks

● Dell EqualLogic

● HP Lefthand P4000

● IBM XIV

● And others



Disk Virtualization and Thin Provisioning

Storage Virtualization

Storage Virtualization: Disk Virtualization

● Recent storage system architecture

- Use storage pooling concept – carve up device resources

into smaller granular segments (typically called chunks)

- Allows new or more efficient operation

Finer grained allocation of capacity

Enables thin provisioning

Allows new, selectable data protection

Leads to great capacity proficiency

- Newly designed storage systems

- Added to older system designs


Thin Provisioning

● Change in storage system design to allocation space only

on write

● Historically committed entire volume (LUN) to operating

system for use

- All capacity allocated

- Operating system or application used space as needed and

managed the space

- Led to inefficiencies – not all space for volumes was utilized


Unused

Capacity

Used

Capacity

Volume/LUN

Traditional Allocation

Trapped

and

unavailable

for other

uses

Fully

Allocated

Typical Allocation


Disk Virtualization

● When RAID added to storage, storage architecture was

changed to use data from stripes across drives in RAID

group

- Parity within the RAID group

- Distribution of data – wide striping – was based on number

of disks in RAID group

● Still have capacity efficiency issue

- Volumes with trapped capacity


0 1 2 3 P

LUN 1 LUN 2 LUN 3 LUN 4 . . . .

RAID

Stripe

LUN Assigned From

Stripes in RAID Group

Physical Disk

Drives

RAID Group Example: RAID 5

4+1 (Data and Parity)

RAID Group Volumes


Available

Used

Capacity

Used

Capacity

Available

Used

Capacity Used

Capacity

Available

LUN 2 LUN 1 LUN 3 LUN 4

Used

Available

Thinly Provisioned Volumes

Thin Provisioning Trapped Capacity


Disk Virtualization

● Storage system architecture to only assign capacity as it

was written – some systems have had for some time but

not fully utilized

● Creates storage pools

- Set of chunks of data – size of chunk could be specified at

time of configuration

- Volumes were created without assigning real capacity

- As data written, chunks were assigned

● For modern architecture, chunks were distributed across

physical disks to match data protection choices

- Different RAID levels

- Advanced data protection techniques


. . . .

Storage Pool

. . . . Physical

Disks

Chunks

Chunks added as required


Storage Pooling

Thin Provisioned LUN

. . . .

Storage Pool

. . . . Physical

Disks

Chunks

mapped to

specific

physical

devices

Protection

of Chunks

Chunks

Chunks added as required

Thin Provisioned LUN


Storage Pooling with Data Protection Across Pool

Thin Provisioning

● When space in volume is no longer required, data is

deleted

- For example: space can be returned to storage pool –

called space reclamation

● Different approaches for space reclamation

- Storage system scans for zero blocks and returns those

- APIs with applications and file systems to send UNMAP

SCSI command to storage system to release space

- Administrator copies volume that has grown “fat” to another

thin volume to return to thin state


Forward Error Correction

● The RAID problem

- Large capacity disk drives take a long time to rebuild

● Potentially days

● Probability of a second error occurring is high – loss of data


● The RAID problem - continued

- Multiple parity disks being utilized

● Complexity – management and algorithms

● How many should be used as capacity gets larger?

- Information is being distributed to multiple locations

● Does RAID work with multi-site locations?

● How much protection is required?


● New approach for data protection

- For failed drives

- For locations that don’t respond when geographically dispersed

● Information Dispersal Algorithms or IDA

- Study area in computer science

- Early implementations from different sources – no large scale

adoption

- More correct term is Forward Error Correction

- Also Erasure Codes is used based on the details of

implementation


● Selectivity on the amount of data protection

- Example: 12 of 16 drives must be present – tolerate loss of 4

- Example: 12 of 16 sites must respond with date – tolerate 4 sites not responding

- Important characteristic is the ability to set the protection level

● Potential benefit

- Disk system with protection set calculated for lifespan of storage system

- Failures tolerated over time – no replacements expected (with warning and exception conditions)

- No service planned ● Reduces warranty cost for vendor

● Reduces service interruption / impact for IT


● Current products with Forward Error Correction

implementations

- EMC Isilon

- Amplidata

- Cleversafe

- EMC Atmos

- Scality

- NEC HydraStor

- DataDirect Networks Web Object Scaler

● Expect others – this will become a competitive area


Storage as an Application

Storage Systems

Hypervisor within the Storage System

● Most storage systems hardware uses standard processor

technology

● New: Use of hypervisor in storage system

- Allows for multiple personalities

● Changes dynamic of storage system

- Run embedded storage system software as an application

Block storage control software

NAS software – file system – clustered, distributed, single

- Can run additional features as applications – example is

replication software

- Can run application software on storage system


Advanced Storage System


Hypervisor

Device

Blo

ck

Sto

rag

e

File S

tora

ge

Rep

lic

ati

on

Ap

pli

ca

tio

n

Ap

pli

cati

on


Object-based Storage


● Renewed interest in object-based storage

● First very successful product was Centera

- Used content address as object ID

● New solutions developing

- Most use REST and HTTP for communication

- Generally best described as files with additional metadata

is the object



● Storage systems designed that can store and retrieve

objects

● Reasons:

- Ability to manage more objects than standard file systems

- Additional information – metadata – aids in characterization

of information

Controls such as protection requirements, retention periods,

other useful information

● Standards exist for object-based storage

- Not necessarily completely followed

● Applications must change to use object-based storage


Object Storage System


Application Network Object

Storage

System

HTTP /

REST

Objects

Metadata

Typically

10GbE

File

Attributes

File

Storage Data &

Attributes

Object

Storage

(local or

Cloud)

CIFS, NFS HTTP

REST/CD

MI

Adds Metadata

Administrator Defined

Object Storage Vendors

● Amplidata

● Data Direct Networks Web Object Scaler

● Dell DX

● EMC Centera

● HDS Hitachi Content Platform

● Others – including cloud locations such as Amazon S3


Summary

● Storage systems are changing

- New capabilities

● Storage as an application – running in a virtual machine

● Virtual machines within storage systems

- New architectures

● Storage pooling in disk virtualization

● Thin provisioning

● Technology will cause more changes

- Solid state technology

- Tiering & caching

- Object based storage

● More options, better opportunities and choices



Randy Kerns: [email protected]

Twitter: @rgkerns

Blog: http://itknowledgeexchange.techtarget.com/storage-soup/

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